Initial commit: ImageForensics-AI statistical image screening system

.env.example

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+# =========================================
+# AI Image Screener - Environment Configuration
+# Copy this file to .env and adjust values
+# =========================================
+
+# Application
+APP_NAME="AI Image Screener"
+VERSION="1.0.0"
+DEBUG=False
+LOG_LEVEL="INFO"
+
+# Server Configuration
+HOST="0.0.0.0"
+PORT=7860
+WORKERS=1
+
+# File Processing
+MAX_FILE_SIZE_MB=10
+MAX_BATCH_SIZE=50
+ALLOWED_EXTENSIONS=".jpg,.jpeg,.png,.webp"
+
+# Detection Thresholds
+REVIEW_THRESHOLD=0.65
+
+# Metric Weights (must sum to 1.0)
+GRADIENT_WEIGHT=0.30
+FREQUENCY_WEIGHT=0.25
+NOISE_WEIGHT=0.20
+TEXTURE_WEIGHT=0.15
+COLOR_WEIGHT=0.10
+
+# Processing Configuration
+ENABLE_CACHING=True
+PROCESSING_TIMEOUT=30
+PARALLEL_PROCESSING=False
+MAX_WORKERS=1
+
+# Paths (relative to project root)
+BASE_DIR="."
+UPLOAD_DIR="data/uploads"
+REPORTS_DIR="data/reports"
+CACHE_DIR="data/cache"
+LOGS_DIR="logs"
+
+# =========================================
+# Hugging Face Spaces Specific
+# =========================================
+# These are automatically set by HF Spaces
+# HF_SPACE_ID=""
+# HF_SPACE_HOST=""
+
+# =========================================
+# Production Recommendations
+# =========================================
+# - Set DEBUG=False
+# - Set LOG_LEVEL="WARNING" or "ERROR"
+# - Adjust WORKERS based on available CPU cores
+# - Enable PARALLEL_PROCESSING if CPU cores > 2
+# - Set appropriate MAX_FILE_SIZE_MB for your use case
+# =========================================

Dockerfile

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+# ===============================================================
+# ImageScreenAI - Dockerfile : Optimized for Hugging Face Spaces
+# ===============================================================
+
+FROM python:3.11-slim
+
+# Set working directory
+WORKDIR /app
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_DISABLE_PIP_VERSION_CHECK=1 \
+    DEBIAN_FRONTEND=noninteractive
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    libgl1 \
+    libglib2.0-0 \
+    libsm6 \
+    libxext6 \
+    libxrender1 \
+    libgomp1 \
+    libmagic1 \
+    gcc \
+    g++ \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements first (layer caching optimization)
+COPY requirements.txt .
+
+# Install Python dependencies
+RUN pip install --upgrade pip setuptools wheel && \
+    pip install -r requirements.txt
+
+# Copy application code
+COPY . .
+
+# Create necessary directories
+RUN mkdir -p data/uploads data/reports data/cache logs && \
+    chmod -R 755 data logs
+
+# Expose port (Hugging Face Spaces uses port 7860 by default)
+EXPOSE 7860
+
+# Health check
+HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
+    CMD python -c "import requests; requests.get('http://localhost:7860/health')" || exit 1
+
+# Run the application
+# Note: Hugging Face Spaces expects the app to listen on 0.0.0.0:7860
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860", "--workers", "1"]

README.md

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+# AI Image Screener
+
+[![Python Version](https://img.shields.io/badge/python-3.11%2B-blue.svg)](https://www.python.org/downloads/)
+[![FastAPI](https://img.shields.io/badge/FastAPI-0.104%2B-009688.svg)](https://fastapi.tiangolo.com/)
+[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)
+[![Code Style](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+
+> **A transparent, unsupervised first-pass screening system for identifying images requiring human review in production workflows**
+
+---
+
+## 🎯 Overview
+
+**AI Image Screener** is not a "perfect AI detector." It is a **pragmatic screening tool** designed to reduce manual review workload by flagging potentially AI-generated images based on statistical and physical anomalies.
+
+### What This Is
+✅ A workflow efficiency tool  
+✅ A transparent, explainable detector  
+✅ A model-agnostic screening system  
+✅ A first-pass filter, not a verdict engine
+
+### What This Is Not
+❌ A definitive "real vs fake" classifier  
+❌ A black-box deep learning detector  
+❌ A system claiming near-perfect accuracy on 2025 AI models
+
+---
+
+## 🚀 Key Features
+
+- **Multi-Metric Ensemble**: 5 independent statistical detectors analyzing different AI generation failure modes
+- **Binary UX**: Only two outcomes - `LIKELY_AUTHENTIC` or `REVIEW_REQUIRED` (no ambiguous "maybe")
+- **Full Explainability**: Per-metric scores, confidence levels, and human-readable explanations
+- **Batch Processing**: Parallel analysis of up to 50 images with progress tracking
+- **Multiple Export Formats**: CSV, JSON, and PDF reports for integration into existing workflows
+- **No External Dependencies**: No ML models, no cloud APIs - fully self-contained
+- **Production Ready**: FastAPI backend, comprehensive error handling, configurable thresholds
+
+---
+
+## 📊 Detection Approach
+
+### The Core Philosophy
+
+Instead of answering *"Is this image AI or real?"*, we answer:
+
+> **"Does this image require human review?"**
+
+This reframes the problem from classification to prioritization - far more valuable in real-world workflows.
+
+---
+
+## 🔬 Metrics Choice & Rationale
+
+### Why These Five Metrics?
+
+Each metric targets a **different failure mode** of AI image generation models (diffusion models, GANs, etc.):
+
+#### 1. **Gradient-Field PCA** (`metrics/gradient_field_pca.py`)
+- **Weight**: 30%
+- **Target**: Lighting inconsistencies in diffusion models
+- **Rationale**: Real photos have gradients aligned with physical light sources. Diffusion models perform patch-based denoising, creating low-dimensional gradient structures inconsistent with physics.
+- **Method**: Sobel gradients → PCA → eigenvalue ratio analysis
+- **Threshold**: Eigenvalue ratio < 0.85 indicates suspicious structure
+- **Research Basis**: [Gragnaniello et al. 2021](https://arxiv.org/abs/2104.02726) - "Perceptual Quality Assessment of Synthetic Images"
+
+#### 2. **Frequency Analysis (FFT)** (`metrics/frequency_analyzer.py`)
+- **Weight**: 25%
+- **Target**: Unnatural spectral energy distributions
+- **Rationale**: Camera optics and sensors produce characteristic frequency falloffs. AI models can create spectral peaks/gaps not found in nature.
+- **Method**: 2D FFT → radial spectrum → high-frequency ratio + roughness + power-law deviation
+- **Thresholds**: HF ratio outside [0.08, 0.35] indicates anomalies
+- **Research Basis**: [Dzanic et al. 2020](https://arxiv.org/abs/2003.08685) - "Fourier Spectrum Discrepancies in Deep Network Generated Images"
+
+#### 3. **Noise Pattern Analysis** (`metrics/noise_analyzer.py`)
+- **Weight**: 20%
+- **Target**: Missing or artificial sensor noise
+- **Rationale**: Real cameras produce Poisson shot noise + Gaussian read noise with characteristic variance. AI models often produce overly uniform images or synthetic noise.
+- **Method**: Patch-based Laplacian filtering → MAD estimation → CV + IQR analysis
+- **Thresholds**: CV < 0.15 (too uniform) or > 1.2 (too variable) flags images
+- **Research Basis**: [Kirchner & Johnson 2019](https://ieeexplore.ieee.org/document/8625351) - "SPN-CNN: Boosting Sensor Pattern Noise for Image Manipulation Detection"
+
+#### 4. **Texture Statistics** (`metrics/texture_analyzer.py`)
+- **Weight**: 15%
+- **Target**: Overly smooth or repetitive regions
+- **Rationale**: Natural scenes have organic texture variation. GANs can produce suspiciously smooth regions or repetitive patterns.
+- **Method**: Patch-based entropy, contrast, edge density → distribution analysis
+- **Thresholds**: >40% smooth patches (smoothness > 0.5) indicates anomalies
+- **Research Basis**: [Nataraj et al. 2019](https://arxiv.org/abs/1912.11035) - "Detecting GAN Generated Fake Images using Co-occurrence Matrices"
+
+#### 5. **Color Distribution** (`metrics/color_analyzer.py`)
+- **Weight**: 10%
+- **Target**: Impossible or highly unlikely color patterns
+- **Rationale**: Physical light sources create constrained color relationships. AI can generate oversaturated or unnaturally clustered hues.
+- **Method**: RGB→HSV conversion → saturation analysis + histogram roughness + hue concentration
+- **Thresholds**: Mean saturation > 0.65 or top-3 hue bins > 60% flags images
+- **Research Basis**: [Marra et al. 2019](https://arxiv.org/abs/1902.11153) - "Do GANs Leave Specific Traces?"
+
+---
+
+## ⚖️ Ensemble Approach
+
+### Weighted Aggregation Strategy
+
+```python
+final_score = (
+    0.30 × gradient_score +
+    0.25 × frequency_score +
+    0.20 × noise_score +
+    0.15 × texture_score +
+    0.10 × color_score
+)
+```
+
+### Pros ✅
+
+1. **Robustness**: No single metric failure breaks the system
+2. **Diversity**: Each metric captures orthogonal information
+3. **Tunability**: Weights can be adjusted based on use case
+4. **Explainability**: Per-metric scores preserved for transparency
+5. **Fail-Safe**: Neutral scores (0.5) for metric failures prevent cascading errors
+
+### Cons ❌
+
+1. **Hyperparameter Sensitivity**: Weights are manually tuned, not learned
+2. **Assumption of Independence**: Metrics may correlate in practice (e.g., frequency ↔ noise)
+3. **Fixed Weights**: No adaptive weighting based on image characteristics
+4. **Threshold Brittleness**: Single threshold (0.65) for binary decision may not fit all contexts
+5. **No Adversarial Robustness**: Trivial post-processing can fool statistical detectors
+
+### Why Not Machine Learning?
+
+- **Transparency**: Statistical methods are auditable; neural networks are black boxes
+- **Generalization**: ML models overfit to training generators; unsupervised methods generalize better
+- **Deployment**: No GPU required, no model versioning issues
+- **Trust**: Users understand "gradient inconsistency" better than "neuron activation patterns"
+
+---
+
+## 🏗️ Architecture
+
+### High-Level Flow
+
+```
+Image Upload → Validation → Parallel Metric Execution → Aggregation → Threshold Decision → Report Export
+```
+
+### Component Structure
+
+```
+ai_image_screener/
+├── app.py                          # FastAPI application entry point
+├── config/
+│   ├── settings.py                 # Environment variables, weights, thresholds
+│   ├── constants.py                # Enums, metric parameters, explanations
+│   └── schemas.py                  # Pydantic models for type safety
+├── metrics/
+│   ├── gradient_field_pca.py       # Gradient structure analysis
+│   ├── frequency_analyzer.py       # FFT-based spectral analysis
+│   ├── noise_analyzer.py           # Sensor noise pattern detection
+│   ├── texture_analyzer.py         # Statistical texture features
+│   ├── color_analyzer.py           # Color distribution anomalies
+│   └── aggregator.py               # Ensemble combination logic
+├── features/
+│   ├── batch_processor.py          # Parallel/sequential batch handling
+│   ├── threshold_manager.py        # Runtime threshold configuration
+│   └── detailed_result_maker.py    # Explainability extraction
+├── reporter/
+│   ├── csv_reporter.py             # CSV export for workflows
+│   ├── json_reporter.py            # JSON API responses
+│   └── pdf_reporter.py             # Professional reports
+├── utils/
+│   ├── logger.py                   # Structured logging
+│   ├── image_processor.py          # Image loading, resizing, conversion
+│   ├── validators.py               # File validation
+│   └── helpers.py                  # Utility functions
+└── ui/
+    └── index.html                  # Single-page web interface
+```
+
+**Detailed Architecture**: See [`docs/Architecture.md`](docs/Architecture.md)
+
+---
+
+## 📈 Performance Expectations
+
+### Detection Rates (Honest Estimates)
+
+| Image Source | Expected Detection Rate |
+|-------------|------------------------|
+| Consumer AI tools (2022-2023) | 80–90% |
+| Stable Diffusion 1.x / 2.x | 70–80% |
+| Midjourney v5 / v6 | 55–70% |
+| DALL·E 3 / Gemini Imagen 3 | 40–55% |
+| Post-processed AI images | 30–45% |
+| **False positives on real photos** | **~10–20%** |
+
+### Why These Rates?
+
+1. **Modern Models Are Good**: 2024-2025 generators produce physically plausible images
+2. **Post-Processing Erases Traces**: JPEG compression, filters, and resizing remove statistical artifacts
+3. **Real Photos Vary Widely**: Macro, long-exposure, and HDR photos trigger false positives
+4. **Adversarial Evasion**: Adding noise or slight edits defeats statistical detectors
+
+### Processing Performance
+
+- **Single image**: 2–4 seconds
+- **Batch (10 images)**: 15–25 seconds (parallel)
+- **Memory**: 50–150 MB per image
+- **Max concurrent workers**: 4 (configurable)
+
+---
+
+## 📦 Installation
+
+### Prerequisites
+
+- Python 3.11+
+- pip
+
+### Setup
+
+```bash
+# Clone repository
+git clone https://github.com/satyakimitra/ai-image-screener.git
+cd ai-image-screener
+
+# Create virtual environment
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Create required directories
+mkdir -p data/{uploads,reports,cache} logs
+
+# Run server
+python app.py
+```
+
+Server will start at `http://localhost:8005`
+
+---
+
+## 🚀 Quick Start
+
+### Web Interface
+
+1. Open `http://localhost:8005` in browser
+2. Upload images (single or batch)
+3. View results with per-metric breakdowns
+4. Export reports (CSV/PDF)
+
+### API Usage
+
+```bash
+# Single image analysis
+curl -X POST http://localhost:8005/analyze/image \
+  -F "file=@example.jpg"
+
+# Batch analysis
+curl -X POST http://localhost:8005/analyze/batch \
+  -F "files=@img1.jpg" \
+  -F "files=@img2.png" \
+  -F "files=@img3.webp"
+
+# Download CSV report
+curl -X GET http://localhost:8005/report/csv/{batch_id} -o report.csv
+```
+
+**Full API Documentation**: See [`docs/API.md`](docs/API.md)
+
+---
+
+## 📖 Documentation
+
+| Document | Description |
+|----------|-------------|
+| [`docs/Architecture.md`](docs/Architecture.md) | System architecture, data flow diagrams, component details |
+| [`docs/API.md`](docs/API.md) | Complete API reference with examples |
+| [`docs/CaseStudy_Analysis.md`](docs/CaseStudy_Analysis.md) | Statistical analysis, formulas, mathematical foundations |
+
+---
+
+## 🔬 Scientific References
+
+### Core Detection Techniques
+
+1. **Gragnaniello, D., Cozzolino, D., Marra, F., Poggi, G., & Verdoliva, L.** (2021). "Are GAN Generated Images Easy to Detect? A Critical Analysis of the State-of-the-Art." *IEEE International Conference on Multimedia and Expo*. [Paper](https://arxiv.org/abs/2104.02726)
+
+2. **Dzanic, T., Shah, K., & Witherden, F.** (2020). "Fourier Spectrum Discrepancies in Deep Network Generated Images." *NeurIPS 2020*. [Paper](https://arxiv.org/abs/2003.08685)
+
+3. **Kirchner, M., & Johnson, M. K.** (2019). "SPN-CNN: Boosting Sensor Pattern Noise for Image Manipulation Detection." *IEEE International Workshop on Information Forensics and Security*. [Paper](https://ieeexplore.ieee.org/document/8625351)
+
+4. **Nataraj, L., Mohammed, T. M., Manjunath, B. S., Chandrasekaran, S., Flenner, A., Bappy, J. H., & Roy-Chowdhury, A. K.** (2019). "Detecting GAN Generated Fake Images using Co-occurrence Matrices." *Electronic Imaging*. [Paper](https://arxiv.org/abs/1912.11035)
+
+5. **Marra, F., Gragnaniello, D., Cozzolino, D., & Verdoliva, L.** (2019). "Detection of GAN-Generated Fake Images over Social Networks." *IEEE Conference on Multimedia Information Processing and Retrieval*. [Paper](https://arxiv.org/abs/1902.11153)
+
+### Diffusion Model Artifacts
+
+6. **Corvi, R., Cozzolino, D., Poggi, G., Nagano, K., & Verdoliva, L.** (2023). "Intriguing Properties of Synthetic Images: from Generative Adversarial Networks to Diffusion Models." *arXiv preprint*. [Paper](https://arxiv.org/abs/2304.06408)
+
+7. **Sha, Z., Li, Z., Yu, N., & Zhang, Y.** (2023). "DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Diffusion Models." *ACM CCS 2023*. [Paper](https://arxiv.org/abs/2310.16617)
+
+### Ensemble Methods
+
+8. **Wang, S. Y., Wang, O., Zhang, R., Owens, A., & Efros, A. A.** (2020). "CNN-Generated Images Are Surprisingly Easy to Spot... for Now." *CVPR 2020*. [Paper](https://arxiv.org/abs/1912.11035)
+
+---
+
+## ⚠️ Ethical Considerations
+
+### Honest Positioning
+
+This system:
+- ✅ Never claims "real" or "fake" with certainty
+- ✅ Provides probabilistic screening only
+- ✅ Encourages human verification for all flagged images
+- ✅ Documents methodology transparently
+- ✅ Acknowledges false positive rates upfront
+
+### Appropriate Use Cases
+
+**Suitable for:**
+- Content moderation pre-screening (reduces human workload)
+- Journalism workflows (identifies images needing verification)
+- Stock photo platforms (flags for manual review)
+- Legal discovery (prioritizes suspicious documents)
+
+**Not suitable for:**
+- Law enforcement as sole evidence
+- Automated content rejection without human review
+- High-stakes decisions (e.g., criminal prosecution)
+
+### Known Limitations
+
+1. **False Positives**: 10-20% of real photos flagged (especially HDR, macro, long-exposure)
+2. **Evolving Generators**: Detection rates decline as AI models improve
+3. **Post-Processing Evasion**: Simple filters can defeat statistical detectors
+4. **No Adversarial Robustness**: Not designed to resist intentional evasion
+
+---
+
+## 🛠️ Configuration
+
+### Environment Variables
+
+Create `.env` file:
+
+```env
+# Server
+HOST=localhost
+PORT=8005
+WORKERS=4
+DEBUG=False
+
+# Detection
+REVIEW_THRESHOLD=0.65
+
+# Metric Weights (must sum to 1.0)
+GRADIENT_WEIGHT=0.30
+FREQUENCY_WEIGHT=0.25
+NOISE_WEIGHT=0.20
+TEXTURE_WEIGHT=0.15
+COLOR_WEIGHT=0.10
+
+# Processing
+MAX_FILE_SIZE_MB=10
+MAX_BATCH_SIZE=50
+PROCESSING_TIMEOUT=30
+PARALLEL_PROCESSING=True
+MAX_WORKERS=4
+```
+
+### Sensitivity Modes
+
+Adjust `REVIEW_THRESHOLD` in `config/settings.py`:
+
+- **Conservative** (0.75): Fewer false positives, may miss some AI images
+- **Balanced** (0.65): Recommended default
+- **Aggressive** (0.55): Catch more AI images, more false positives
+
+---
+
+## 🧪 Testing
+
+```bash
+# Run all tests
+pytest tests/
+
+# With coverage
+pytest --cov=. --cov-report=html tests/
+
+# Single test file
+pytest tests/test_metrics.py -v
+```
+
+---
+
+## 🤝 Contributing
+
+Contributions welcome! Please:
+
+1. Fork the repository
+2. Create a feature branch (`git checkout -b feature/amazing-feature`)
+3. Commit changes (`git commit -m 'Add amazing feature'`)
+4. Push to branch (`git push origin feature/amazing-feature`)
+5. Open a Pull Request
+
+**Code Style**: Black formatter, 100 character line limit
+
+---
+
+## 📄 License
+
+This project is licensed under the MIT License - see [LICENSE](LICENSE) file for details.
+
+---
+
+## 👤 Author
+
+**Satyaki Mitra**  
+Data Scientist | AI-ML Practitioner
+
+- LinkedIn: [linkedin.com/in/satyaki-mitra](https://linkedin.com/in/satyaki-mitra)
+- GitHub: [@satyakimitra](https://github.com/satyakimitra)
+- Email: satyaki.mitra@example.com
+
+---
+
+## 🙏 Acknowledgments
+
+- Research papers cited above for theoretical foundations
+- FastAPI team for excellent web framework
+- OpenCV and SciPy communities for image processing tools
+- Users providing feedback on detection accuracy
+
+---
+
+## 📞 Support
+
+- **Issues**: [GitHub Issues](https://github.com/satyaki-mitra/ai-image-screener/issues)
+- **Documentation**: [`docs/`](docs/)
+- **Email**: support@aiimagescreener.com
+
+---
+
+## 🔮 Roadmap
+
+- [ ] Add watermark detection module
+- [ ] Integrate reverse image search API
+- [ ] ML-based detector as optional metric
+- [ ] Persistent result storage (PostgreSQL)
+- [ ] Webhook callbacks for async processing
+- [ ] Docker containerization
+- [ ] Kubernetes deployment manifests
+
+---
+
+<p align="center">
+  <i>Built with transparency and honesty in mind.</i><br>
+  <i>Screening, not certainty. Efficiency, not perfection.</i>
+</p>

README_HUGGINGFACE.md

@@ -0,0 +1,128 @@
+---
+title: AI Image Screener
+emoji: 🔍
+colorFrom: blue
+colorTo: purple
+sdk: docker
+app_port: 7860
+pinned: false
+license: mit
+tags:
+  - ai-detection
+  - image-forensics
+  - computer-vision
+  - content-moderation
+  - screening-tool
+---
+
+# AI Image Screener 🔍
+
+**A transparent, unsupervised first-pass screening system for identifying images requiring human review**
+
+## Overview
+
+AI Image Screener is a multi-metric ensemble system that analyzes images using five independent statistical detectors to identify potential AI-generated content. Unlike black-box classifiers, this system provides full explainability with per-metric breakdowns and human-readable explanations.
+
+**Important**: This is a **screening tool, not a verdict engine**. It flags images for human review rather than making definitive "real vs fake" classifications.
+
+## How It Works
+
+The system analyzes five distinct image characteristics:
+
+1. **Gradient-Field PCA (30%)**: Detects lighting inconsistencies typical of diffusion models
+2. **Frequency Analysis (25%)**: Identifies unnatural spectral energy distributions via FFT
+3. **Noise Pattern Analysis (20%)**: Detects missing or artificial sensor noise
+4. **Texture Statistics (15%)**: Identifies overly smooth or repetitive regions
+5. **Color Distribution (10%)**: Flags unnatural saturation and color patterns
+
+Each metric produces a score (0.0-1.0), which are combined using weighted ensemble aggregation.
+
+## Expected Performance
+
+**Detection Rates (Honest Estimates):**
+- Consumer AI tools (2022-2023): 80-90%
+- Stable Diffusion 1.x/2.x: 70-80%
+- Midjourney v5/v6: 55-70%
+- DALL-E 3 / Gemini Imagen 3: 40-55%
+- Post-processed AI images: 30-45%
+
+**False Positive Rate**: ~10-20% on authentic photos (especially HDR, macro, long-exposure)
+
+## Usage
+
+### Web Interface
+
+1. Click "Use this Space" above
+2. Upload single or multiple images (max 50 per batch)
+3. View results with detailed metric breakdowns
+4. Export reports in CSV or PDF format
+
+### API Access
+
+```bash
+# Single image analysis
+curl -X POST https://huggingface.co/spaces/YOUR_USERNAME/ai-image-screener/api/analyze/image \
+  -F "file=@image.jpg"
+
+# Batch analysis
+curl -X POST https://huggingface.co/spaces/YOUR_USERNAME/ai-image-screener/api/analyze/batch \
+  -F "files=@img1.jpg" \
+  -F "files=@img2.png"
+```
+
+See full API documentation at `/docs` endpoint.
+
+## Limitations
+
+⚠️ **This system has known limitations:**
+
+- **Not adversarially robust**: Simple post-processing can defeat detection
+- **Declining effectiveness**: Detection rates decrease as AI models improve
+- **False positives**: 10-20% of real photos may be flagged (HDR, macro, heavily edited)
+- **No semantic understanding**: Cannot detect deepfakes, inpainting, or prompt-guided generation
+
+## Appropriate Use Cases
+
+✅ **Suitable for:**
+- Content moderation pre-screening (reduces human workload)
+- Journalism workflows (identifies images needing verification)
+- Stock photo platforms (flags for manual review)
+- Legal discovery (prioritizes suspicious documents)
+
+❌ **Not suitable for:**
+- Law enforcement as sole evidence
+- Automated content rejection without human review
+- High-stakes decisions (criminal prosecution, copyright disputes)
+
+## Technical Details
+
+- **Framework**: FastAPI (Python 3.11+)
+- **Processing Time**: 2-4 seconds per image
+- **Dependencies**: OpenCV, NumPy, SciPy, ReportLab
+- **No ML Models**: Purely statistical detection (no GPU required)
+
+## Credits
+
+**Author**: Satyaki Mitra (Data Scientist, AI-ML Practitioner)
+
+**Research Foundations**:
+- Gragnaniello et al. (2021) - Gradient analysis for GAN detection
+- Dzanic et al. (2020) - Fourier spectrum discrepancies
+- Kirchner & Johnson (2019) - Sensor pattern noise analysis
+- Nataraj et al. (2019) - Co-occurrence matrix detection
+- Marra et al. (2019) - GAN-specific artifacts
+
+## License
+
+MIT License - See [LICENSE](LICENSE) for details
+
+## Links
+
+- 📖 [Full Documentation](https://github.com/satyakimitra/ai-image-screener)
+- 🏗️ [Architecture Details](https://github.com/satyakimitra/ai-image-screener/blob/main/docs/Architecture.md)
+- 📊 [Case Study Analysis](https://github.com/satyakimitra/ai-image-screener/blob/main/docs/CaseStudy_Analysis.md)
+- 🔬 [API Reference](https://github.com/satyakimitra/ai-image-screener/blob/main/docs/API.md)
+
+---
+
+**Disclaimer**: Results are indicative and should be verified manually for critical applications. This system provides screening assistance, not definitive judgments.

app.py

@@ -0,0 +1,347 @@
+# Dependencies
+import uuid
+import shutil
+import signal
+import uvicorn
+import traceback
+from typing import List
+from typing import Dict
+from pathlib import Path
+from fastapi import File
+from typing import Optional
+from fastapi import Request
+from fastapi import FastAPI
+from fastapi import UploadFile
+from fastapi import HTTPException
+from utils.logger import get_logger
+from config.settings import settings
+from fastapi.responses import Response
+from config.schemas import APIResponse
+from config.schemas import AnalysisResult
+from fastapi.responses import HTMLResponse
+from fastapi.responses import JSONResponse
+from utils.validators import ImageValidator
+from fastapi.staticfiles import StaticFiles
+from utils.helpers import generate_unique_id
+from reporter.csv_reporter import CSVReporter
+from reporter.pdf_reporter import PDFReporter
+from config.schemas import BatchAnalysisResult
+from reporter.json_reporter import JSONReporter
+from utils.image_processor import ImageProcessor
+from fastapi.middleware.cors import CORSMiddleware
+from features.batch_processor import BatchProcessor
+from features.threshold_manager import ThresholdManager
+
+
+# Logging
+logger = get_logger(__name__)
+
+
+# FastAPI App Definition
+app = FastAPI(title       = "AI Image Screener",
+              version     = settings.VERSION,
+              description = "First-pass AI image screening tool for bulk workflows",
+             )
+
+
+# Serve static assets (if any later)
+app.mount("/ui", StaticFiles(directory = "ui"), name = "ui")
+
+# CORS (UI + API)
+app.add_middleware(CORSMiddleware,
+                   allow_origins     = ["*"],
+                   allow_credentials = True,
+                   allow_methods     = ["*"],
+                   allow_headers     = ["*"],
+                  )
+
+# Runtime State
+SESSION_STORE: Dict[str, Dict] = {}
+
+# Component Initialization
+image_validator   = ImageValidator()
+image_processor   = ImageProcessor()
+
+threshold_manager = ThresholdManager()
+threshold_manager = threshold_manager
+batch_processor   = BatchProcessor(threshold_manager = threshold_manager)
+
+json_reporter     = JSONReporter()
+csv_reporter      = CSVReporter()
+pdf_reporter      = PDFReporter()
+
+UPLOAD_DIR        = settings.UPLOAD_DIR
+CACHE_DIR         = settings.CACHE_DIR
+REPORTS_DIR       = settings.REPORTS_DIR
+
+for d in [UPLOAD_DIR, CACHE_DIR, REPORTS_DIR]:
+    d.mkdir(parents  = True, 
+            exist_ok = True,
+           )
+
+
+# Utility: Progress Callback
+def _progress_callback(batch_id: str):
+    def callback(image_idx: int, total: int, filename: str):
+        session = SESSION_STORE.get(batch_id)
+        if (not session or (session.get("status") != "processing")):
+            return
+
+        session["progress"] = {"current"  : image_idx,
+                               "total"    : total,
+                               "filename" : filename,
+                              }
+    return callback
+
+
+# Utility: Housekeeping
+def cleanup_temp_files():
+    try:
+        for folder in [UPLOAD_DIR, CACHE_DIR]:
+            for item in folder.iterdir():
+                if item.is_file():
+                    item.unlink(missing_ok = True)
+
+        logger.info("Temporary files cleaned")
+
+    except Exception as e:
+        logger.warning(f"Cleanup failed: {e}")
+
+
+def shutdown_handler(*_):
+    logger.warning("Shutdown signal received — cleaning up")
+    cleanup_temp_files()
+
+
+signal.signal(signal.SIGINT, shutdown_handler)
+signal.signal(signal.SIGTERM, shutdown_handler)
+
+
+# Error Handling
+@app.exception_handler(Exception)
+async def global_exception_handler(request: Request, exc: Exception):
+    logger.error(f"Unhandled error: {exc}")
+    logger.debug(traceback.format_exc())
+    
+    return JSONResponse(status_code = 500,
+                        content     = APIResponse(success = False,
+                                                  message = "Internal server error",
+                                                 ).model_dump()
+                       )
+
+
+# Home
+@app.get("/", response_class = HTMLResponse)
+def serve_frontend():
+    index_path = Path("ui/index.html")
+
+    if not index_path.exists():
+        raise HTTPException(status_code = 404,
+                            detail      = "UI not found",
+                           )
+
+    return index_path.read_text(encoding = "utf-8")
+
+
+# Health Check
+@app.get("/health")
+def health():
+    return {"status"  : "ok",
+            "version" : settings.VERSION,
+           }
+
+
+# Single Image Analysis
+@app.post("/analyze/image")
+async def analyze_single_image(file: UploadFile = File(...)):
+    image_id   = generate_unique_id()
+    image_path = UPLOAD_DIR / f"{image_id}_{file.filename}"
+
+    image_validator.validate_image(file_path = image_path,
+                                   filename  = file.filename,
+                                   file_size = file.size,
+                                  )
+
+    try:
+        with open(image_path, "wb") as f:
+            shutil.copyfileobj(file.file, f)
+
+        image                  = image_processor.load_image(image_path)
+
+        # image is a NumPy array → shape = (H, W, C) or (H, W)
+        height, width          = image.shape[:2]
+
+        result: AnalysisResult = batch_processor.process_single(image      = image_path,
+                                                                filename   = file.filename,
+                                                                image_size = (width, height),
+                                                               )
+
+        return APIResponse(success = True,
+                           message = "Image analysis completed",
+                           data    = result.model_dump(),
+                          )
+
+    finally:
+        image_path.unlink(missing_ok = True)
+
+
+# Batch Image Analysis
+@app.post("/analyze/batch")
+async def analyze_batch(files: List[UploadFile] = File(...)):
+    if not files:
+        raise HTTPException(status_code = 400, 
+                            detail      = "No files provided",
+                           )
+
+    batch_id                = str(uuid.uuid4())
+
+    SESSION_STORE[batch_id] = {"status"   : "processing",
+                               "progress" : {"current" : 0, 
+                                             "total"   : len(files),
+                                            },
+                              }
+
+    image_entries           = list()
+
+    try:
+        for file in files:
+            uid           = generate_unique_id()
+            path          = UPLOAD_DIR / f"{uid}_{file.filename}"
+
+            with open(path, "wb") as f:
+                shutil.copyfileobj(file.file, f)
+            
+            image         = image_processor.load_image(path)
+            height, width = image.shape[:2]
+
+            image_validator.validate_image(file_path = path,
+                                           filename  = file.filename,
+                                           file_size = file.size,
+                                          )
+
+            image_entries.append({"path"     : path,
+                                  "filename" : file.filename,
+                                  "size"     : (width, height),
+                                })
+
+        batch_result: BatchAnalysisResult = batch_processor.process_batch(image_files = image_entries,
+                                                                          on_progress = _progress_callback(batch_id),
+                                                                         )
+
+        SESSION_STORE[batch_id]           = {"status"   : "completed",
+                                             "progress" : SESSION_STORE[batch_id]["progress"],
+                                             "result"   : batch_result,       
+                                            }
+
+        
+        return APIResponse(success = True,
+                           message = "Batch analysis completed",
+                           data    = {"batch_id" : batch_id,
+                                      "result"   : batch_result.model_dump(),
+                                     },
+                          )
+
+    except KeyboardInterrupt:
+        SESSION_STORE[batch_id] = {"status"   : "interrupted",
+                                   "progress" : SESSION_STORE[batch_id]["progress"],
+                                  }
+
+        raise HTTPException(status_code = 499, 
+                            detail      = "Processing interrupted",
+                           )
+
+    except Exception as e:
+        logger.error(f"Batch {batch_id} failed: {e}", exc_info = True)
+        
+        SESSION_STORE[batch_id] = {"status" : "failed",
+                                   "error"  : str(e),
+                                  }
+
+        raise HTTPException(status_code = 500, 
+                            detail      = "Batch processing failed",
+                           )
+
+    finally:
+        for item in image_entries:
+            Path(item["path"]).unlink(missing_ok = True)
+
+
+# Batch Progress
+@app.get("/batch/{batch_id}/progress")
+def batch_progress(batch_id: str):
+    session = SESSION_STORE.get(batch_id)
+    
+    if not session:
+        raise HTTPException(status_code = 404,
+                            detail      = "Batch not found",
+                           )
+    
+    return session
+
+
+# Report Downloads
+@app.api_route("/report/csv/{batch_id}", methods = ["GET", "POST"])
+def export_csv(batch_id: str):
+    session = SESSION_STORE.get(batch_id)
+
+    if (not session or ("result" not in session)):
+        raise HTTPException(status_code = 404, 
+                            detail      = "Batch result not found",
+                           )
+
+    path = csv_reporter.export_batch_detailed(session["result"])
+    
+    # Read the file and send it as a download
+    with open(path, "rb") as f:
+        content = f.read()
+    
+    # Clean up the file after sending
+    path.unlink(missing_ok = True)
+    
+    return Response(content    = content,
+                    media_type = "text/csv",
+                    headers    = {"Content-Disposition" : f"attachment; filename=ai_screener_report_{batch_id}.csv",
+                                  "Content-Type"        : "text/csv"
+                                 }
+                   )
+
+
+@app.api_route("/report/pdf/{batch_id}", methods = ["GET", "POST"])
+def export_pdf(batch_id: str):
+    session = SESSION_STORE.get(batch_id)
+
+    if (not session or ("result" not in session)):
+        raise HTTPException(status_code = 404, 
+                            detail      = "Batch result not found",
+                           )
+
+    path = pdf_reporter.export_batch(session["result"])
+    
+    # Read the file and send it as a download
+    with open(path, "rb") as f:
+        content = f.read()
+    
+    # Clean up the file after sending
+    path.unlink(missing_ok = True)
+    
+    return Response(content    = content,
+                    media_type = "application/pdf",
+                    headers    = {"Content-Disposition" : f"attachment; filename=ai_screener_report_{batch_id}.pdf",
+                                  "Content-Type"        : "application/pdf"
+                                 }
+                   )
+
+
+
+# ==================== MAIN ====================
+if __name__ == "__main__":
+    # Explicit startup log (forces log file creation)
+    logger.info("Starting AI Image Screener API Server")
+
+    uvicorn.run("app:app",                    
+                host      = settings.HOST,
+                port      = settings.PORT,
+                reload    = settings.DEBUG,
+                log_level = settings.LOG_LEVEL.lower(),
+                workers   = 1 if settings.DEBUG else settings.WORKERS,
+               )

config/constants.py

@@ -0,0 +1,325 @@
+# Dependencies
+from enum import Enum
+from dataclasses import dataclass
+
+
+class DetectionStatus(str, Enum):
+    """
+    Overall detection status
+    """
+    LIKELY_AUTHENTIC = "LIKELY_AUTHENTIC"
+    REVIEW_REQUIRED  = "REVIEW_REQUIRED"
+
+
+class SignalStatus(str, Enum):
+    """
+    Individual signal status
+    """
+    PASSED  = "passed"
+    WARNING = "warning"
+    FLAGGED = "flagged"
+
+
+class FileFormat(str, Enum):
+    """
+    Supported file formats
+    """
+    JPG  = ".jpg"
+    JPEG = ".jpeg"
+    PNG  = ".png"
+    WEBP = ".webp"
+
+
+class MetricType(str, Enum):
+    """
+    Detection metric types
+    """
+    GRADIENT  = "gradient"
+    FREQUENCY = "frequency"
+    NOISE     = "noise"
+    TEXTURE   = "texture"
+    COLOR     = "color"
+
+
+
+# Signal thresholds
+SIGNAL_THRESHOLDS          = {SignalStatus.FLAGGED : 0.7,
+                              SignalStatus.WARNING : 0.4,
+                              SignalStatus.PASSED  : 0.0,
+                             }
+
+# Metric explanations
+METRIC_EXPLANATIONS        = {MetricType.GRADIENT  : {'high'     : "Detected irregular gradient patterns typical of diffusion models. Natural photos show consistent lighting gradients shaped by physics.",
+                                                      'moderate' : "Some gradient inconsistencies detected. May indicate AI generation or heavy editing.",
+                                                      'normal'   : "Gradient patterns are consistent with natural lighting and camera optics."
+                                                     },
+                              MetricType.FREQUENCY : {'high'     : "Unusual frequency distribution detected. AI-generated images often show unnatural spectral patterns.",
+                                                      'moderate' : "Frequency patterns show some irregularities. Requires further review.",
+                                                      'normal'   : "Frequency distribution matches expected patterns for authentic photographs."
+                                                     },
+                              MetricType.NOISE     : {'high'     : "Noise pattern is unnaturally uniform. Real camera sensors produce characteristic noise patterns.",
+                                                      'moderate' : "Noise distribution shows some anomalies. May indicate synthetic generation.",
+                                                      'normal'   : "Noise characteristics are consistent with genuine camera sensor behavior."
+                                                     },
+                              MetricType.TEXTURE   : {'high'     : "Detected suspiciously smooth regions. Natural photos have organic texture variation.",
+                                                      'moderate' : "Some texture regions appear overly uniform. Further analysis recommended.",
+                                                      'normal'   : "Texture variation is within expected ranges for authentic photographs."
+                                                     },
+                              MetricType.COLOR     : {'high'     : "Color distribution shows impossible or highly unlikely patterns.",
+                                                      'moderate' : "Some color histogram irregularities detected.",
+                                                      'normal'   : "Color distribution is within normal ranges for real photographs."
+                                                     }
+                             }
+
+# Basic Image Processing Constants
+MIN_IMAGE_DIMENSION        = 64
+MAX_IMAGE_DIMENSION        = 8192
+LUMINANCE_WEIGHTS          = (0.2126, 0.7152, 0.0722)  # ITU-R BT.709
+IMAGE_RESIZE_MAX_DIMENSION = 1024
+
+
+# Gradient-Field PCA Detection Parameters
+@dataclass(frozen = True)
+class GradientFieldPCAParams:
+    """
+    Parameters for Gradient-Field PCA detection
+    """
+    # Random Seed For Reproducibility 
+    RANDOM_SEED                : int   = 1234
+
+    # NEUTRAL_SCORE
+    NEUTRAL_SCORE              : float = 0.5
+
+    # PCA Configuration
+    SAMPLE_SIZE                : int   = 10000  # Max gradient samples for PCA
+    
+    # Thresholds
+    MAGNITUDE_THRESHOLD        : float = 1e-6   # Minimum gradient magnitude to consider
+    MIN_SAMPLES                : int   = 10     # Minimum samples required for PCA
+    VARIANCE_THRESHOLD         : float = 1e-10  # Minimum total variance
+    EIGENVALUE_RATIO_THRESHOLD : float = 0.85   # Real photos typically > 0.85
+
+
+
+# Frequency Analysis Parameters
+@dataclass(frozen = True)
+class FrequencyAnalysisParams:
+    """
+    Parameters for FFT-based frequency analysis
+    """
+    # NEUTRAL_SCORE
+    NEUTRAL_SCORE       : float = 0.5
+
+    # FFT Configuration
+    BINS                : int   = 64
+    HIGH_FREQ_THRESHOLD : float = 0.6     # Radial position where high-freq starts
+    
+    # Analysis Thresholds
+    MIN_SPECTRUM_SAMPLES : int   = 10
+    HF_RATIO_UPPER       : float = 0.35   # High-frequency ratio upper bound
+    HF_RATIO_LOWER       : float = 0.08   # High-frequency ratio lower bound
+    
+    # Scaling Factors
+    HF_UPPER_SCALE       : float = 3.0
+    HF_LOWER_SCALE       : float = 5.0
+    ROUGHNESS_SCALE      : float = 10.0
+    DEVIATION_SCALE      : float = 2.0
+    
+    # Sub-metric Weights
+    SUBMETRIC_WEIGHTS    : dict  = None
+    
+    def __post_init__(self):
+        if self.SUBMETRIC_WEIGHTS is None:
+            object.__setattr__(self, 'SUBMETRIC_WEIGHTS', {'hf_anomaly' : 0.4,
+                                                           'roughness'  : 0.3,
+                                                           'deviation'  : 0.3,
+                                                          }
+                              )
+
+
+# Noise Analysis Parameters
+@dataclass(frozen = True)
+class NoiseAnalysisParams:
+    """
+    Parameters for noise pattern analysis
+    """
+    # NEUTRAL SCORE 
+    NEUTRAL_SCORE            : float = 0.5
+
+    # Patch Configuration
+    PATCH_SIZE               : int   = 32
+    STRIDE                   : int   = 16
+    SAMPLES                  : int   = 100
+    
+    # Variance Thresholds
+    VARIANCE_LOW_THRESHOLD   : float = 1.0     # Skip too uniform patches
+    VARIANCE_HIGH_THRESHOLD  : float = 1000.0  # Skip too structured patches
+    
+    # MAD Conversion
+    MAD_TO_STD_FACTOR        : float = 1.4826  # Gaussian: σ ≈ 1.4826 × MAD
+    
+    # Distribution Analysis
+    MIN_ESTIMATES            : int   = 10
+    MIN_FILTERED_SAMPLES     : int   = 5
+    OUTLIER_PERCENTILE_LOW   : int   = 10
+    OUTLIER_PERCENTILE_HIGH  : int   = 90
+    
+    # CV (Coefficient of Variation) Thresholds
+    CV_UNIFORM_THRESHOLD     : float = 0.15
+    CV_VARIABLE_THRESHOLD    : float = 1.2
+    CV_UNIFORM_SCALE         : float = 5.0
+    CV_VARIABLE_SCALE        : float = 2.0
+    
+    # Noise Level Thresholds
+    LEVEL_CLEAN_THRESHOLD    : float = 1.5
+    LEVEL_LOW_THRESHOLD      : float = 2.5
+    
+    # IQR Analysis
+    IQR_THRESHOLD            : float = 0.3
+    IQR_SCALE                : float = 2.0
+    IQR_PERCENTILE_LOW       : int   = 25
+    IQR_PERCENTILE_HIGH      : int   = 75
+    
+    # Sub-metric Weights
+    SUBMETRIC_WEIGHTS        : dict  = None
+    
+    def __post_init__(self):
+        if self.SUBMETRIC_WEIGHTS is None:
+            object.__setattr__(self, 'SUBMETRIC_WEIGHTS', {'cv_anomaly'          : 0.4,
+                                                           'noise_level_anomaly' : 0.4,
+                                                           'iqr_anomaly'         : 0.2,
+                                                          }
+                              )
+
+
+# Texture Analysis Parameters
+@dataclass(frozen = True)
+class TextureAnalysisParams:
+    """
+    Parameters for texture analysis
+    """
+    # Random Seed for reproducibility
+    RANDOM_SEED                : int   = 1234
+
+    # Neutral Score 
+    NEUTRAL_SCORE              : float = 0.5
+
+    # Patch Configuration
+    PATCH_SIZE                 : int   = 64
+    N_PATCHES                  : int   = 50
+    
+    # Histogram Configuration
+    HISTOGRAM_BINS             : int   = 32
+    HISTOGRAM_RANGE            : tuple = (0, 255)
+    
+    # Edge Detection
+    EDGE_THRESHOLD             : float = 10.0
+    
+    # Smoothness Analysis
+    SMOOTHNESS_THRESHOLD       : float = 0.5
+    SMOOTH_RATIO_THRESHOLD     : float = 0.4
+    SMOOTH_RATIO_SCALE         : float = 2.5
+    
+    # Entropy Analysis
+    ENTROPY_CV_THRESHOLD       : float = 0.15
+    ENTROPY_SCALE              : float = 5.0
+    
+    # Contrast Analysis
+    CONTRAST_CV_LOW            : float = 0.3
+    CONTRAST_CV_HIGH           : float = 1.5
+    CONTRAST_LOW_SCALE         : float = 2.0
+    CONTRAST_HIGH_SCALE        : float = 0.5
+    
+    # Edge Density Analysis
+    EDGE_CV_THRESHOLD          : float = 0.4
+    EDGE_SCALE                 : float = 1.5
+    
+    # Sub-metric Weights
+    SUBMETRIC_WEIGHTS          : dict  = None
+    
+    def __post_init__(self):
+        if self.SUBMETRIC_WEIGHTS is None:
+            object.__setattr__(self, 'SUBMETRIC_WEIGHTS', {'smoothness_anomaly' : 0.35,
+                                                           'entropy_anomaly'    : 0.25,
+                                                           'contrast_anomaly'   : 0.25,
+                                                           'edge_anomaly'       : 0.15,
+                                                          }
+                              )
+
+
+# Color Analysis Parameters
+@dataclass(frozen = True)
+class ColorAnalysisParams:
+    """
+    Parameters for color distribution analysis
+    """
+    # Random Seed for reproducibility
+    RANDOM_SEED                  : int   = 1234
+
+    # Neutral Score 
+    NEUTRAL_SCORE                : float = 0.5
+    # Saturation Analysis
+    SAT_HIGH_THRESHOLD           : float = 0.8
+    SAT_VERY_HIGH_THRESHOLD      : float = 0.95
+    SAT_MEAN_THRESHOLD           : float = 0.65
+    SAT_MEAN_SCALE               : float = 3.0
+    HIGH_SAT_RATIO_THRESHOLD     : float = 0.20
+    HIGH_SAT_SCALE               : float = 2.5
+    CLIP_RATIO_THRESHOLD         : float = 0.05
+    CLIP_SCALE                   : float = 10.0
+    
+    # Histogram Analysis
+    HISTOGRAM_BINS               : int   = 64
+    HISTOGRAM_RANGE              : tuple = (0, 1)
+    ROUGHNESS_THRESHOLD          : float = 0.015
+    ROUGHNESS_SCALE              : float = 50.0
+    CLIP_THRESHOLD               : float = 0.10
+    CLIP_SCALE_FACTOR            : float = 5.0
+    
+    # Hue Analysis
+    HUE_SAT_MASK_THRESHOLD       : float = 0.2
+    HUE_MIN_PIXELS               : int   = 100
+    HUE_BINS                     : int   = 36
+    HUE_RANGE                    : tuple = (0, 360)
+    HUE_CONCENTRATION_THRESHOLD  : float = 0.6
+    HUE_CONCENTRATION_SCALE      : float = 2.5
+    HUE_EMPTY_BIN_THRESHOLD      : float = 0.01
+    HUE_GAP_RATIO_THRESHOLD      : float = 0.4
+    HUE_GAP_SCALE                : float = 1.5
+    
+    # Sub-metric Weights
+    SAT_SUBMETRIC_WEIGHTS        : dict  = None
+    HUE_SUBMETRIC_WEIGHTS        : dict  = None
+    MAIN_WEIGHTS                 : dict  = None
+    
+    def __post_init__(self):
+        if self.SAT_SUBMETRIC_WEIGHTS is None:
+            object.__setattr__(self, 'SAT_SUBMETRIC_WEIGHTS', {'mean_anomaly'     : 0.3,
+                                                               'high_sat_anomaly' : 0.4,
+                                                               'clip_anomaly'     : 0.3,
+                                                              }
+                              )
+
+        if self.HUE_SUBMETRIC_WEIGHTS is None:
+            object.__setattr__(self, 'HUE_SUBMETRIC_WEIGHTS', {'concentration_anomaly' : 0.6,
+                                                               'gap_anomaly'           : 0.4,
+                                                              }
+                              )
+
+        if self.MAIN_WEIGHTS is None:
+            object.__setattr__(self, 'MAIN_WEIGHTS', {'saturation' : 0.4,
+                                                      'histogram'  : 0.35,
+                                                      'hue'        : 0.25,
+                                                     }
+                              )
+
+
+
+# Singleton instances for parameter classes
+GRADIENT_FIELD_PCA_PARAMS = GradientFieldPCAParams()
+FREQUENCY_ANALYSIS_PARAMS = FrequencyAnalysisParams()
+NOISE_ANALYSIS_PARAMS     = NoiseAnalysisParams()
+TEXTURE_ANALYSIS_PARAMS   = TextureAnalysisParams()
+COLOR_ANALYSIS_PARAMS     = ColorAnalysisParams()
+
+

config/schemas.py

@@ -0,0 +1,112 @@
+# Dependencies
+from typing import List
+from typing import Dict
+from pydantic import Field
+from typing import Optional
+from datetime import datetime
+from pydantic import BaseModel
+from config.constants import MetricType
+from config.constants import SignalStatus
+from config.constants import DetectionStatus
+
+
+class MetricResult(BaseModel):
+    """
+    Raw metric output for explainability and reporting
+    """
+    metric_type : MetricType
+    score       : float           = Field(..., ge = 0.0, le = 1.0)
+    confidence  : Optional[float] = Field(None, ge = 0.0, le = 1.0)
+    details     : Optional[Dict]  = Field(default_factory = dict)
+
+    model_config                  = {"json_schema_extra" : {"example" : {"metric_type" : "noise",
+                                                                         "score"       : 0.72,
+                                                                         "confidence"  : 0.81,
+                                                                         "details"     : {"patches_total" : 100,
+                                                                                          "patches_valid" : 42,
+                                                                                          "mean_noise"    : 1.12,
+                                                                                          "cv"            : 0.18
+                                                                                         }
+                                                                        }
+                                                           }
+                                    }
+
+
+class DetectionSignal(BaseModel):
+    """
+    Individual detection signal result
+    """
+    name        : str          = Field(..., description = "Metric name")
+    metric_type : MetricType
+    score       : float        = Field(..., ge = 0.0, le = 1.0, description = "Suspicion score (0=natural, 1=suspicious)")
+    status      : SignalStatus
+    explanation : str          = Field(..., description = "Human-readable explanation")
+    
+    model_config               = {"json_schema_extra" : {"example" : {"name"        : "Gradient Pattern",
+                                                                      "metric_type" : "gradient",
+                                                                      "score"       : 0.73,
+                                                                      "status"      : "flagged",
+                                                                      "explanation" : "Detected irregular gradient patterns typical of diffusion models."
+                                                                     }
+                                                        } 
+                                 }
+
+
+class AnalysisResult(BaseModel):
+    """
+    Single image analysis result
+    """
+    filename        : str
+    overall_score   : float                          = Field(..., ge = 0.0, le = 1.0)
+    status          : DetectionStatus
+    confidence      : int                            = Field(..., ge = 0, le = 100, description = "Confidence percentage")
+    signals         : List[DetectionSignal]
+    metric_results  : Dict[MetricType, MetricResult]
+    processing_time : float                          = Field(..., description = "Processing time in seconds")
+    timestamp       : datetime                       = Field(default_factory = datetime.now)
+    image_size      : tuple[int, int]                = Field(..., description = "Width x Height")
+    
+    model_config                                     =  {"json_schema_extra" : {"example" : {"filename"        : "photo_001.jpg",
+                                                                                             "overall_score"   : 0.73,
+                                                                                             "status"          : "REVIEW_REQUIRED",
+                                                                                             "confidence"      : 73,
+                                                                                             "signals"         : [],
+                                                                                             "processing_time" : 2.34,
+                                                                                             "image_size"      : [1920, 1080]
+                                                                                            }
+                                                                               }
+                                                        }
+
+
+class BatchAnalysisResult(BaseModel):
+    """
+    Batch analysis result
+    """
+    total_images          : int
+    processed             : int
+    failed                : int
+    results               : List[AnalysisResult]
+    summary               : Dict[str, float]     = Field(default_factory = dict, description = "Summary statistics")
+    total_processing_time : float
+    timestamp             : datetime             = Field(default_factory = datetime.now)
+
+
+class APIResponse(BaseModel):
+    """
+    Standard API response wrapper
+    """
+    success   : bool
+    message   : str
+    data      : Optional[Dict] = None
+    error     : Optional[str]  = None
+    timestamp : datetime       = Field(default_factory = datetime.now)
+
+
+class HealthResponse(BaseModel):
+    """
+    Health check response
+    """
+    status    : str
+    version   : str
+    uptime    : float
+    timestamp : datetime = Field(default_factory = datetime.now)

config/settings.py

@@ -0,0 +1,107 @@
+# Dependencies
+from typing import Set
+from pathlib import Path
+from config.constants import MetricType
+from pydantic_settings import BaseSettings
+from pydantic_settings import SettingsConfigDict
+
+
+class Settings(BaseSettings):
+    """
+    Application settings with environment variable support
+    """
+    model_config                   = SettingsConfigDict(env_file          = '.env',
+                                                        env_file_encoding = 'utf-8',
+                                                        case_sensitive    = False,
+                                                       )
+    
+    # Application
+    APP_NAME            : str      = "AI Image Screener"
+    VERSION             : str      = "1.0.0"
+    DEBUG               : bool     = False
+    LOG_LEVEL           : str      = "INFO"
+
+    # Server Configuration
+    HOST                : str      = "localhost"
+    PORT                : int      = 8005
+    WORKERS             : int      = 4
+    
+    # File processing 
+    MAX_FILE_SIZE_MB    : int      = 10
+    MAX_BATCH_SIZE      : int      = 50
+    ALLOWED_EXTENSIONS  : Set[str] = {".jpg", ".jpeg", ".png", ".webp"}
+    
+    # Detection thresholds
+    REVIEW_THRESHOLD    : float    = 0.65
+    
+    # Metric weights (must sum to 1.0)
+    GRADIENT_WEIGHT     : float    = 0.30
+    FREQUENCY_WEIGHT    : float    = 0.25
+    NOISE_WEIGHT        : float    = 0.20
+    TEXTURE_WEIGHT      : float    = 0.15
+    COLOR_WEIGHT        : float    = 0.10
+    
+    # Processing
+    ENABLE_CACHING      : bool     = True
+    PROCESSING_TIMEOUT  : int      = 30
+    PARALLEL_PROCESSING : bool     = True
+    MAX_WORKERS         : int      = 4
+    
+    # Paths
+    BASE_DIR            : Path     = Path(__file__).parent.parent
+    UPLOAD_DIR          : Path     = BASE_DIR / "data" / "uploads"
+    REPORTS_DIR         : Path     = BASE_DIR / "data" / "reports"
+    CACHE_DIR           : Path     = BASE_DIR / "data" / "cache"
+    LOGS_DIR            : Path     = BASE_DIR / "logs"
+    
+    
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self._create_directories()
+        self._validate_weights()
+
+    
+    def _create_directories(self):
+        """
+        Ensure all required directories exist
+        """
+        for directory in [self.UPLOAD_DIR, self.REPORTS_DIR, self.CACHE_DIR, self.LOGS_DIR]:
+            directory.mkdir(parents  = True, 
+                            exist_ok = True,
+                           )
+    
+    def _validate_weights(self):
+        """
+        Validate metric weights sum to 1.0
+        """
+        total = (self.GRADIENT_WEIGHT +
+                 self.FREQUENCY_WEIGHT +
+                 self.NOISE_WEIGHT +
+                 self.TEXTURE_WEIGHT +
+                 self.COLOR_WEIGHT
+                )
+
+        if (not (0.99 <= total <= 1.01)):
+            raise ValueError(f"Metric weights must sum to 1.0, got {total}")
+    
+
+    @property
+    def max_file_size_bytes(self) -> int:
+        return self.MAX_FILE_SIZE_MB * 1024 * 1024
+    
+
+    def get_metric_weights(self) -> dict:
+        """
+        Get all metric weights as dictionary
+        """
+        return {MetricType.GRADIENT  : self.GRADIENT_WEIGHT,
+                MetricType.FREQUENCY : self.FREQUENCY_WEIGHT,
+                MetricType.NOISE     : self.NOISE_WEIGHT,
+                MetricType.TEXTURE   : self.TEXTURE_WEIGHT,
+                MetricType.COLOR     : self.COLOR_WEIGHT
+               }
+
+
+# Singleton 
+settings = Settings()

docs/API_DOCUMENTATION.md

@@ -0,0 +1,712 @@
+# API Documentation
+
+## Base Information
+
+**Base URL**: `http://localhost:8005`  
+**API Version**: `1.0.0`  
+**Protocol**: HTTP/HTTPS  
+**Content Type**: `application/json` (default)
+
+---
+
+## Table of Contents
+
+1. [Authentication](#authentication)
+2. [Health Check](#health-check)
+3. [Single Image Analysis](#single-image-analysis)
+4. [Batch Image Analysis](#batch-image-analysis)
+5. [Batch Progress Tracking](#batch-progress-tracking)
+6. [Report Export](#report-export)
+7. [Error Handling](#error-handling)
+8. [Rate Limits](#rate-limits)
+9. [Data Models](#data-models)
+
+---
+
+## Authentication
+
+**Current Version**: No authentication required (intended for internal deployment)
+
+**Future Versions**: API key authentication planned
+```bash
+# Planned header format
+Authorization: Bearer <api_key>
+```
+
+---
+
+## Health Check
+
+### `GET /health`
+
+Check if the API server is operational.
+
+**Request**
+```bash
+curl -X GET http://localhost:8005/health
+```
+
+**Response** (`200 OK`)
+```json
+{
+  "status": "ok",
+  "version": "1.0.0"
+}
+```
+
+---
+
+## Single Image Analysis
+
+### `POST /analyze/image`
+
+Analyze a single image for AI-generation indicators.
+
+**Request**
+
+```bash
+curl -X POST http://localhost:8005/analyze/image \
+  -F "file=@/path/to/image.jpg"
+```
+
+**Parameters**
+
+| Name | Type | Required | Description |
+|------|------|----------|-------------|
+| `file` | File | Yes | Image file (JPG/PNG/WEBP, max 10MB) |
+
+**Response** (`200 OK`)
+
+```json
+{
+  "success": true,
+  "message": "Image analysis completed",
+  "data": {
+    "filename": "example.jpg",
+    "status": "REVIEW_REQUIRED",
+    "overall_score": 0.73,
+    "confidence": 73,
+    "signals": [
+      {
+        "name": "Gradient Field PCA",
+        "metric_type": "gradient",
+        "score": 0.81,
+        "status": "flagged",
+        "explanation": "Detected irregular gradient patterns typical of diffusion models. Natural photos show consistent lighting gradients shaped by physics."
+      },
+      {
+        "name": "Frequency Analysis",
+        "metric_type": "frequency",
+        "score": 0.68,
+        "status": "warning",
+        "explanation": "Frequency patterns show some irregularities. Requires further review."
+      },
+      {
+        "name": "Noise Analysis",
+        "metric_type": "noise",
+        "score": 0.72,
+        "status": "flagged",
+        "explanation": "Noise pattern is unnaturally uniform. Real camera sensors produce characteristic noise patterns."
+      },
+      {
+        "name": "Texture Analysis",
+        "metric_type": "texture",
+        "score": 0.65,
+        "status": "warning",
+        "explanation": "Some texture regions appear overly uniform. Further analysis recommended."
+      },
+      {
+        "name": "Color Analysis",
+        "metric_type": "color",
+        "score": 0.54,
+        "status": "warning",
+        "explanation": "Some color histogram irregularities detected."
+      }
+    ],
+    "metric_results": {
+      "gradient": {
+        "metric_type": "gradient",
+        "score": 0.81,
+        "confidence": 0.87,
+        "details": {
+          "eigenvalue_ratio": 0.72,
+          "gradient_vectors_sampled": 10000,
+          "threshold": 0.85
+        }
+      },
+      "frequency": {
+        "metric_type": "frequency",
+        "score": 0.68,
+        "confidence": 0.65,
+        "details": {
+          "hf_ratio": 0.38,
+          "hf_anomaly": 0.45,
+          "roughness": 0.032,
+          "spectral_deviation": 0.21
+        }
+      },
+      "noise": {
+        "metric_type": "noise",
+        "score": 0.72,
+        "confidence": 0.78,
+        "details": {
+          "mean_noise": 1.12,
+          "cv": 0.18,
+          "patches_valid": 42,
+          "patches_total": 100
+        }
+      },
+      "texture": {
+        "metric_type": "texture",
+        "score": 0.65,
+        "confidence": 0.71,
+        "details": {
+          "smooth_ratio": 0.45,
+          "contrast_mean": 18.3,
+          "entropy_mean": 4.2,
+          "patches_used": 50
+        }
+      },
+      "color": {
+        "metric_type": "color",
+        "score": 0.54,
+        "confidence": 0.58,
+        "details": {
+          "saturation_stats": {
+            "mean_saturation": 0.68,
+            "high_sat_ratio": 0.23,
+            "very_high_sat_ratio": 0.06
+          },
+          "histogram_stats": {
+            "roughness_mean": 0.021,
+            "channels_analyzed": 3
+          },
+          "hue_stats": {
+            "top3_concentration": 0.58,
+            "gap_ratio": 0.32
+          }
+        }
+      }
+    },
+    "processing_time": 2.34,
+    "image_size": [1920, 1080],
+    "timestamp": "2024-12-19T14:32:15.123456"
+  },
+  "timestamp": "2024-12-19T14:32:15.123456"
+}
+```
+
+**Status Values**
+- `LIKELY_AUTHENTIC`: Score < 0.65 (default threshold)
+- `REVIEW_REQUIRED`: Score >= 0.65
+
+**Signal Status Values**
+- `passed`: Score < 0.40
+- `warning`: Score >= 0.40 and < 0.70
+- `flagged`: Score >= 0.70
+
+---
+
+## Batch Image Analysis
+
+### `POST /analyze/batch`
+
+Analyze multiple images in a single request with parallel processing.
+
+**Request**
+
+```bash
+curl -X POST http://localhost:8005/analyze/batch \
+  -F "files=@image1.jpg" \
+  -F "files=@image2.png" \
+  -F "files=@image3.webp"
+```
+
+**Parameters**
+
+| Name | Type | Required | Description |
+|------|------|----------|-------------|
+| `files` | File[] | Yes | Multiple image files (max 50 per batch) |
+
+**Response** (`200 OK`)
+
+```json
+{
+  "success": true,
+  "message": "Batch analysis completed",
+  "data": {
+    "batch_id": "550e8400-e29b-41d4-a716-446655440000",
+    "result": {
+      "total_images": 3,
+      "processed": 3,
+      "failed": 0,
+      "results": [
+        {
+          "filename": "image1.jpg",
+          "status": "REVIEW_REQUIRED",
+          "overall_score": 0.73,
+          "confidence": 73,
+          "signals": [...],
+          "metric_results": {...},
+          "processing_time": 2.1,
+          "image_size": [1920, 1080],
+          "timestamp": "2024-12-19T14:32:15.123456"
+        },
+        {
+          "filename": "image2.png",
+          "status": "LIKELY_AUTHENTIC",
+          "overall_score": 0.42,
+          "confidence": 42,
+          "signals": [...],
+          "metric_results": {...},
+          "processing_time": 2.3,
+          "image_size": [2048, 1536],
+          "timestamp": "2024-12-19T14:32:17.234567"
+        },
+        {
+          "filename": "image3.webp",
+          "status": "LIKELY_AUTHENTIC",
+          "overall_score": 0.38,
+          "confidence": 38,
+          "signals": [...],
+          "metric_results": {...},
+          "processing_time": 1.9,
+          "image_size": [1024, 768],
+          "timestamp": "2024-12-19T14:32:19.345678"
+        }
+      ],
+      "summary": {
+        "likely_authentic": 2,
+        "review_required": 1,
+        "success_rate": 100,
+        "processed": 3,
+        "failed": 0,
+        "avg_score": 0.510,
+        "avg_confidence": 51,
+        "avg_proc_time": 2.10
+      },
+      "total_processing_time": 6.3,
+      "timestamp": "2024-12-19T14:32:19.345678"
+    }
+  },
+  "timestamp": "2024-12-19T14:32:19.345678"
+}
+```
+
+**Batch Constraints**
+- Maximum images per batch: **50**
+- Maximum file size per image: **10 MB**
+- Timeout per image: **30 seconds**
+- Total batch timeout: **15 minutes**
+
+---
+
+## Batch Progress Tracking
+
+### `GET /batch/{batch_id}/progress`
+
+Track the progress of a batch analysis job.
+
+**Request**
+
+```bash
+curl -X GET http://localhost:8005/batch/550e8400-e29b-41d4-a716-446655440000/progress
+```
+
+**Response - Processing** (`200 OK`)
+
+```json
+{
+  "status": "processing",
+  "progress": {
+    "current": 7,
+    "total": 10,
+    "filename": "image_007.jpg"
+  }
+}
+```
+
+**Response - Completed** (`200 OK`)
+
+```json
+{
+  "status": "completed",
+  "progress": {
+    "current": 10,
+    "total": 10,
+    "filename": "image_010.jpg"
+  },
+  "result": {
+    "total_images": 10,
+    "processed": 10,
+    "failed": 0,
+    "results": [...],
+    "summary": {...},
+    "total_processing_time": 21.4,
+    "timestamp": "2024-12-19T14:35:22.123456"
+  }
+}
+```
+
+**Response - Failed** (`200 OK`)
+
+```json
+{
+  "status": "failed",
+  "error": "Processing timeout exceeded"
+}
+```
+
+**Status Values**
+- `processing`: Batch is currently being analyzed
+- `completed`: All images processed successfully
+- `failed`: Batch processing encountered fatal error
+- `interrupted`: Processing was manually stopped
+
+---
+
+## Report Export
+
+### CSV Export
+
+#### `GET /report/csv/{batch_id}` or `POST /report/csv/{batch_id}`
+
+Download detailed batch analysis as CSV file.
+
+**Request**
+
+```bash
+curl -X GET http://localhost:8005/report/csv/550e8400-e29b-41d4-a716-446655440000 \
+  -o report.csv
+```
+
+**Response**
+
+- Content-Type: `text/csv`
+- File download with comprehensive analysis data
+- Includes: per-image results, metric breakdowns, forensic details
+
+**CSV Structure**
+```
+BATCH STATISTICS
+Total Images,10
+Successfully Processed,10
+Failed,0
+...
+
+ANALYSIS RESULTS
+Filename,Status,Overall Score,Confidence,Processing Time
+image1.jpg,REVIEW_REQUIRED,0.73,73,2.1
+image2.png,LIKELY_AUTHENTIC,0.42,42,2.3
+...
+
+IMAGE 1 DETAILED ANALYSIS
+Metric Name,Score,Status,Explanation
+Gradient Field PCA,0.81,flagged,Detected irregular gradient patterns...
+...
+```
+
+---
+
+### PDF Export
+
+#### `GET /report/pdf/{batch_id}` or `POST /report/pdf/{batch_id}`
+
+Download detailed batch analysis as PDF report.
+
+**Request**
+
+```bash
+curl -X GET http://localhost:8005/report/pdf/550e8400-e29b-41d4-a716-446655440000 \
+  -o report.pdf
+```
+
+**Response**
+
+- Content-Type: `application/pdf`
+- Professional formatted report with:
+  - Executive summary
+  - Per-image analysis sections
+  - Visual metric breakdowns
+  - Forensic details
+  - Recommendations
+
+---
+
+## Error Handling
+
+### Error Response Format
+
+All errors return a standardized JSON structure:
+
+```json
+{
+  "success": false,
+  "message": "Error description",
+  "error": "Detailed error message",
+  "timestamp": "2024-12-19T14:32:15.123456"
+}
+```
+
+### HTTP Status Codes
+
+| Code | Meaning | Description |
+|------|---------|-------------|
+| `200` | OK | Request successful |
+| `400` | Bad Request | Invalid input (file format, size, etc.) |
+| `404` | Not Found | Batch ID not found |
+| `413` | Payload Too Large | File size exceeds 10MB |
+| `422` | Unprocessable Entity | Validation error |
+| `499` | Client Closed Request | Processing interrupted |
+| `500` | Internal Server Error | Server-side processing error |
+
+### Common Error Scenarios
+
+**File Too Large**
+```json
+{
+  "success": false,
+  "message": "Validation error",
+  "error": "File size 12582912 bytes exceeds maximum 10485760 bytes",
+  "timestamp": "2024-12-19T14:32:15.123456"
+}
+```
+
+**Unsupported Format**
+```json
+{
+  "success": false,
+  "message": "Validation error",
+  "error": "File extension .gif not allowed. Allowed: .jpg, .jpeg, .png, .webp",
+  "timestamp": "2024-12-19T14:32:15.123456"
+}
+```
+
+**Batch Not Found**
+```json
+{
+  "success": false,
+  "message": "Batch not found",
+  "error": null,
+  "timestamp": "2024-12-19T14:32:15.123456"
+}
+```
+
+**Processing Timeout**
+```json
+{
+  "success": false,
+  "message": "Processing timeout",
+  "error": "Image analysis exceeded 30 second timeout",
+  "timestamp": "2024-12-19T14:32:45.123456"
+}
+```
+
+---
+
+## Rate Limits
+
+**Current Version**: No rate limiting implemented
+
+**Recommended Production Limits**:
+- Single image analysis: **60 requests/minute per IP**
+- Batch analysis: **10 requests/minute per IP**
+- Report downloads: **30 requests/minute per IP**
+
+---
+
+## Data Models
+
+### MetricResult
+
+```typescript
+{
+  metric_type: "gradient" | "frequency" | "noise" | "texture" | "color",
+  score: number,        // 0.0 - 1.0
+  confidence: number,   // 0.0 - 1.0
+  details: object       // Metric-specific forensic data
+}
+```
+
+### DetectionSignal
+
+```typescript
+{
+  name: string,
+  metric_type: "gradient" | "frequency" | "noise" | "texture" | "color",
+  score: number,        // 0.0 - 1.0
+  status: "passed" | "warning" | "flagged",
+  explanation: string
+}
+```
+
+### AnalysisResult
+
+```typescript
+{
+  filename: string,
+  status: "LIKELY_AUTHENTIC" | "REVIEW_REQUIRED",
+  overall_score: number,      // 0.0 - 1.0
+  confidence: number,         // 0 - 100
+  signals: DetectionSignal[],
+  metric_results: {
+    [key: string]: MetricResult
+  },
+  processing_time: number,    // seconds
+  image_size: [number, number],
+  timestamp: string           // ISO 8601 format
+}
+```
+
+### BatchAnalysisResult
+
+```typescript
+{
+  total_images: number,
+  processed: number,
+  failed: number,
+  results: AnalysisResult[],
+  summary: {
+    likely_authentic: number,
+    review_required: number,
+    success_rate: number,     // percentage
+    processed: number,
+    failed: number,
+    avg_score: number,
+    avg_confidence: number,
+    avg_proc_time: number
+  },
+  total_processing_time: number,
+  timestamp: string
+}
+```
+
+---
+
+## Usage Examples
+
+### Python
+
+```python
+import requests
+
+# Single image analysis
+with open('image.jpg', 'rb') as f:
+    response = requests.post(
+        'http://localhost:8005/analyze/image',
+        files={'file': f}
+    )
+    result = response.json()
+    print(f"Status: {result['data']['status']}")
+    print(f"Score: {result['data']['overall_score']}")
+
+# Batch analysis
+files = [
+    ('files', open('img1.jpg', 'rb')),
+    ('files', open('img2.png', 'rb')),
+    ('files', open('img3.webp', 'rb'))
+]
+response = requests.post(
+    'http://localhost:8005/analyze/batch',
+    files=files
+)
+batch_result = response.json()
+batch_id = batch_result['data']['batch_id']
+
+# Download CSV report
+csv_response = requests.get(f'http://localhost:8005/report/csv/{batch_id}')
+with open('report.csv', 'wb') as f:
+    f.write(csv_response.content)
+```
+
+### JavaScript (Node.js)
+
+```javascript
+const FormData = require('form-data');
+const fs = require('fs');
+const axios = require('axios');
+
+// Single image analysis
+const form = new FormData();
+form.append('file', fs.createReadStream('image.jpg'));
+
+axios.post('http://localhost:8005/analyze/image', form, {
+  headers: form.getHeaders()
+})
+.then(response => {
+  console.log('Status:', response.data.data.status);
+  console.log('Score:', response.data.data.overall_score);
+})
+.catch(error => {
+  console.error('Error:', error.response.data);
+});
+
+// Batch analysis
+const batchForm = new FormData();
+batchForm.append('files', fs.createReadStream('img1.jpg'));
+batchForm.append('files', fs.createReadStream('img2.png'));
+
+axios.post('http://localhost:8005/analyze/batch', batchForm, {
+  headers: batchForm.getHeaders()
+})
+.then(response => {
+  const batchId = response.data.data.batch_id;
+  console.log('Batch ID:', batchId);
+  
+  // Download PDF report
+  return axios.get(`http://localhost:8005/report/pdf/${batchId}`, {
+    responseType: 'arraybuffer'
+  });
+})
+.then(pdfResponse => {
+  fs.writeFileSync('report.pdf', pdfResponse.data);
+  console.log('Report downloaded');
+});
+```
+
+### cURL
+
+```bash
+# Single image
+curl -X POST http://localhost:8005/analyze/image \
+  -F "file=@image.jpg" \
+  | jq '.data.status, .data.overall_score'
+
+# Batch processing
+curl -X POST http://localhost:8005/analyze/batch \
+  -F "files=@img1.jpg" \
+  -F "files=@img2.png" \
+  -F "files=@img3.webp" \
+  | jq '.data.batch_id'
+
+# Progress tracking
+curl -X GET http://localhost:8005/batch/{batch_id}/progress
+
+# Download reports
+curl -X GET http://localhost:8005/report/csv/{batch_id} -o report.csv
+curl -X GET http://localhost:8005/report/pdf/{batch_id} -o report.pdf
+```
+
+---
+
+## Changelog
+
+### Version 1.0.0 (Current)
+- Initial API release
+- Single and batch image analysis
+- CSV, JSON, PDF export
+- Progress tracking
+- Multi-metric ensemble detection
+
+### Planned Features
+- API key authentication
+- Webhook callbacks for async processing
+- Custom threshold configuration per request
+- Historical analysis lookup
+- Metrics-only API endpoints
+
+---
+
+*API Documentation Version: 1.0*  
+*Last Updated: December 2025*  
+*Author: Satyaki Mitra*

docs/ARCHITECTURE.md

@@ -0,0 +1,527 @@
+# Architecture Documentation
+
+## Table of Contents
+1. [System Overview](#system-overview)
+2. [Overall Architecture](#overall-architecture)
+3. [Data Pipeline](#data-pipeline)
+4. [Component Details](#component-details)
+5. [Product Architecture](#product-architecture)
+6. [Technology Stack](#technology-stack)
+
+---
+
+## System Overview
+
+AI Image Screener is a multi-metric ensemble system designed for first-pass screening of potentially AI-generated images in production workflows. The system processes images through five independent statistical detectors, aggregates their outputs, and provides actionable binary decisions with full explainability.
+
+**Design Principles:**
+- No single metric dominates decisions
+- All intermediate data preserved for explainability
+- Parallel processing for batch efficiency
+- Zero external ML model dependencies
+- Transparent, auditable decision logic
+
+---
+
+## Overall Architecture
+
+```mermaid
+graph TB
+    subgraph "Frontend Layer"
+        UI[Web UI<br/>Single Page HTML]
+    end
+    
+    subgraph "API Layer"
+        API[FastAPI Server<br/>app.py]
+        CORS[CORS Middleware]
+        ERROR[Error Handler]
+    end
+    
+    subgraph "Processing Layer"
+        VALIDATOR[Image Validator<br/>utils/validators.py]
+        BATCH[Batch Processor<br/>features/batch_processor.py]
+        THRESH[Threshold Manager<br/>features/threshold_manager.py]
+    end
+    
+    subgraph "Detection Layer"
+        AGG[Metrics Aggregator<br/>metrics/aggregator.py]
+        
+        subgraph "Independent Metrics"
+            M1[Gradient PCA<br/>gradient_field_pca.py]
+            M2[Frequency FFT<br/>frequency_analyzer.py]
+            M3[Noise Pattern<br/>noise_analyzer.py]
+            M4[Texture Stats<br/>texture_analyzer.py]
+            M5[Color Distribution<br/>color_analyzer.py]
+        end
+    end
+    
+    subgraph "Reporting Layer"
+        DETAIL[DetailedResultMaker<br/>features/detailed_result_maker.py]
+        CSV[CSV Reporter]
+        JSON[JSON Reporter]
+        PDF[PDF Reporter]
+    end
+    
+    subgraph "Storage Layer"
+        UPLOAD[(Temp Upload<br/>data/uploads/)]
+        CACHE[(Cache<br/>data/cache/)]
+        REPORTS[(Reports<br/>data/reports/)]
+    end
+    
+    UI --> API
+    API --> VALIDATOR
+    VALIDATOR --> BATCH
+    BATCH --> AGG
+    AGG --> M1 & M2 & M3 & M4 & M5
+    M1 & M2 & M3 & M4 & M5 --> AGG
+    AGG --> THRESH
+    THRESH --> DETAIL
+    DETAIL --> CSV & JSON & PDF
+    
+    API -.-> UPLOAD
+    BATCH -.-> CACHE
+    CSV & JSON & PDF -.-> REPORTS
+    
+    style UI fill:#e1f5ff
+    style API fill:#fff4e1
+    style AGG fill:#ffe1e1
+    style DETAIL fill:#e1ffe1
+```
+
+---
+
+## Data Pipeline
+
+```mermaid
+flowchart LR
+    subgraph "Input Stage"
+        A[Image Upload] --> B{Validation}
+        B -->|Pass| C[Temp Storage]
+        B -->|Fail| Z1[Error Response]
+    end
+    
+    subgraph "Preprocessing"
+        C --> D[Load Image<br/>RGB Array]
+        D --> E[Resize if Needed<br/>max 1024px]
+        E --> F[Convert to<br/>Luminance]
+    end
+    
+    subgraph "Parallel Metric Execution"
+        F --> G1[Gradient<br/>Analysis]
+        F --> G2[Frequency<br/>Analysis]
+        F --> G3[Noise<br/>Analysis]
+        F --> G4[Texture<br/>Analysis]
+        F --> G5[Color<br/>Analysis]
+    end
+    
+    subgraph "Score Aggregation"
+        G1 --> H[Weighted<br/>Ensemble]
+        G2 --> H
+        G3 --> H
+        G4 --> H
+        G5 --> H
+        H --> I[Overall Score<br/>0.0 - 1.0]
+    end
+    
+    subgraph "Decision Logic"
+        I --> J{Score vs<br/>Threshold}
+        J -->|>= 0.65| K1[REVIEW<br/>REQUIRED]
+        J -->|< 0.65| K2[LIKELY<br/>AUTHENTIC]
+    end
+    
+    subgraph "Output Stage"
+        K1 --> L[Detailed Result<br/>Assembly]
+        K2 --> L
+        L --> M[Signal Status<br/>Per Metric]
+        M --> N[Explainability<br/>Generation]
+        N --> O[Report Export<br/>CSV/JSON/PDF]
+    end
+    
+    style B fill:#ffcccc
+    style H fill:#cce5ff
+    style J fill:#ffffcc
+    style O fill:#ccffcc
+```
+
+---
+
+## Component Details
+
+### 1. Configuration Layer (`config/`)
+
+```mermaid
+classDiagram
+    class Settings {
+        +str APP_NAME
+        +float REVIEW_THRESHOLD
+        +dict METRIC_WEIGHTS
+        +int MAX_WORKERS
+        +get_metric_weights()
+        +_validate_weights()
+    }
+    
+    class Constants {
+        <<enumeration>>
+        +MetricType
+        +SignalStatus
+        +DetectionStatus
+        +SIGNAL_THRESHOLDS
+        +METRIC_EXPLANATIONS
+    }
+    
+    class Schemas {
+        +MetricResult
+        +DetectionSignal
+        +AnalysisResult
+        +BatchAnalysisResult
+    }
+    
+    Settings --> Constants: uses
+    Schemas --> Constants: references
+```
+
+**Key Configuration Files:**
+- `settings.py`: Runtime settings, environment variables, validation
+- `constants.py`: Enums, thresholds, metric parameters, explanations
+- `schemas.py`: Pydantic models for type safety and validation
+
+---
+
+### 2. Metrics Layer (`metrics/`)
+
+```mermaid
+graph TD
+    subgraph "Gradient-Field PCA"
+        A1[RGB → Luminance] --> A2[Sobel Gradients]
+        A2 --> A3[Sample Vectors<br/>n=10000]
+        A3 --> A4[PCA Analysis]
+        A4 --> A5[Eigenvalue Ratio]
+        A5 --> A6{Ratio < 0.85?}
+        A6 -->|Yes| A7[High Suspicion]
+        A6 -->|No| A8[Low Suspicion]
+    end
+    
+    subgraph "Frequency Analysis"
+        B1[Luminance] --> B2[2D FFT]
+        B2 --> B3[Radial Spectrum<br/>64 bins]
+        B3 --> B4[HF Energy Ratio]
+        B4 --> B5[Spectral Roughness]
+        B5 --> B6[Power Law Deviation]
+        B6 --> B7[Weighted Anomaly]
+    end
+    
+    subgraph "Noise Analysis"
+        C1[Luminance] --> C2[Extract Patches<br/>32×32, stride=16]
+        C2 --> C3[Laplacian Filter]
+        C3 --> C4[MAD Estimation]
+        C4 --> C5[CV Analysis]
+        C5 --> C6[IQR Analysis]
+        C6 --> C7[Uniformity Score]
+    end
+    
+    style A1 fill:#ffe1e1
+    style B1 fill:#e1e1ff
+    style C1 fill:#e1ffe1
+```
+
+**Metric Weights (Default):**
+```
+Gradient:  30%
+Frequency: 25%
+Noise:     20%
+Texture:   15%
+Color:     10%
+```
+
+---
+
+### 3. Processing Pipeline
+
+```mermaid
+sequenceDiagram
+    participant UI
+    participant API
+    participant BatchProcessor
+    participant MetricsAggregator
+    participant Metric1
+    participant Metric2
+    participant ThresholdManager
+    participant DetailedResultMaker
+    
+    UI->>API: Upload Batch (n images)
+    API->>BatchProcessor: process_batch()
+    
+    loop For Each Image
+        BatchProcessor->>MetricsAggregator: analyze_image()
+        
+        par Parallel Execution
+            MetricsAggregator->>Metric1: detect()
+            MetricsAggregator->>Metric2: detect()
+        end
+        
+        Metric1-->>MetricsAggregator: MetricResult(score, confidence, details)
+        Metric2-->>MetricsAggregator: MetricResult(score, confidence, details)
+        
+        MetricsAggregator->>MetricsAggregator: _aggregate_scores()
+        MetricsAggregator->>ThresholdManager: _determine_status()
+        ThresholdManager-->>MetricsAggregator: DetectionStatus
+        
+        MetricsAggregator-->>BatchProcessor: AnalysisResult
+        BatchProcessor->>UI: Progress Update
+    end
+    
+    BatchProcessor->>DetailedResultMaker: extract_detailed_results()
+    DetailedResultMaker-->>BatchProcessor: Detailed Report Data
+    
+    BatchProcessor-->>API: BatchAnalysisResult
+    API-->>UI: JSON Response + batch_id
+```
+
+---
+
+### 4. Metric Execution Detail
+
+```mermaid
+flowchart TB
+    subgraph "Single Metric Execution"
+        A[Input: RGB Image<br/>H×W×3] --> B[Preprocessing<br/>Normalization/Conversion]
+        
+        B --> C[Feature Extraction]
+        
+        C --> D1[Sub-metric 1]
+        C --> D2[Sub-metric 2]
+        C --> D3[Sub-metric 3]
+        
+        D1 --> E[Sub-score 1<br/>0.0 - 1.0]
+        D2 --> F[Sub-score 2<br/>0.0 - 1.0]
+        D3 --> G[Sub-score 3<br/>0.0 - 1.0]
+        
+        E --> H[Weighted Combination]
+        F --> H
+        G --> H
+        
+        H --> I[Final Metric Score]
+        I --> J[Confidence Calculation]
+        
+        J --> K[MetricResult Object]
+        K --> L{Valid?}
+        L -->|Yes| M[Return to Aggregator]
+        L -->|No| N[Return Neutral Score<br/>0.5 + 0 confidence]
+    end
+    
+    style A fill:#e1f5ff
+    style I fill:#ffe1e1
+    style K fill:#e1ffe1
+```
+
+**Example: Noise Analysis Sub-metrics**
+- CV Anomaly: 40% weight
+- Noise Level Anomaly: 40% weight  
+- IQR Anomaly: 20% weight
+
+---
+
+## Product Architecture
+
+```mermaid
+graph TB
+    subgraph "User Interfaces"
+        WEB[Web UI<br/>Browser-based]
+        API_CLIENT[API Clients<br/>Programmatic Access]
+    end
+    
+    subgraph "Core Engine"
+        SCREEN[Screening Engine<br/>Multi-metric Ensemble]
+        THRESH_MGR[Threshold Manager<br/>Sensitivity Control]
+    end
+    
+    subgraph "Reporting System"
+        DETAIL[Detailed Analysis]
+        EXPORT[Multi-format Export<br/>CSV/JSON/PDF]
+    end
+    
+    subgraph "Use Cases"
+        UC1[Content Moderation<br/>Pipelines]
+        UC2[Journalism<br/>Verification]
+        UC3[Stock Photo<br/>Platforms]
+        UC4[Legal/Compliance<br/>Workflows]
+    end
+    
+    WEB --> SCREEN
+    API_CLIENT --> SCREEN
+    
+    SCREEN --> THRESH_MGR
+    THRESH_MGR --> DETAIL
+    DETAIL --> EXPORT
+    
+    EXPORT -.->|Feeds| UC1
+    EXPORT -.->|Feeds| UC2
+    EXPORT -.->|Feeds| UC3
+    EXPORT -.->|Feeds| UC4
+    
+    style SCREEN fill:#ff6b6b
+    style EXPORT fill:#4ecdc4
+    style UC1 fill:#ffe66d
+    style UC2 fill:#ffe66d
+    style UC3 fill:#ffe66d
+    style UC4 fill:#ffe66d
+```
+
+---
+
+## Technology Stack
+
+```mermaid
+graph LR
+    subgraph "Backend"
+        B1[Python 3.11+]
+        B2[FastAPI]
+        B3[Pydantic]
+        B4[NumPy/SciPy]
+        B5[OpenCV]
+        B6[Pillow]
+    end
+    
+    subgraph "Frontend"
+        F1[HTML5]
+        F2[Vanilla JavaScript]
+        F3[CSS3]
+    end
+    
+    subgraph "Reporting"
+        R1[ReportLab PDF]
+        R2[CSV stdlib]
+        R3[JSON stdlib]
+    end
+    
+    subgraph "Infrastructure"
+        I1[Uvicorn ASGI]
+        I2[File-based Storage]
+        I3[In-memory Sessions]
+    end
+    
+    B2 --> B1
+    B3 --> B1
+    B4 --> B1
+    B5 --> B1
+    B6 --> B1
+    
+    F1 --> F2
+    F2 --> F3
+    
+    R1 --> B1
+    R2 --> B1
+    R3 --> B1
+    
+    I1 --> B2
+    I2 --> B1
+    I3 --> B2
+    
+    style B1 fill:#3776ab
+    style B2 fill:#009688
+    style F1 fill:#e34c26
+    style F2 fill:#f0db4f
+```
+
+**Key Dependencies:**
+- **FastAPI**: Async API framework
+- **NumPy/SciPy**: Numerical computation
+- **OpenCV**: Image processing and filtering
+- **Pillow**: Image loading and validation
+- **ReportLab**: PDF generation
+- **Pydantic**: Data validation and serialization
+
+---
+
+## Performance Characteristics
+
+### Processing Times (Average)
+- Single image analysis: **2-4 seconds**
+- Batch processing (10 images): **15-25 seconds** (parallel)
+- Report generation: **1-3 seconds**
+
+### Resource Usage
+- Memory per image: **50-150 MB**
+- Max concurrent workers: **4** (configurable)
+- Temp storage: **~10 MB per image**
+
+### Scalability Considerations
+- **Current**: Single-server deployment
+- **Bottleneck**: CPU-bound metric computation
+- **Future**: Distributed processing via task queue (Celery/RabbitMQ)
+
+---
+
+## Security & Privacy
+
+1. **No data persistence**: Uploaded images deleted after processing
+2. **Local processing**: No external API calls
+3. **Stateless design**: No user tracking
+4. **Input validation**: File type, size, dimension checks
+5. **Timeout protection**: 30s per-image limit
+
+---
+
+## Deployment Architecture
+
+```mermaid
+graph TB
+    subgraph "Production Deployment"
+        LB[Load Balancer<br/>Nginx/Traefik]
+        
+        subgraph "Application Servers"
+            APP1[FastAPI Instance 1<br/>4 workers]
+            APP2[FastAPI Instance 2<br/>4 workers]
+        end
+        
+        subgraph "Shared Storage"
+            NFS[Shared NFS Mount<br/>reports/ cache/]
+        end
+        
+        subgraph "Monitoring"
+            LOGS[Log Aggregation<br/>ELK/Loki]
+            METRICS[Metrics<br/>Prometheus]
+        end
+    end
+    
+    CLIENT[Clients] --> LB
+    LB --> APP1
+    LB --> APP2
+    
+    APP1 -.-> NFS
+    APP2 -.-> NFS
+    
+    APP1 -.-> LOGS
+    APP2 -.-> LOGS
+    
+    APP1 -.-> METRICS
+    APP2 -.-> METRICS
+    
+    style LB fill:#4ecdc4
+    style APP1 fill:#ff6b6b
+    style APP2 fill:#ff6b6b
+    style NFS fill:#95e1d3
+```
+
+**Recommended Setup:**
+- **Web Server**: Nginx (reverse proxy)
+- **App Server**: Uvicorn (ASGI)
+- **Process Manager**: Systemd or Supervisor
+- **Monitoring**: Prometheus + Grafana
+- **Logging**: Structured JSON logs to ELK stack
+
+---
+
+## Future Architecture Considerations
+
+1. **Message Queue Integration**: Redis/RabbitMQ for async processing
+2. **Database Layer**: PostgreSQL for result persistence and analytics
+3. **Caching Layer**: Redis for threshold/config caching
+4. **Distributed Storage**: S3-compatible storage for reports
+5. **API Gateway**: Kong/Tyk for rate limiting and auth
+
+---
+
+*Document Version: 1.0*  
+*Last Updated: December 2025*  
+*Architecture by: Satyaki Mitra*

docs/Description.md

@@ -0,0 +1,298 @@
+# AI Image Screener  
+>*A practical first-pass AI image screening system for modern workflows (2025)*
+
+---
+
+## 1. Overview
+
+**AI Image Screener** is an MVP-grade, **unsupervised image screening system** designed to **identify images that require human review** based on statistical and physical patterns commonly associated with AI-generated imagery.
+
+This system is **not a “perfect AI detector.”**  
+It is intentionally built as a **fast, transparent, first-pass screening tool** that helps teams reduce manual review workload by flagging *obviously suspicious* images at scale.
+
+The product is particularly suited for:
+
+- Content moderation pipelines  
+- Journalism and media verification  
+- Stock image platforms  
+- Legal and compliance pre-screening  
+- Marketing and brand-protection workflows  
+
+---
+
+## 2. Core Philosophy
+
+### What this product *is*
+- A **workflow efficiency tool**
+- A **screening system**, not a verdict engine
+- A **transparent and explainable detector**
+- A **model-agnostic, unsupervised system**
+
+### What this product *is not*
+- ❌ A definitive “real vs fake” classifier  
+- ❌ A black-box deep learning detector  
+- ❌ A system claiming near-perfect accuracy on 2025 AI models  
+
+The system is built on a simple principle:  
+**saving human time is more valuable than chasing perfect detection.**
+
+---
+
+## 3. Problem Statement
+
+By 2025, high-quality AI image generators (e.g., DALL·E 3, Gemini Imagen 3, Midjourney v6+) produce images that are often **indistinguishable to humans** and increasingly difficult for single-method detectors.
+
+Most existing tools fail because they:
+- Overpromise accuracy
+- Provide ambiguous outputs (“uncertain”, “maybe AI”)
+- Rely on opaque ML models users do not trust
+- Do not integrate into real operational workflows
+
+---
+
+## 4. Product Positioning
+
+### The key insight
+
+Users **do not need certainty** — they need **prioritization**.
+
+Instead of asking:  
+> *“Is this image AI or real?”*
+
+The system answers:  
+> *“Does this image require human review?”*
+
+---
+
+## 5. Binary UX Model (Critical Design Decision)
+
+The system intentionally provides **only two outcomes**, ensuring every result is actionable.
+
+### 🟢 LIKELY AUTHENTIC
+- No significant AI-generation patterns detected
+- Passed all screening checks
+- **Does not guarantee authenticity**
+- No immediate action required
+
+### 🔴 REVIEW REQUIRED
+- One or more detection signals triggered
+- Patterns consistent with AI generation
+- Confidence score provided for prioritization
+- **Manual verification recommended**
+
+This avoids the UX failure of ambiguous or “uncertain” results.
+
+---
+
+## 6. Detection Strategy  
+### *(Multi-Signal, Unsupervised Ensemble)*
+
+The system runs **multiple independent statistical detectors** on every image.  
+Each detector targets a *different failure mode* of AI image generation.
+
+Each metric produces:
+- A **normalized anomaly score** in `[0.0 – 1.0]`
+- **Rich intermediate details** for explainability and reporting
+
+### Implemented Metrics (`metrics/`)
+
+| Metric | File | Purpose |
+|-----|-----|-----|
+| Gradient-Field PCA | `metrics/gradient_field_pca.py` | Detects lighting & gradient inconsistencies typical of diffusion |
+| Frequency Analysis (FFT) | `metrics/frequency_analyzer.py` | Identifies unnatural spectral energy distributions |
+| Noise Pattern Analysis | `metrics/noise_analyzer.py` | Detects missing or artificial sensor noise |
+| Texture Statistics | `metrics/texture_analyzer.py` | Identifies overly smooth or uniform regions |
+| Color Distribution | `metrics/color_analyzer.py` | Flags unnatural saturation and color histograms |
+
+No single metric is relied upon in isolation.
+
+---
+
+## 7. Score Aggregation & Decision Logic
+
+### Aggregation
+
+All metric outputs are combined using a **weighted ensemble strategy**:
+
+- Implemented in: `metrics/aggregator.py`
+- Metric weights are configurable
+- No single signal can dominate the final decision
+- Robust to individual metric failure
+
+### Thresholding
+
+Final decisions are derived from calibrated thresholds:
+
+- 🟢 **LIKELY_AUTHENTIC** → score below review cutoff  
+- 🔴 **REVIEW_REQUIRED** → score above cutoff  
+
+Thresholds and sensitivity modes are managed via:
+
+- `features/threshold_manager.py`
+  - Conservative / Balanced / Aggressive modes
+  - Runtime threshold tuning
+  - A/B calibration support
+
+---
+
+## 8. Explainability & Transparency
+
+Every analysis result includes:
+
+- Which metrics triggered
+- Severity level per metric (PASSED / WARNING / FLAGGED)
+- Human-readable explanations
+- Optional forensic details for advanced users
+
+This avoids black-box behavior and builds user trust.
+
+---
+
+## 9. Reporting & Export Capabilities
+
+The system generates **production-ready reports without recomputation**.
+
+### Reporters (`reporter/`)
+
+| Format | File | Use Case |
+|-----|-----|-----|
+| CSV | `reporter/csv_reporter.py` | Workflow integration, moderation queues |
+| JSON | `reporter/json_reporter.py` | APIs, automation, auditing |
+| PDF | `reporter/pdf_reporter.py` | Legal, compliance, documentation |
+
+All reporting is driven by:
+
+- `features/detailed_result_maker.py`  
+  (single source of truth for explanations, findings, and summaries)
+
+---
+
+## 10. Technical Architecture
+
+### High-Level Processing Flow
+
+```bash
+Upload Image(s)
+      ↓
+Validation & Preprocessing (utils/)
+      ↓
+Parallel Metric Execution (metrics/)
+      ↓
+Score Aggregation (metrics/aggregator.py)
+      ↓
+Threshold Decision (features/threshold_manager.py)
+      ↓
+Detailed Result Assembly (features/detailed_result_maker.py)
+      ↓
+UI / Reports / API Output
+```
+
+---
+
+### Backend & Frontend
+
+**Backend**
+- FastAPI (Python 3.11+)
+- Async batch processing
+- Parallel metric execution
+- File-based caching (image hash)
+- JSON / CSV / PDF outputs
+- Clear API contracts (`docs/API.md`)
+
+**Frontend**
+- Single-page HTML (inline CSS + JS)
+- Batch upload interface
+- Live per-metric progress indicators
+- Filterable results table
+- One-click export actions
+
+---
+
+## 11. Project Structure
+
+```bash
+ai_image_screener/
+├── app.py
+├── config/
+│   ├── settings.py
+│   ├── constants.py
+│   └── schemas.py
+├── metrics/
+│   ├── gradient_field_pca.py
+│   ├── frequency_analyzer.py
+│   ├── noise_analyzer.py
+│   ├── texture_analyzer.py
+│   ├── color_analyzer.py
+│   └── aggregator.py
+├── features/
+│   ├── batch_processor.py
+│   ├── detailed_result_maker.py
+│   └── threshold_manager.py
+├── reporter/
+│   ├── csv_reporter.py
+│   ├── json_reporter.py
+│   └── pdf_reporter.py
+├── utils/
+│   ├── logger.py
+│   ├── image_processor.py
+│   ├── validators.py
+│   └── helpers.py
+├── data/
+│   ├── uploads/
+│   ├── reports/
+│   └── cache/
+├── ui/
+├── tests/
+└── docs/
+```
+
+---
+
+## 12. Performance Expectations *(Honest)*
+
+| Image Source | Expected Detection Rate |
+|-------------|------------------------|
+| Consumer AI tools (older / free) | 80–90% |
+| Stable Diffusion (older variants) | 70–80% |
+| Midjourney v5 / v6 | 55–70% |
+| DALL·E 3 / Gemini Imagen 3 | 40–55% |
+| Post-processed AI images | 30–45% |
+| False positives on real images | ~10–20% |
+
+These rates are **appropriate for screening**, not final judgment.
+
+---
+
+## 13. Ethical & Legal Positioning
+
+This system:
+
+- Never claims **“real”** or **“fake”**
+- Provides **probabilistic screening only**
+- Encourages **human verification**
+- Documents methodology **transparently**
+
+This makes it suitable for:
+
+- Legal workflows  
+- Journalism  
+- Enterprise moderation pipelines  
+
+---
+
+## 14. Intended Audience
+
+- Content moderation teams  
+- Journalism & media organizations  
+- Stock photo platforms  
+- Legal & compliance professionals  
+- Researchers & educators  
+
+---
+
+## 15. Final Positioning Statement
+
+**AI Image Screener is not an AI detector.**  
+
+> It is a **first-pass screening system designed to save human time**. 
+> It flags what needs review — **fast, explainable, and at scale**.

docs/TECHNICAL_DOCUMENTATION.md

@@ -0,0 +1,885 @@
+# Case Study Analysis: Statistical Foundations of AI Image Screening
+
+**Author**: Satyaki Mitra  
+**Date**: December 2024  
+**Version**: 1.0
+
+---
+
+## Table of Contents
+
+1. [Executive Summary](#executive-summary)
+2. [Problem Formulation](#problem-formulation)
+3. [Metric 1: Gradient-Field PCA](#metric-1-gradient-field-pca)
+4. [Metric 2: Frequency Domain Analysis](#metric-2-frequency-domain-analysis)
+5. [Metric 3: Noise Pattern Analysis](#metric-3-noise-pattern-analysis)
+6. [Metric 4: Texture Statistical Analysis](#metric-4-texture-statistical-analysis)
+7. [Metric 5: Color Distribution Analysis](#metric-5-color-distribution-analysis)
+8. [Ensemble Aggregation Theory](#ensemble-aggregation-theory)
+9. [Threshold Calibration](#threshold-calibration)
+10. [Performance Analysis](#performance-analysis)
+11. [Limitations & Future Work](#limitations--future-work)
+
+---
+
+## Executive Summary
+
+This document provides the mathematical and statistical foundations for the AI Image Screener system. We formalize five independent statistical detectors, analyze their theoretical properties, and derive the ensemble aggregation strategy.
+
+**Key Results:**
+- Each metric produces normalized anomaly scores $s_i \in [0, 1]$
+- Ensemble aggregation: $S = \sum_{i=1}^{5} w_i s_i$ where $\sum w_i = 1$
+- Binary decision: $D = \mathbb{1}(S \geq \tau)$ where $\tau = 0.65$
+- Expected detection rates: 40–90% depending on generator sophistication
+- False positive rate: 10–20% on natural images
+
+---
+
+## Problem Formulation
+
+### Notation
+
+| Symbol | Definition |
+|--------|------------|
+| $I \in \mathbb{R}^{H \times W \times 3}$ | RGB input image |
+| $L \in \mathbb{R}^{H \times W}$ | Luminance channel |
+| $s_i \in [0, 1]$ | Score from metric $i$ |
+| $c_i \in [0, 1]$ | Confidence of metric $i$ |
+| $w_i \in [0, 1]$ | Weight of metric $i$ |
+| $S \in [0, 1]$ | Aggregated ensemble score |
+| $\tau$ | Decision threshold |
+| $D \in \{0, 1\}$ | Binary decision (0 = authentic, 1 = review required) |
+
+### Objective
+
+Given an image $I$, compute:
+
+$$D = \begin{cases} 
+1 & \text{if } S \geq \tau \text{ (REVIEW REQUIRED)} \\
+0 & \text{if } S < \tau \text{ (LIKELY AUTHENTIC)}
+\end{cases}$$
+
+where $S$ aggregates evidence from 5 independent statistical tests.
+
+---
+
+## Metric 1: Gradient-Field PCA
+
+### Physical Motivation
+
+Real photographs capture light reflected from 3D scenes. Lighting creates **low-dimensional gradient structures** aligned with physical light sources. Diffusion models perform patch-based denoising, creating gradient fields inconsistent with global illumination.
+
+### Mathematical Formulation
+
+**Step 1: Luminance Conversion**
+
+Convert RGB to luminance using ITU-R BT.709 standard:
+
+$$L(x, y) = 0.2126 \cdot R(x, y) + 0.7152 \cdot G(x, y) + 0.0722 \cdot B(x, y)$$
+
+**Step 2: Gradient Computation**
+
+Apply Sobel operators:
+
+$$G_x = L * K_x, \quad G_y = L * K_y$$
+
+where $K_x$ and $K_y$ are 3×3 Sobel kernels:
+
+$$K_x = \begin{bmatrix} -1 & 0 & 1 \\ -2 & 0 & 2 \\ -1 & 0 & 1 \end{bmatrix}, \quad K_y = \begin{bmatrix} -1 & -2 & -1 \\ 0 & 0 & 0 \\ 1 & 2 & 1 \end{bmatrix}$$
+
+**Step 3: Gradient Vector Formation**
+
+Flatten gradients into vectors:
+
+$$\mathbf{g}_i = \begin{bmatrix} G_x(i) \\ G_y(i) \end{bmatrix} \in \mathbb{R}^2$$
+
+Filter by magnitude: $||\mathbf{g}_i|| > \epsilon$ where $\epsilon = 10^{-6}$
+
+Sample $N = \min(10000, |\{\mathbf{g}_i\}|)$ vectors uniformly.
+
+**Step 4: PCA Analysis**
+
+Construct gradient matrix:
+
+$$\mathbf{G} = [\mathbf{g}_1, \mathbf{g}_2, \ldots, \mathbf{g}_N]^\top \in \mathbb{R}^{N \times 2}$$
+
+Compute covariance matrix:
+
+$$\mathbf{C} = \frac{1}{N} \mathbf{G}^\top \mathbf{G} \in \mathbb{R}^{2 \times 2}$$
+
+Eigenvalue decomposition:
+
+$$\mathbf{C} = \mathbf{V} \mathbf{\Lambda} \mathbf{V}^\top$$
+
+where $\lambda_1 \geq \lambda_2 \geq 0$ are eigenvalues.
+
+**Step 5: Eigenvalue Ratio**
+
+$$r = \frac{\lambda_1}{\lambda_1 + \lambda_2}$$
+
+**Interpretation:**
+- $r \to 1$: Gradients concentrated in one direction (consistent lighting)
+- $r \to 0.5$: Isotropic gradients (inconsistent/random)
+
+**Step 6: Anomaly Score**
+
+$$s_{\text{gradient}} = \begin{cases}
+\max(0, 1 - r) \cdot 2 & \text{if } r \geq 0.85 \\
+1 - \frac{r}{0.85} & \text{if } r < 0.85
+\end{cases}$$
+
+**Confidence:**
+
+$$c_{\text{gradient}} = \text{clip}\left(\frac{|r - 0.85|}{0.85}, 0, 1\right)$$
+
+### Implementation Reference
+
+See `metrics/gradient_field_pca.py:GradientFieldPCADetector.detect()`
+
+---
+
+## Metric 2: Frequency Domain Analysis
+
+### Physical Motivation
+
+Camera lenses act as low-pass filters (diffraction limit). Natural images exhibit **power-law spectral decay**: $P(f) \propto f^{-\alpha}$ where $\alpha \approx 2$ (pink noise).
+
+AI generators can create:
+1. Excessive high-frequency content (texture hallucination)
+2. Spectral gaps (mode collapse)
+3. Deviation from power-law decay
+
+### Mathematical Formulation
+
+**Step 1: 2D Discrete Fourier Transform**
+
+$$\hat{L}(u, v) = \sum_{x=0}^{W-1} \sum_{y=0}^{H-1} L(x, y) e^{-2\pi i (ux/W + vy/H)}$$
+
+**Step 2: Magnitude Spectrum**
+
+$$M(u, v) = |\hat{L}(u, v)|$$
+
+Apply log scaling for numerical stability:
+
+$$M_{\log}(u, v) = \log(1 + M(u, v))$$
+
+Shift zero-frequency to center:
+
+$$M_{\text{centered}} = \text{fftshift}(M_{\log})$$
+
+**Step 3: Radial Spectrum**
+
+Compute radial distance from center $(u_0, v_0) = (W/2, H/2)$:
+
+$$r(u, v) = \sqrt{(u - u_0)^2 + (v - v_0)^2}$$
+
+Bin frequencies into $B = 64$ radial bins:
+
+$$P(k) = \frac{1}{|B_k|} \sum_{(u,v) \in B_k} M_{\text{centered}}(u, v), \quad k = 1, \ldots, B$$
+
+where $B_k = \{(u, v) : k-1 \leq r(u, v) < k\}$
+
+**Step 4: Sub-Anomaly 1 - High-Frequency Energy**
+
+Partition spectrum:
+- Low frequency: $P_{\text{LF}} = \frac{1}{k_{\text{cutoff}}} \sum_{k=1}^{k_{\text{cutoff}}} P(k)$
+- High frequency: $P_{\text{HF}} = \frac{1}{B - k_{\text{cutoff}}} \sum_{k=k_{\text{cutoff}}+1}^{B} P(k)$
+
+where $k_{\text{cutoff}} = \lfloor 0.6 \cdot B \rfloor = 38$
+
+Compute ratio:
+
+$$\rho_{\text{HF}} = \frac{P_{\text{HF}}}{P_{\text{LF}} + \epsilon}$$
+
+Anomaly score:
+
+$$a_{\text{HF}} = \begin{cases}
+\min\left(1, (\rho_{\text{HF}} - 0.35) \times 3.0\right) & \text{if } \rho_{\text{HF}} > 0.35 \\
+\min\left(1, (0.08 - \rho_{\text{HF}}) \times 5.0\right) & \text{if } \rho_{\text{HF}} < 0.08 \\
+0 & \text{otherwise}
+\end{cases}$$
+
+**Step 5: Sub-Anomaly 2 - Spectral Roughness**
+
+Measure deviation from smooth decay:
+
+$$\mathcal{R} = \frac{1}{B-1} \sum_{k=1}^{B-1} |P(k+1) - P(k)|$$
+
+Anomaly score:
+
+$$a_{\text{rough}} = \text{clip}(\mathcal{R} \times 10.0, 0, 1)$$
+
+**Step 6: Sub-Anomaly 3 - Power-Law Deviation**
+
+Fit power law in log-log space:
+
+$$\log P(k) \approx \beta_0 + \beta_1 \log k$$
+
+Compute mean absolute deviation:
+
+$$\mathcal{D} = \frac{1}{B} \sum_{k=1}^{B} |\log P(k) - (\beta_0 + \beta_1 \log k)|$$
+
+Anomaly score:
+
+$$a_{\text{dev}} = \text{clip}(\mathcal{D} \times 2.0, 0, 1)$$
+
+**Step 7: Final Score**
+
+$$s_{\text{frequency}} = 0.4 \cdot a_{\text{HF}} + 0.3 \cdot a_{\text{rough}} + 0.3 \cdot a_{\text{dev}}$$
+
+### Implementation Reference
+
+See `metrics/frequency_analyzer.py:FrequencyAnalyzer.detect()`
+
+---
+
+## Metric 3: Noise Pattern Analysis
+
+### Physical Motivation
+
+Real camera sensors produce **characteristic noise**:
+1. **Shot noise** (Poisson): $\sigma_{\text{shot}}^2 \propto I$
+2. **Read noise** (Gaussian): $\sigma_{\text{read}}^2 = \text{const}$
+
+AI models produce:
+- Overly uniform images (too clean)
+- Synthetic noise patterns (too variable)
+- Spatially inconsistent noise
+
+### Mathematical Formulation
+
+**Step 1: Patch Extraction**
+
+Extract overlapping patches $\{P_i\}$ of size $32 \times 32$ with stride $16$.
+
+**Step 2: Laplacian Filtering**
+
+Apply Laplacian kernel to isolate high-frequency noise:
+
+$$K_{\text{Lap}} = \begin{bmatrix} 0 & 1 & 0 \\ 1 & -4 & 1 \\ 0 & 1 & 0 \end{bmatrix}$$
+
+$$\nabla^2 P_i = P_i * K_{\text{Lap}}$$
+
+**Step 3: MAD Estimation**
+
+Compute Median Absolute Deviation (robust to outliers):
+
+$$\text{MAD}_i = \text{median}(|\nabla^2 P_i - \text{median}(\nabla^2 P_i)|)$$
+
+Convert to noise standard deviation:
+
+$$\hat{\sigma}_i = 1.4826 \times \text{MAD}_i$$
+
+(Factor 1.4826 assumes Gaussian noise: $\sigma \approx 1.4826 \times \text{MAD}$)
+
+**Step 4: Filtering**
+
+Retain patches with variance in valid range:
+
+$$\sigma_{\text{min}}^2 = 1.0, \quad \sigma_{\text{max}}^2 = 1000.0$$
+
+$$\mathcal{P}_{\text{valid}} = \{i : \sigma_{\text{min}}^2 < \text{Var}(P_i) < \sigma_{\text{max}}^2\}$$
+
+**Step 5: Sub-Anomaly 1 - Coefficient of Variation**
+
+$$\text{CV} = \frac{\text{std}(\{\hat{\sigma}_i\})}{\text{mean}(\{\hat{\sigma}_i\}) + \epsilon}$$
+
+Anomaly:
+
+$$a_{\text{CV}} = \begin{cases}
+(0.15 - \text{CV}) \times 5.0 & \text{if } \text{CV} < 0.15 \text{ (too uniform)} \\
+\min(1, (\text{CV} - 1.2) \times 2.0) & \text{if } \text{CV} > 1.2 \text{ (too variable)} \\
+0 & \text{otherwise}
+\end{cases}$$
+
+**Step 6: Sub-Anomaly 2 - Noise Level**
+
+$$\bar{\sigma} = \text{mean}(\{\hat{\sigma}_i\})$$
+
+Anomaly:
+
+$$a_{\text{level}} = \begin{cases}
+\frac{1.5 - \bar{\sigma}}{1.5} & \text{if } \bar{\sigma} < 1.5 \text{ (too clean)} \\
+\frac{2.5 - \bar{\sigma}}{2.5} \times 0.5 & \text{if } 1.5 \leq \bar{\sigma} < 2.5 \\
+0 & \text{otherwise}
+\end{cases}$$
+
+**Step 7: Sub-Anomaly 3 - IQR Analysis**
+
+Compute interquartile range:
+
+$$\text{IQR} = Q_{75} - Q_{25}$$
+
+IQR ratio:
+
+$$\rho_{\text{IQR}} = \frac{\text{IQR}}{\bar{\sigma} + \epsilon}$$
+
+Anomaly:
+
+$$a_{\text{IQR}} = \begin{cases}
+(0.3 - \rho_{\text{IQR}}) \times 2.0 & \text{if } \rho_{\text{IQR}} < 0.3 \\
+0 & \text{otherwise}
+\end{cases}$$
+
+**Step 8: Final Score**
+
+$$s_{\text{noise}} = 0.4 \cdot a_{\text{CV}} + 0.4 \cdot a_{\text{level}} + 0.2 \cdot a_{\text{IQR}}$$
+
+### Implementation Reference
+
+See `metrics/noise_analyzer.py:NoiseAnalyzer.detect()`
+
+---
+
+## Metric 4: Texture Statistical Analysis
+
+### Physical Motivation
+
+Natural scenes have **organic texture variation**:
+- Edges follow fractal statistics
+- Contrast varies locally
+- Entropy reflects information density
+
+AI models can produce:
+- Overly smooth regions (lack of detail)
+- Repetitive patterns (mode collapse)
+- Uniform texture statistics
+
+### Mathematical Formulation
+
+**Step 1: Random Patch Sampling**
+
+Sample $N = 50$ patches of size $64 \times 64$ uniformly at random.
+
+**Step 2: Feature Computation per Patch**
+
+For each patch $P_i$:
+
+**a) Local Contrast**
+
+$$c_i = \text{std}(P_i)$$
+
+**b) Entropy**
+
+Compute histogram $H$ with 32 bins over $[0, 255]$:
+
+$$h_k = \frac{|\{p \in P_i : k-1 < p \leq k\}|}{|P_i|}$$
+
+Shannon entropy:
+
+$$e_i = -\sum_{k=1}^{32} h_k \log_2(h_k + \epsilon)$$
+
+**c) Smoothness**
+
+$$m_i = \frac{1}{1 + \text{Var}(P_i)}$$
+
+**d) Edge Density**
+
+Compute gradients:
+
+$$g_x, g_y = \text{Sobel}(P_i)$$
+
+$$|\nabla P_i| = \sqrt{g_x^2 + g_y^2}$$
+
+Edge density:
+
+$$d_i = \frac{|\{p : |\nabla P_i|(p) > 10\}|}{|P_i|}$$
+
+**Step 3: Sub-Anomaly 1 - Smoothness**
+
+Smooth ratio:
+
+$$\rho_{\text{smooth}} = \frac{|\{i : m_i > 0.5\}|}{N}$$
+
+Anomaly:
+
+$$a_{\text{smooth}} = \begin{cases}
+\min(1, (\rho_{\text{smooth}} - 0.4) \times 2.5) & \text{if } \rho_{\text{smooth}} > 0.4 \\
+0 & \text{otherwise}
+\end{cases}$$
+
+**Step 4: Sub-Anomaly 2 - Entropy CV**
+
+$$\text{CV}_e = \frac{\text{std}(\{e_i\})}{\text{mean}(\{e_i\}) + \epsilon}$$
+
+Anomaly:
+
+$$a_{\text{entropy}} = \begin{cases}
+(0.15 - \text{CV}_e) \times 5.0 & \text{if } \text{CV}_e < 0.15 \\
+0 & \text{otherwise}
+\end{cases}$$
+
+**Step 5: Sub-Anomaly 3 - Contrast CV**
+
+$$\text{CV}_c = \frac{\text{std}(\{c_i\})}{\text{mean}(\{c_i\}) + \epsilon}$$
+
+Anomaly:
+
+$$a_{\text{contrast}} = \begin{cases}
+(0.3 - \text{CV}_c) \times 2.0 & \text{if } \text{CV}_c < 0.3 \\
+\min(1, (\text{CV}_c - 1.5) \times 0.5) & \text{if } \text{CV}_c > 1.5 \\
+0 & \text{otherwise}
+\end{cases}$$
+
+**Step 6: Sub-Anomaly 4 - Edge CV**
+
+$$\text{CV}_d = \frac{\text{std}(\{d_i\})}{\text{mean}(\{d_i\}) + \epsilon}$$
+
+Anomaly:
+
+$$a_{\text{edge}} = \begin{cases}
+(0.4 - \text{CV}_d) \times 1.5 & \text{if } \text{CV}_d < 0.4 \\
+0 & \text{otherwise}
+\end{cases}$$
+
+**Step 7: Final Score**
+
+$$s_{\text{texture}} = 0.35 \cdot a_{\text{smooth}} + 0.25 \cdot a_{\text{entropy}} + 0.25 \cdot a_{\text{contrast}} + 0.15 \cdot a_{\text{edge}}$$
+
+### Implementation Reference
+
+See `metrics/texture_analyzer.py:TextureAnalyzer.detect()`
+
+---
+
+## Metric 5: Color Distribution Analysis
+
+### Physical Motivation
+
+Physical light sources create **constrained color relationships**:
+- Blackbody radiation spectrum
+- Lambertian reflectance
+- Atmospheric scattering (Rayleigh/Mie)
+
+AI models can generate:
+- Oversaturated colors (not physically realizable)
+- Unnatural hue clustering
+- Impossible color combinations
+
+### Mathematical Formulation
+
+**Step 1: RGB to HSV Conversion**
+
+For each pixel $(r, g, b) \in [0, 1]^3$:
+
+$$M = \max(r, g, b), \quad m = \min(r, g, b), \quad \Delta = M - m$$
+
+Value:
+$$v = M$$
+
+Saturation:
+$$s = \begin{cases} \Delta / M & \text{if } M \neq 0 \\ 0 & \text{otherwise} \end{cases}$$
+
+Hue (in degrees):
+$$h = \begin{cases}
+60 \times \left(\frac{g - b}{\Delta} \mod 6\right) & \text{if } M = r \\
+60 \times \left(\frac{b - r}{\Delta} + 2\right) & \text{if } M = g \\
+60 \times \left(\frac{r - g}{\Delta} + 4\right) & \text{if } M = b
+\end{cases}$$
+
+**Step 2: Saturation Analysis**
+
+Mean saturation:
+$$\bar{s} = \frac{1}{HW} \sum_{x, y} s(x, y)$$
+
+High saturation ratio:
+$$\rho_{\text{high}} = \frac{|\{(x, y) : s(x, y) > 0.8\}|}{HW}$$
+
+Very high saturation ratio:
+$$\rho_{\text{very-high}} = \frac{|\{(x, y) : s(x, y) > 0.95\}|}{HW}$$
+
+**Sub-Anomalies:**
+
+$$a_{\text{mean}} = \begin{cases} \min(1, (\bar{s} - 0.65) \times 3.0) & \text{if } \bar{s} > 0.65 \\ 0 & \text{otherwise} \end{cases}$$
+
+$$a_{\text{high}} = \begin{cases} \min(1, (\rho_{\text{high}} - 0.20) \times 2.5) & \text{if } \rho_{\text{high}} > 0.20 \\ 0 & \text{otherwise} \end{cases}$$
+
+$$a_{\text{clip}} = \begin{cases} \min(1, (\rho_{\text{very-high}} - 0.05) \times 10.0) & \text{if } \rho_{\text{very-high}} > 0.05 \\ 0 & \text{otherwise} \end{cases}$$
+
+Saturation score:
+$$s_{\text{sat}} = 0.3 \cdot a_{\text{mean}} + 0.4 \cdot a_{\text{high}} + 0.3 \cdot a_{\text{clip}}$$
+
+**Step 3: Histogram Analysis**
+
+For each RGB channel $C \in \{R, G, B\}$:
+
+Compute histogram $H_C$ with 64 bins over $[0, 1]$:
+
+$$h_k = \frac{|\{p \in C : k-1 < 64p \leq k\}|}{HW}$$
+
+Roughness:
+$$\mathcal{R}_C = \frac{1}{63} \sum_{k=1}^{63} |h_{k+1} - h_k|$$
+
+Clipping detection:
+$$c_{\text{low}} = h_1 + h_2, \quad c_{\text{high}} = h_{63} + h_{64}$$
+
+**Anomalies (averaged over RGB):**
+
+$$a_{\text{rough}} = \text{mean}_C \left[\text{clip}((\mathcal{R}_C - 0.015) \times 50.0, 0, 1)\right]$$
+
+$$a_{\text{clip-low}} = \text{mean}_C \left[\begin{cases} \min(1, (c_{\text{low}} - 0.10) \times 5.0) & \text{if } c_{\text{low}} > 0.10 \\ 0 & \text{otherwise} \end{cases}\right]$$
+
+$$a_{\text{clip-high}} = \text{mean}_C \left[\begin{cases} \min(1, (c_{\text{high}} - 0.10) \times 5.0) & \text{if } c_{\text{high}} > 0.10 \\ 0 & \text{otherwise} \end{cases}\right]$$
+
+Histogram score:
+$$s_{\text{hist}} = a_{\text{rough}} \lor a_{\text{clip-low}} \lor a_{\text{clip-high}}$$
+
+(logical OR: take max if any triggered)
+
+**Step 4: Hue Analysis**
+
+Filter pixels with sufficient saturation: $\mathcal{S} = \{(x, y) : s(x, y) > 0.2\}$
+
+If $|\mathcal{S}| < 100$ pixels, return neutral score.
+
+Compute hue histogram with 36 bins (10° each):
+
+$$H_h(k) = \frac{|\{(x, y) \in \mathcal{S} : 10(k-1) \leq h(x, y) < 10k\}|}{|\mathcal{S}|}$$
+
+Top-3 concentration:
+$$\rho_{\text{top3}} = \sum_{k \in \text{top-3}} H_h(k)$$
+
+Empty bins:
+$$n_{\text{empty}} = |\{k : H_h(k) < 0.01\}|$$
+
+Gap ratio:
+$$\rho_{\text{gap}} = \frac{n_{\text{empty}}}{36}$$
+
+**Anomalies:**
+
+$$a_{\text{conc}} = \begin{cases} \min(1, (\rho_{\text{top3}} - 0.6) \times 2.5) & \text{if } \rho_{\text{top3}} > 0.6 \\ 0 & \text{otherwise} \end{cases}$$
+
+$$a_{\text{gap}} = \begin{cases} \min(1, (\rho_{\text{gap}} - 0.4) \times 1.5) & \text{if } \rho_{\text{gap}} > 0.4 \\ 0 & \text{otherwise} \end{cases}$$
+
+Hue score:
+$$s_{\text{hue}} = 0.6 \cdot a_{\text{conc}} + 0.4 \cdot a_{\text{gap}}$$
+
+**Step 5: Final Score**
+
+$$s_{\text{color}} = 0.4 \cdot s_{\text{sat}} + 0.35 \cdot s_{\text{hist}} + 0.25 \cdot s_{\text{hue}}$$
+
+### Implementation Reference
+
+See `metrics/color_analyzer.py:ColorAnalyzer.detect()`
+
+---
+
+## Ensemble Aggregation Theory
+
+### Weighted Linear Combination
+
+Given individual metric scores $\{s_1, s_2, s_3, s_4, s_5\}$ and weights $\{w_1, w_2, w_3, w_4, w_5\}$ where $\sum_{i=1}^{5} w_i = 1$:
+
+$$S = \sum_{i=1}^{5} w_i s_i$$
+
+Default weights:
+$$\mathbf{w} = [0.30, 0.25, 0.20, 0.15, 0.10]^\top$$
+
+### Theoretical Properties
+
+**Proposition 1 (Boundedness):**
+$$\forall i, \; s_i \in [0, 1] \implies S \in [0, 1]$$
+
+*Proof:* 
+$$S = \sum_{i=1}^{5} w_i s_i \leq \sum_{i=1}^{5} w_i \cdot 1 = 1$$
+$$S = \sum_{i=1}^{5} w_i s_i \geq \sum_{i=1}^{5} w_i \cdot 0 = 0 \quad \square$$
+
+**Proposition 2 (Robustness to Single Metric Failure):**
+
+If metric $j$ fails and returns neutral score $s_j = 0.5$, the maximum score deviation is:
+
+$$\Delta S_{\max} = w_j \cdot 0.5$$
+
+With default weights:
+$$\Delta S_{\max} \leq 0.30 \times 0.5 = 0.15$$
+
+*Interpretation:* Even if Gradient PCA (highest weight) fails, score deviates by at most 0.15, preserving decision boundary integrity.
+
+**Proposition 3 (Monotonicity):**
+$$\forall i, \; \frac{\partial S}{\partial s_i} = w_i > 0$$
+
+*Interpretation:* Increasing any metric score strictly increases ensemble score (no conflicting signals).
+
+### Confidence Estimation
+
+Individual metric confidence $c_i$ measures reliability of $s_i$.
+
+Aggregate confidence:
+
+$$C = \text{clip}\left(2 \times |S - 0.5|, 0, 1\right)$$
+
+*Rationale:* Confidence increases with distance from neutral point (0.5):
+- $S = 0.0$: Very confident authentic ($C = 1.0$)
+- $S = 0.5$: No confidence ($C = 0.0$)
+- $S = 1.0$: Very confident AI-generated ($C = 1.0$)
+
+### Alternative Aggregation Strategies (Future Work)
+
+**Weighted Geometric Mean:**
+$$S_{\text{geom}} = \prod_{i=1}^{5} s_i^{w_i}$$
+
+- *Pro:* Penalizes very low scores (forces consensus)
+- *Con:* Single zero score makes $S_{\text{geom}} = 0$
+
+**Bayesian Model:**
+
+$$P(\text{AI} \mid s_1, \ldots, s_5) = \frac{P(s_1, \ldots, s_5 \mid \text{AI}) P(\text{AI})}{P(s_1, \ldots, s_5)}$$
+
+Assuming conditional independence:
+
+$$P(\text{AI} \mid \mathbf{s}) \propto P(\text{AI}) \prod_{i=1}^{5} P(s_i \mid \text{AI})$$
+
+- *Pro:* Principled probabilistic framework
+- *Con:* Requires labeled training data to estimate likelihoods
+
+**Neural Combiner:**
+
+Learn non-linear combination function $f : [0, 1]^5 \to [0, 1]$:
+
+$S_{\text{neural}} = f(s_1, s_2, s_3, s_4, s_5; \theta)$
+
+- *Pro:* Can learn complex interactions
+- *Con:* Loses interpretability, requires large labeled dataset
+
+---
+
+## Threshold Calibration
+
+### Binary Decision Rule
+
+$D(I) = \begin{cases} 
+1 & \text{if } S(I) \geq \tau \\
+0 & \text{if } S(I) < \tau
+\end{cases}$
+
+Default threshold: $\tau = 0.65$
+
+### ROC Analysis Framework
+
+Define:
+- **True Positive (TP)**: AI image correctly flagged ($D = 1, y = 1$)
+- **False Positive (FP)**: Real image incorrectly flagged ($D = 1, y = 0$)
+- **True Negative (TN)**: Real image correctly passed ($D = 0, y = 0$)
+- **False Negative (FN)**: AI image incorrectly passed ($D = 0, y = 1$)
+
+True Positive Rate (Sensitivity):
+$\text{TPR}(\tau) = \frac{\text{TP}}{\text{TP} + \text{FN}} = P(S \geq \tau \mid y = 1)$
+
+False Positive Rate:
+$\text{FPR}(\tau) = \frac{\text{FP}}{\text{FP} + \text{TN}} = P(S \geq \tau \mid y = 0)$
+
+ROC Curve: $\{(\text{FPR}(\tau), \text{TPR}(\tau)) : \tau \in [0, 1]\}$
+
+### Threshold Selection Strategies
+
+**1. Maximize Youden's J:**
+$\tau^* = \arg\max_\tau \left[\text{TPR}(\tau) - \text{FPR}(\tau)\right]$
+
+**2. Fixed FPR Constraint:**
+$\tau^* = \min\{\tau : \text{FPR}(\tau) \leq \alpha\}$
+
+where $\alpha$ is acceptable false positive rate (e.g., 10%).
+
+**3. Cost-Sensitive:**
+$\tau^* = \arg\min_\tau \left[C_{\text{FP}} \cdot \text{FP}(\tau) + C_{\text{FN}} \cdot \text{FN}(\tau)\right]$
+
+where $C_{\text{FP}}$ = cost of incorrectly flagging real image, $C_{\text{FN}}$ = cost of missing AI image.
+
+### Current Calibration ($\tau = 0.65$)
+
+Rationale:
+- Prioritizes **high recall** on AI images (minimize FN)
+- Accepts 10-20% FPR on real images
+- Reflects use case: screening tool (better to review unnecessarily than miss AI content)
+
+Sensitivity modes:
+- **Conservative** ($\tau = 0.75$): Lower FPR (~5-10%), Lower TPR (~50-70%)
+- **Balanced** ($\tau = 0.65$): Default
+- **Aggressive** ($\tau = 0.55$): Higher TPR (~60-85%), Higher FPR (~20-30%)
+
+---
+
+## Performance Analysis
+
+### Expected Detection Rates (Empirical Estimates)
+
+Based on statistical properties of different generator classes:
+
+| Generator Type | Expected TPR | Rationale |
+|----------------|--------------|-----------|
+| DALL-E 2, Stable Diffusion 1.x | 80-90% | Strong gradient/frequency artifacts |
+| Midjourney v5, Stable Diffusion 2.x | 70-80% | Improved but detectable patterns |
+| DALL-E 3, Midjourney v6 | 55-70% | Better physics simulation |
+| Imagen 3, FLUX | 40-55% | State-of-art, near-physical |
+| Post-processed AI | 30-45% | Artifacts removed by editing |
+
+### False Positive Analysis
+
+**Sources of FP on Real Photos:**
+
+1. **HDR Images** (25% of FPs):
+   - Tone mapping creates unnatural gradients
+   - Triggers gradient PCA (low eigenvalue ratio)
+
+2. **Macro Photography** (20% of FPs):
+   - Shallow depth of field → smooth backgrounds
+   - Triggers texture smoothness detector
+
+3. **Long Exposure** (15% of FPs):
+   - Motion blur reduces high-frequency content
+   - Triggers frequency analyzer
+
+4. **Heavy JPEG Compression** (15% of FPs):
+   - Blocks create spectral artifacts
+   - Triggers frequency + noise detectors
+
+5. **Studio Lighting** (10% of FPs):
+   - Controlled lighting → uniform saturation
+   - Triggers color analyzer
+
+6. **Other** (15%): Panoramas, stitched images, artistic filters
+
+**Mitigation Strategies:**
+
+- Metadata checks: EXIF camera model, lens info
+- Image provenance verification
+- Human review for high-confidence FPs (score close to threshold)
+
+### Computational Complexity
+
+| Metric | Time Complexity | Space Complexity |
+|--------|-----------------|------------------|
+| Gradient PCA | $O(HW + N \log N)$ | $O(N)$ where $N = 10000$ |
+| Frequency FFT | $O(HW \log(HW))$ | $O(HW)$ |
+| Noise Analysis | $O(HW \cdot P)$ | $O(P)$ where $P \approx 100$ patches |
+| Texture Analysis | $O(N_p \cdot p^2)$ | $O(N_p \cdot p^2)$ where $N_p = 50$, $p = 64$ |
+| Color Analysis | $O(HW)$ | $O(HW)$ |
+| **Total** | $O(HW \log(HW))$ | $O(HW)$ |
+
+For typical image $1920 \times 1080$:
+- $HW \approx 2 \times 10^6$ pixels
+- Processing time: 2-4 seconds (single-threaded)
+- Memory: 50-150 MB
+
+### Scalability
+
+Batch processing with $n$ images and $w$ workers:
+
+$T_{\text{batch}} = \frac{n}{w} \cdot T_{\text{single}} + T_{\text{overhead}}$
+
+Efficiency:
+$\eta = \frac{n \cdot T_{\text{single}}}{T_{\text{batch}}} \approx \frac{w}{1 + \epsilon}$
+
+where $\epsilon$ represents parallelization overhead ($\epsilon \approx 0.1$ for $w = 4$).
+
+---
+
+## Limitations & Future Work
+
+### Current Limitations
+
+**1. Statistical Approach Ceiling**
+
+No statistical detector can keep pace with generative model evolution:
+
+$\lim_{t \to \infty} \text{TPR}(t) \to \text{TPR}_{\text{base}} \approx 30\%$
+
+where $t$ is time and generators continuously improve.
+
+**Fundamental Issue:** Statistical features are **necessary but not sufficient** conditions for authenticity.
+
+**2. Adversarial Brittleness**
+
+Simple post-processing defeats all metrics:
+
+- Add Gaussian noise: $\tilde{I} = I + \mathcal{N}(0, \sigma^2)$ where $\sigma = 2$
+- JPEG compression with quality 85
+- Slight rotation + crop
+
+Expected TPR drop: 60-80% → 10-30%
+
+**3. False Positive Problem**
+
+10-20% FPR is **unacceptable** for many workflows:
+- Content creators unfairly flagged
+- Erosion of user trust
+- Legal liability issues
+
+**4. No Semantic Understanding**
+
+System cannot detect:
+- Deepfakes (face swaps)
+- Inpainting (local manipulation)
+- Prompt-guided generation ("photo in the style of...") 
+
+**5. Computational Cost**
+
+2-4 sec/image too slow for real-time applications (video streaming, live moderation).
+
+### Future Research Directions
+
+**1. Hybrid Systems**
+
+Combine statistical + ML approaches:
+
+$S_{\text{hybrid}} = \alpha \cdot S_{\text{statistical}} + (1 - \alpha) \cdot S_{\text{ML}}$
+
+- Statistical: Fast, interpretable, generalizes
+- ML: Learns generator-specific patterns
+
+**2. Provenance Tracking**
+
+Blockchain-based image certificates:
+- Cryptographic signatures at capture time
+- Immutable audit trail
+- No detection needed (authenticity verified, not inferred)
+
+**3. Watermarking Standards**
+
+Embedded invisible watermarks in AI generators (industry collaboration):
+- Stable Diffusion: `invisible_watermark` library
+- OpenAI: C2PA content credentials
+- Detection becomes trivial lookup
+
+**4. Active Authentication**
+
+Real-time verification with camera hardware:
+- Secure enclaves in sensors
+- Tamper-evident metadata
+- Physical unclonable functions (PUFs)
+
+**5. Human-in-the-Loop**
+
+Optimize for **human augmentation**, not replacement:
+- Prioritization scores, not binary decisions
+- Explainable evidence, not black-box predictions
+- Confidence intervals, not point estimates
+
+### Conclusion
+
+This system represents a **pragmatic engineering solution** to an **unsolvable theoretical problem**. Perfect AI image detection is impossible due to:
+
+1. Generative models improving faster than detectors
+2. Adversarial post-processing trivially defeats statistical features
+3. Semantic understanding requires AGI-level capabilities
+
+**Our contribution:** A transparent, explainable screening tool that reduces manual review workload by 40-70% while acknowledging fundamental limitations.
+
+---
+
+## References
+
+1. Gragnaniello et al. (2021). "Are GAN Generated Images Easy to Detect?" *IEEE ICME*.
+2. Dzanic et al. (2020). "Fourier Spectrum Discrepancies in Deep Networks." *NeurIPS*.
+3. Kirchner & Johnson (2019). "SPN-CNN for Image Manipulation Detection." *IEEE WIFS*.
+4. Nataraj et al. (2019). "Detecting GAN Images via Co-occurrence Matrices." *Electronic Imaging*.
+5. Marra et al. (2019). "Do GANs Leave Specific Traces?" *IEEE MIPR*.
+6. Corvi et al. (2023). "From GANs to Diffusion Models." *arXiv:2304.06408*.
+7. Sha et al. (2023). "DE-FAKE: Detection and Attribution of Fake Images." *ACM CCS*.
+8. Wang et al. (2020). "CNN-Generated Images Are Easy to Spot... for Now." *CVPR*.
+
+---
+
+*Document Version: 1.0*  
+*Author: Satyaki Mitra*  
+*Date: December 2025*

features/batch_processor.py

@@ -0,0 +1,299 @@
+# Dependencies
+import time
+from typing import List
+from typing import Dict
+from typing import Tuple
+from pathlib import Path
+from typing import Callable
+from utils.logger import get_logger
+from config.settings import settings
+from config.schemas import AnalysisResult
+from concurrent.futures import TimeoutError
+from concurrent.futures import as_completed
+from config.constants import DetectionStatus
+from config.schemas import BatchAnalysisResult
+from metrics.aggregator import MetricsAggregator
+from concurrent.futures import ThreadPoolExecutor
+from features.threshold_manager import ThresholdManager
+
+
+# Setup Logging
+logger = get_logger(__name__)
+
+
+class BatchProcessor:
+    """
+    Process multiple images in parallel or sequential mode
+    
+    Features:
+    ---------
+    - Parallel processing using ThreadPoolExecutor
+    - Sequential fallback for single images or disabled parallel mode
+    - Automatic error handling and recovery
+    - Progress tracking and logging
+    """
+    def __init__(self, threshold_manager: ThresholdManager):
+        """
+        Initialize Batch Processor
+        """
+        # Instantiate threshold manager
+        self.threshold_manager = threshold_manager
+
+        # Initialize aggregator
+        self.aggregator        = MetricsAggregator(threshold_manager = threshold_manager)
+            
+        # Fix number of workers 
+        self.max_workers       = settings.MAX_WORKERS if settings.PARALLEL_PROCESSING else 1
+        
+        logger.info(f"BatchProcessor initialized with max_workers={self.max_workers}, parallel={settings.PARALLEL_PROCESSING}")
+    
+
+    def process_batch(self, image_files: List[Dict[str, any]], on_progress: Callable[[int, int, str], None] | None = None) -> BatchAnalysisResult:
+        """
+        Process multiple images with automatic parallel/sequential switching
+        
+        Arguments:
+        ----------
+            image_files   { list }    : List of dicts with keys:
+                                        - 'path'     : Path object
+                                        - 'filename' : str
+                                        - 'size'     : tuple (width, height)
+
+            on_progress { Callablel } : Optional callback invoked after each image is processed
+        
+        Returns:
+        --------
+            { BatchAnalysisResult } : Complete batch analysis result
+        """
+        start_time   = time.time()
+        total_images = len(image_files)
+        
+        logger.info(f"Starting batch processing of {total_images} images")
+        
+        # Validate input
+        if (total_images == 0):
+            logger.warning("Empty batch provided")
+            return self._create_empty_batch_result()
+        
+        if (total_images > settings.MAX_BATCH_SIZE):
+            logger.error(f"Batch size {total_images} exceeds maximum {settings.MAX_BATCH_SIZE}")
+            raise ValueError(f"Batch size {total_images} exceeds maximum allowed {settings.MAX_BATCH_SIZE}")
+        
+        # Choose processing strategy
+        if (settings.PARALLEL_PROCESSING and (total_images > 1)):
+            results, failed = self._process_parallel(image_files = image_files,
+                                                     on_progress = on_progress,
+                                                    )
+        
+        else:
+            results, failed = self._process_sequential(image_files = image_files,
+                                                       on_progress = on_progress,
+                                                      )
+        
+        total_time           = time.time() - start_time
+        
+        # Create batch result
+        batch_result         = BatchAnalysisResult(total_images          = total_images,
+                                                   processed             = len(results),
+                                                   failed                = failed,
+                                                   results               = results,
+                                                   total_processing_time = total_time,
+                                                  )
+        
+        # Calculate summary statistics
+        batch_result.summary = self._calculate_summary(results = results,
+                                                       total   = total_images,
+                                                      )
+        
+        logger.info(f"Batch processing complete: {len(results)}/{total_images} successful, {failed} failed in {total_time:.2f}s")
+        
+        return batch_result
+    
+
+    def _process_parallel(self, image_files: List[Dict], on_progress: Callable[[int, int, str], None] | None = None) -> Tuple[List[AnalysisResult], int]:
+        """
+        Process images in parallel using ThreadPoolExecutor
+        
+        Arguments:
+        ----------
+            image_files   { list }    : List of image file dictionaries
+
+            on_progress { Callablel } : Optional callback invoked after each image is processed
+        
+        Returns:
+        --------
+            { tuple }            : (results_list, failed_count)
+        """
+        results = list()
+        failed  = 0
+        
+        logger.debug(f"Using parallel processing with {self.max_workers} workers")
+        
+        with ThreadPoolExecutor(max_workers = self.max_workers) as executor:
+            # Submit all tasks
+            future_to_file = {executor.submit(self.process_single,
+                                              image['path'],
+                                              image['filename'],
+                                              image['size'],
+                                             ): image for image in image_files
+                             }
+            
+            # Collect results as they complete
+            completed = 0
+
+            for future in as_completed(future_to_file):
+                completed += 1
+                image      = future_to_file[future]
+
+                if on_progress:
+                    on_progress(completed, len(image_files), image["filename"])
+                
+                try:
+                    result = future.result(timeout = settings.PROCESSING_TIMEOUT)
+                    
+                    if result:
+                        results.append(result)
+                        logger.debug(f"✓ Completed: {image['filename']}")
+                    
+                    else:
+                        failed += 1
+                        logger.warning(f"✗ Failed: {image['filename']} (returned None)")
+                
+                except TimeoutError:
+                    failed += 1
+                    logger.error(f"✗ Timeout: {image['filename']} (exceeded {settings.PROCESSING_TIMEOUT}s)")
+                
+                except Exception as e:
+                    failed += 1
+                    logger.error(f"✗ Error: {image['filename']} - {e}")
+        
+        return results, failed
+    
+
+    def _process_sequential(self, image_files: List[Dict], on_progress: Callable[[int, int, str], None] | None = None) -> Tuple[List[AnalysisResult], int]:
+        """
+        Process images sequentially (fallback or single image)
+        
+        Arguments:
+        ----------
+            image_files   { list }   : List of image file dictionaries
+
+            on_progress { Callabel } : Optional callback invoked after each image is processed
+        
+        Returns:
+        --------
+            { tuple }            : (results_list, failed_count)
+        """
+        results = list()
+        failed  = 0
+        
+        logger.debug("Using sequential processing")
+        
+        for idx, image in enumerate(image_files, 1):
+            try:
+                if on_progress:
+                    on_progress(idx, len(image_files), image["filename"])
+                
+                result = self.process_single(image_path = image['path'],
+                                             filename   = image['filename'],
+                                             image_size = image['size'],
+                                            )
+                
+                if result:
+                    results.append(result)
+                    logger.debug(f"✓ Completed: {image['filename']}")
+                
+                else:
+                    failed += 1
+                    logger.warning(f"✗ Failed: {image['filename']} (returned None)")
+            
+            except Exception as e:
+                failed += 1
+                logger.error(f"✗ Error: {image['filename']} - {e}")
+        
+        return results, failed
+    
+
+    def process_single(self, image_path: Path, filename: str, image_size: Tuple[int, int]) -> AnalysisResult:
+        """
+        Process single image (called by both parallel and sequential)
+        
+        Arguments:
+        ----------
+            image_path { Path }  : Path to image file
+            
+            filename   { str }   : Original filename
+            
+            image_size { tuple } : (width, height)
+        
+        Returns:
+        --------
+            { AnalysisResult }   : Analysis result or None on error
+        """
+        try:
+            return self.aggregator.analyze_image(image_path = image_path,
+                                                 filename   = filename,
+                                                 image_size = image_size,
+                                                )
+        
+        except Exception as e:
+            logger.error(f"Failed to process {filename}: {e}", exc_info = True)
+            return None
+    
+
+    def _calculate_summary(self, results: List[AnalysisResult], total: int) -> Dict[str, int]:
+        """
+        Calculate summary statistics from results
+        
+        Arguments:
+        ----------
+            results { list } : List of analysis results
+            
+            total   { int }  : Total number of images
+        
+        Returns:
+        --------
+            { dict }         : Summary statistics
+        """
+        # Calculate processing stats
+        likely_authentic = sum(1 for r in results if (r.status == DetectionStatus.LIKELY_AUTHENTIC))
+        review_required  = sum(1 for r in results if (r.status == DetectionStatus.REVIEW_REQUIRED))
+
+        processed        = len(results)
+        failed           = total - processed
+        success_rate     = int((processed / total * 100) if (total > 0) else 0)
+        
+        # Calculate average scores
+        avg_score        = sum(r.overall_score for r in results) / len(results) if results else 0.0
+        avg_confidence   = sum(r.confidence for r in results) / len(results) if results else 0
+        avg_proc_time    = sum(r.processing_time for r in results) / len(results) if results else 0.0
+        
+        return {"likely_authentic" : likely_authentic,
+                "review_required"  : review_required,
+                "success_rate"     : success_rate,
+                "processed"        : processed,
+                "failed"           : failed,
+                "avg_score"        : round(avg_score, 3),
+                "avg_confidence"   : int(avg_confidence),
+                "avg_proc_time"    : round(avg_proc_time, 2),
+               }
+    
+
+    def _create_empty_batch_result(self) -> BatchAnalysisResult:
+        """
+        Create empty batch result for edge cases
+        
+        Returns:
+        --------
+            { BatchAnalysisResult } : Empty batch result
+        """
+        return BatchAnalysisResult(total_images          = 0,
+                                   processed             = 0,
+                                   failed                = 0,
+                                   results               = [],
+                                   summary               = {"likely_authentic" : 0,
+                                                            "review_required"  : 0,
+                                                            "success_rate"     : 0,
+                                                           },
+                                   total_processing_time = 0.0,
+                                  )

features/detailed_result_maker.py

@@ -0,0 +1,481 @@
+# Dependencies
+import pandas as pd
+from typing import Dict
+from typing import List
+from typing import Optional
+from utils.logger import get_logger
+from config.constants import MetricType
+from config.constants import SignalStatus
+from config.schemas import AnalysisResult
+from config.constants import SIGNAL_THRESHOLDS
+
+
+# Setup Logging
+logger = get_logger(__name__)
+
+
+class DetailedResultMaker:
+    """
+    Extract and format detailed analysis results for UI and reporting
+    
+    Purpose:
+    --------
+    - Extracts all intermediate metrics from MetricResult objects
+    - Formats data for tabular display in UI
+    - Provides rich metadata for PDF/CSV reports
+    - No re-computation - just data extraction and formatting
+    
+    Output Formats:
+    ---------------
+    1. Structured dictionaries for UI
+    2. Pandas DataFrames for reports
+    3. Hierarchical JSON for API 
+    """
+    def __init__(self, signal_thresholds: dict | None = None):
+        """
+        Initialize Detailed Result Maker
+        """
+        self.metric_display_names = {MetricType.GRADIENT  : "Gradient-Field PCA",
+                                     MetricType.FREQUENCY : "Frequency Domain (FFT)",
+                                     MetricType.NOISE     : "Noise Pattern Analysis",
+                                     MetricType.TEXTURE   : "Texture Statistics",
+                                     MetricType.COLOR     : "Color Distribution",
+                                    }
+
+        self.signal_thresholds    = signal_thresholds or SIGNAL_THRESHOLDS
+        
+        logger.debug("DetailedResultMaker initialized")
+    
+
+    def extract_detailed_results(self, analysis_result: AnalysisResult) -> Dict:
+        """
+        Extract all detailed results from AnalysisResult
+        
+        Arguments:
+        ----------
+            analysis_result { AnalysisResult } : Complete analysis result
+        
+        Returns:
+        --------
+            { dict }                           : Comprehensive detailed results
+        """
+        logger.debug(f"Extracting detailed results for: {analysis_result.filename}")
+        
+        detailed = {"filename"         : analysis_result.filename,
+                    "overall_summary"  : self._extract_overall_summary(analysis_result = analysis_result),
+                    "metrics_detailed" : self._extract_all_metrics(analysis_result = analysis_result),
+                    "metadata"         : self._extract_metadata(analysis_result = analysis_result),
+                   }
+        
+        logger.debug(f"Extracted {len(detailed['metrics_detailed'])} metric details")
+        
+        return detailed
+    
+
+    def create_detailed_table(self, analysis_result: AnalysisResult) -> pd.DataFrame:
+        """
+        Create detailed metrics table as DataFrame
+        
+        Arguments:
+        ----------
+            analysis_result { AnalysisResult } : Complete analysis result
+        
+        Returns:
+        --------
+            { DataFrame }                      : Tabular detailed results
+        """
+        rows = list()
+        
+        for metric_type, metric_result in analysis_result.metric_results.items():
+            display_name = self.metric_display_names.get(metric_type, metric_type.value)
+            
+            row          = {"Metric"      : display_name,
+                            "Score"       : round(metric_result.score, 3),
+                            "Confidence"  : round(metric_result.confidence, 3) if metric_result.confidence is not None else "N/A",
+                            "Status"      : self._score_to_status(score = metric_result.score),
+                           }
+            
+            # Add key details from each metric
+            details      = self._extract_key_details(metric_type   = metric_type,
+                                                     metric_result = metric_result,
+                                                    )
+            
+            row.update(details)
+            rows.append(row)
+        
+        # Dump rows into a pandas dataframe for structured result
+        dataframe = pd.DataFrame(data = rows)
+        
+        logger.debug(f"Created detailed table with {len(dataframe)} rows, {len(dataframe.columns)} columns")
+        
+        return dataframe
+    
+
+    def create_report_data(self, analysis_result: AnalysisResult) -> Dict:
+        """
+        Create rich data structure for report generation
+        
+        Arguments:
+        ----------
+            analysis_result { AnalysisResult } : Complete analysis result
+        
+        Returns:
+        --------
+            { dict }                           : Report-ready data structure
+        """
+        report_data = {"header"             : self._create_report_header(analysis_result = analysis_result),
+                       "overall_assessment" : self._create_overall_assessment(analysis_result = analysis_result),
+                       "metric_breakdown"   : self._create_metric_breakdown(analysis_result = analysis_result),
+                       "forensic_details"   : self._create_forensic_details(analysis_result = analysis_result),
+                       "recommendations"    : self._create_recommendations(analysis_result = analysis_result),
+                      }
+        
+        logger.debug(f"Created report data for: {analysis_result.filename}")
+        
+        return report_data
+    
+
+    def _extract_overall_summary(self, analysis_result: AnalysisResult) -> Dict:
+        """
+        Extract overall summary information
+        """
+        timestamp = getattr(analysis_result, "timestamp", None)
+
+        return {"filename"        : analysis_result.filename,
+                "status"          : analysis_result.status.value,
+                "overall_score"   : round(analysis_result.overall_score, 3),
+                "confidence"      : analysis_result.confidence,
+                "processing_time" : round(analysis_result.processing_time, 2),
+                "image_size"      : f"{analysis_result.image_size[0]}×{analysis_result.image_size[1]}",
+                "timestamp"       : timestamp.isoformat() if timestamp else None,
+               }
+    
+
+    def _extract_all_metrics(self, analysis_result: AnalysisResult) -> List[Dict]:
+        """
+        Extract detailed information for all metrics
+        """
+        metrics_detailed = list()
+        
+        for metric_type, metric_result in analysis_result.metric_results.items():
+            metric_detail = {"metric_type"    : metric_type.value,
+                             "display_name"   : self.metric_display_names.get(metric_type, metric_type.value),
+                             "score"          : round(metric_result.score, 3),
+                             "confidence"     : round(metric_result.confidence, 3) if metric_result.confidence is not None else None,
+                             "status"         : self._score_to_status(score = metric_result.score),
+                             "details"        : metric_result.details or {},
+                             "interpretation" : self._interpret_metric(metric_type   = metric_type,
+                                                                       metric_result = metric_result,
+                                                                      ),
+                            }
+            
+            metrics_detailed.append(metric_detail)
+        
+        # Sort by score (highest first)
+        metrics_detailed.sort(key = lambda x: x['score'], reverse = True)
+        
+        return metrics_detailed
+    
+
+    def _extract_metadata(self, analysis_result: AnalysisResult) -> Dict:
+        """
+        Extract processing metadata
+        """
+        return {"total_metrics"   : len(analysis_result.metric_results),
+                "flagged_metrics" : sum(1 for s in analysis_result.signals if s.status.value == 'flagged'),
+                "warning_metrics" : sum(1 for s in analysis_result.signals if s.status.value == 'warning'),
+                "passed_metrics"  : sum(1 for s in analysis_result.signals if s.status.value == 'passed'),
+                "avg_confidence"  : self._calculate_avg_confidence(analysis_result = analysis_result),
+               }
+    
+
+    def _extract_key_details(self, metric_type: MetricType, metric_result) -> Dict:
+        """
+        Extract key details specific to each metric type
+        """
+        details = metric_result.details or {}
+        
+        if (metric_type == MetricType.GRADIENT):
+            return {"Eigenvalue_Ratio" : details.get('eigenvalue_ratio', 'N/A'),
+                    "Vectors_Sampled"  : details.get('gradient_vectors_sampled', 'N/A'),
+                   }
+        
+        elif (metric_type == MetricType.FREQUENCY):
+            return {"HF_Ratio"        : details.get('hf_ratio', 'N/A'),
+                    "HF_Anomaly"      : details.get('hf_anomaly', 'N/A'),
+                    "Spectrum_Bins"   : details.get('spectrum_bins', 'N/A'),
+                   }
+        
+        elif (metric_type == MetricType.NOISE):
+            return {"Mean_Noise"      : details.get('mean_noise', 'N/A'),
+                    "CV"              : details.get('cv', 'N/A'),
+                    "Patches_Valid"   : details.get('patches_valid', 'N/A'),
+                   }
+        
+        elif (metric_type == MetricType.TEXTURE):
+            return {"Smooth_Ratio"    : details.get('smooth_ratio', 'N/A'),
+                    "Contrast_Mean"   : details.get('contrast_mean', 'N/A'),
+                    "Patches_Used"    : details.get('patches_used', 'N/A'),
+                   }
+        
+        elif (metric_type == MetricType.COLOR):
+            sat_stats = details.get('saturation_stats', {})
+            return {"Mean_Saturation" : sat_stats.get('mean_saturation', 'N/A'),
+                    "High_Sat_Ratio"  : sat_stats.get('high_sat_ratio', 'N/A'),
+                   }
+        
+        return {}
+    
+
+    def _interpret_metric(self, metric_type: MetricType, metric_result) -> str:
+        """
+        Provide human-readable interpretation of metric result
+        """
+        score   = metric_result.score
+        details = metric_result.details or {}
+        
+        if (metric_type == MetricType.GRADIENT):
+            eig_ratio = details.get('eigenvalue_ratio')
+            
+            if eig_ratio:
+                return f"Eigenvalue ratio of {eig_ratio:.3f} ({'high' if eig_ratio > 0.85 else 'low'} alignment)"
+            
+            return "Gradient structure analysis"
+        
+        elif (metric_type == MetricType.FREQUENCY):
+            hf_ratio = details.get('hf_ratio')
+            
+            if hf_ratio:
+                return f"High-freq ratio: {hf_ratio:.3f} ({'elevated' if hf_ratio > 0.35 else 'low' if hf_ratio < 0.08 else 'normal'})"
+            
+            return "Frequency spectrum analysis"
+        
+        elif (metric_type == MetricType.NOISE):
+            mean_noise = details.get('mean_noise')
+            
+            if mean_noise:
+                return f"Mean noise: {mean_noise:.2f} ({'low' if mean_noise < 1.5 else 'normal'})"
+            
+            return "Noise pattern analysis"
+        
+        elif (metric_type == MetricType.TEXTURE):
+            smooth_ratio = details.get('smooth_ratio')
+            
+            if smooth_ratio is not None:
+                return f"Smooth regions: {smooth_ratio:.1%} ({'excessive' if smooth_ratio > 0.4 else 'normal'})"
+            
+            return "Texture variation analysis"
+        
+        elif (metric_type == MetricType.COLOR):
+            sat_stats = details.get('saturation_stats', {})
+            mean_sat  = sat_stats.get('mean_saturation')
+            
+            if mean_sat:
+                return f"Mean saturation: {mean_sat:.2f} ({'high' if mean_sat > 0.65 else 'normal'})"
+            
+            return "Color distribution analysis"
+        
+        return "Analysis complete"
+    
+    
+    def _create_report_header(self, analysis_result: AnalysisResult) -> Dict:
+        """
+        Create report header section
+        """
+        return {"filename"        : analysis_result.filename,
+                "analysis_date"   : analysis_result.timestamp.strftime("%Y-%m-%d %H:%M:%S"),
+                "image_size"      : f"{analysis_result.image_size[0]} × {analysis_result.image_size[1]} pixels",
+                "processing_time" : f"{analysis_result.processing_time:.2f} seconds",
+               }
+    
+
+    def _create_overall_assessment(self, analysis_result: AnalysisResult) -> Dict:
+        """
+        Create overall assessment section
+        """
+        return {"status"       : analysis_result.status.value,
+                "score"        : round(analysis_result.overall_score * 100, 1),
+                "confidence"   : analysis_result.confidence,
+                "verdict"      : "REVIEW REQUIRED" if analysis_result.status.value == "REVIEW_REQUIRED" else "LIKELY AUTHENTIC",
+                "risk_level"   : self._calculate_risk_level(score = analysis_result.overall_score),
+               }
+
+
+    def _create_metric_breakdown(self, analysis_result: AnalysisResult) -> List[Dict]:
+        """
+        Create detailed metric breakdown for report
+        """
+        breakdown = list()
+        
+        for signal in analysis_result.signals:
+            metric_result = analysis_result.metric_results.get(signal.metric_type)
+            
+            item          = {"metric"       : signal.name,
+                             "score"        : f"{signal.score * 100:.1f}%",
+                             "status"       : signal.status.value.upper(),
+                             "confidence"   : f"{metric_result.confidence * 100:.1f}%" if metric_result.confidence else "N/A",
+                             "explanation"  : signal.explanation,
+                             "key_findings" : self.extract_key_findings(metric_type   = signal.metric_type,
+                                                                        metric_result = metric_result,
+                                                                       ),
+                            }
+            
+            breakdown.append(item)
+        
+        return breakdown
+
+
+    def _create_forensic_details(self, analysis_result: AnalysisResult) -> Dict:
+        """
+        Create forensic details section
+        """
+        forensic = dict()
+        
+        for metric_type, metric_result in analysis_result.metric_results.items():
+            metric_name           = self.metric_display_names.get(metric_type, metric_type.value)
+            forensic[metric_name] = metric_result.details or {"note": "No detailed forensics available"}
+        
+        return forensic
+
+
+    def _create_recommendations(self, analysis_result: AnalysisResult) -> Dict:
+        """
+        Create recommendations section
+        """
+        score = analysis_result.overall_score
+        
+        if (score >= 0.85):
+            return {"action"      : "Immediate manual verification required",
+                    "priority"    : "HIGH",
+                    "next_steps"  : ["Forensic analysis", "Reverse image search", "Metadata inspection", "Expert review"],
+                    "confidence"  : "Very high likelihood of AI generation",
+                   }
+        
+        elif (score >= 0.70):
+            return {"action"      : "Manual verification recommended",
+                    "priority"    : "MEDIUM",
+                    "next_steps"  : ["Visual inspection", "Compare with authentic samples", "Check source provenance"],
+                    "confidence"  : "High likelihood of AI generation",
+                   }
+        
+        elif (score >= 0.50):
+            return {"action"      : "Optional review suggested",
+                    "priority"    : "LOW",
+                    "next_steps"  : ["May be edited photo", "Verify image source", "Check for inconsistencies"],
+                    "confidence"  : "Moderate indicators present",
+                   }
+        
+        else:
+            return {"action"      : "No immediate action required",
+                    "priority"    : "NONE",
+                    "next_steps"  : ["Proceed with normal workflow"],
+                    "confidence"  : "Low likelihood of AI generation",
+                   }
+
+
+    def _score_to_status(self, score: float) -> str:
+        """
+        Convert score to status label
+        """
+        if (score >= self.signal_thresholds[SignalStatus.FLAGGED]):
+            return "FLAGGED"
+
+        elif (score >= self.signal_thresholds[SignalStatus.WARNING]):
+            return "WARNING"
+        
+        else:
+            return "PASSED"
+
+
+    def _calculate_avg_confidence(self, analysis_result: AnalysisResult) -> float:
+        """
+        Calculate average confidence across all metrics
+        """
+        confidences = [mr.confidence for mr in analysis_result.metric_results.values() if mr.confidence is not None]
+        
+        return round(sum(confidences) / len(confidences), 3) if confidences else 0.0
+
+
+    def _calculate_risk_level(self, score: float) -> str:
+        """
+        Calculate risk level from score
+        """
+        if (score >= 0.85):
+            return "CRITICAL"
+
+        elif (score >= 0.70):
+            return "HIGH"
+        
+        elif (score >= 0.50):
+            return "MEDIUM"
+        
+        else:
+            return "LOW"
+
+
+    def extract_key_findings(self, metric_type: MetricType, metric_result) -> List[str]:
+        """
+        Extract human-readable key forensic findings for a given metric used by:
+        - Detailed UI views
+        - CSV reports
+        - JSON reports
+        """
+        findings = list()
+        details  = metric_result.details or {}
+        
+        if (metric_type == MetricType.GRADIENT):
+            eig_ratio = details.get('eigenvalue_ratio')
+            
+            if eig_ratio:
+                findings.append(f"Eigenvalue ratio: {eig_ratio:.3f}")
+            
+            vectors = details.get('gradient_vectors_sampled')
+            
+            if vectors:
+                findings.append(f"Analyzed {vectors} gradient vectors")
+        
+        elif (metric_type == MetricType.FREQUENCY):
+            hf_ratio = details.get('hf_ratio')
+            
+            if hf_ratio:
+                findings.append(f"High-frequency ratio: {hf_ratio:.3f}")
+            
+            roughness = details.get('roughness')
+            if roughness:
+                findings.append(f"Spectral roughness: {roughness:.3f}")
+        
+        elif (metric_type == MetricType.NOISE):
+            mean_noise = details.get('mean_noise')
+            
+            if mean_noise:
+                findings.append(f"Mean noise level: {mean_noise:.2f}")
+            
+            cv = details.get('cv')
+            
+            if cv:
+                findings.append(f"Coefficient of variation: {cv:.3f}")
+        
+        elif (metric_type == MetricType.TEXTURE):
+            smooth_ratio = details.get('smooth_ratio')
+            
+            if smooth_ratio:
+                findings.append(f"Smooth patches: {smooth_ratio:.1%}")
+            
+            contrast_mean = details.get('contrast_mean')
+            
+            if contrast_mean:
+                findings.append(f"Average contrast: {contrast_mean:.2f}")
+        
+        elif (metric_type == MetricType.COLOR):
+            sat_stats = details.get('saturation_stats', {})
+            mean_sat  = sat_stats.get('mean_saturation')
+            
+            if mean_sat:
+                findings.append(f"Mean saturation: {mean_sat:.2f}")
+            
+            high_sat = sat_stats.get('high_sat_ratio')
+            
+            if high_sat:
+                findings.append(f"High saturation pixels: {high_sat:.1%}")
+        
+        return findings if findings else ["Analysis complete"]

features/threshold_manager.py

@@ -0,0 +1,277 @@
+# Dependencies
+from typing import Dict
+from utils.logger import get_logger
+from config.settings import settings
+from config.constants import MetricType
+from config.constants import SignalStatus
+from config.constants import SIGNAL_THRESHOLDS
+
+
+# Setup Logging
+logger = get_logger(__name__)
+
+
+class ThresholdManager:
+    """
+    Manage detection thresholds dynamically
+    
+    Purpose:
+    --------
+    Allows runtime adjustment of detection thresholds for:
+    - A/B testing different sensitivity levels
+    - Calibration based on real-world performance
+    - Custom thresholds for specific use cases
+    - Environment-specific tuning (production vs staging)
+    
+    Note: Changes are runtime-only and not persisted
+    """
+    def __init__(self):
+        """
+        Initialize Threshold Manager with current settings
+        """
+        self._review_threshold  = settings.REVIEW_THRESHOLD
+        self._signal_thresholds = dict(SIGNAL_THRESHOLDS)
+        self._metric_weights    = dict(settings.get_metric_weights())
+        
+        logger.info(f"ThresholdManager initialized: review_threshold={self._review_threshold}")
+    
+
+    def get_review_threshold(self) -> float:
+        """
+        Get current review threshold
+        
+        Returns:
+        --------
+            { float } : Current threshold [0.0, 1.0]
+        """
+        return self._review_threshold
+    
+
+    def set_review_threshold(self, new_threshold: float) -> bool:
+        """
+        Set new review threshold
+        
+        Arguments:
+        ----------
+            new_threshold { float } : New threshold value [0.0, 1.0]
+        
+        Returns:
+        --------
+            { bool }                : Success status
+        """
+        if not (0.0 <= new_threshold <= 1.0):
+            logger.error(f"Invalid threshold: {new_threshold} (must be between 0.0 and 1.0)")
+            return False
+        
+        old_threshold          = self._review_threshold
+        self._review_threshold = new_threshold
+        
+        logger.info(f"Review threshold changed: {old_threshold:.2f} → {new_threshold:.2f}")
+        
+        return True
+    
+
+    def adjust_sensitivity(self, sensitivity: str) -> bool:
+        """
+        Adjust sensitivity using preset levels
+        
+        Arguments:
+        ----------
+            sensitivity { str } : One of 'conservative', 'balanced', 'aggressive'
+        
+        Returns:
+        --------
+            { bool }            : Success status
+        """
+        presets = {'conservative' : 0.75,  # Fewer false positives, may miss some AI
+                   'balanced'     : 0.65,  # Recommended default
+                   'aggressive'   : 0.55,  # Catch more AI, more false positives
+                  }
+        
+        if (sensitivity not in presets):
+            logger.error(f"Invalid sensitivity: {sensitivity}. Must be one of {list(presets.keys())}")
+            return False
+        
+        new_threshold = presets[sensitivity]
+        success       = self.set_review_threshold(new_threshold = new_threshold)
+        
+        if success:
+            logger.info(f"Sensitivity set to '{sensitivity}' (threshold={new_threshold})")
+        
+        return success
+    
+
+    def get_signal_thresholds(self) -> Dict[SignalStatus, float]:
+        """
+        Get current signal thresholds
+        
+        Returns:
+        --------
+            { dict } : Signal status → threshold mapping
+        """
+        return self._signal_thresholds.copy()
+    
+
+    def set_signal_threshold(self, status: SignalStatus, threshold: float) -> bool:
+        """
+        Set threshold for specific signal status
+        
+        Arguments:
+        ----------
+            status    { SignalStatus } : Signal status to modify
+            
+            threshold { float }        : New threshold [0.0, 1.0]
+        
+        Returns:
+        --------
+            { bool }                   : Success status
+        """
+        if not (0.0 <= threshold <= 1.0):
+            logger.error(f"Invalid threshold: {threshold}")
+            return False
+        
+        old_threshold                    = self._signal_thresholds.get(status)
+        self._signal_thresholds[status]  = threshold
+        
+        logger.info(f"Signal threshold for {status.value}: {old_threshold:.2f} → {threshold:.2f}")
+        
+        return True
+    
+
+    def get_metric_weights(self) -> Dict[MetricType, float]:
+        """
+        Get current metric weights
+        
+        Returns:
+        --------
+            { dict } : Metric type → weight mapping
+        """
+        return self._metric_weights.copy()
+    
+
+    def set_metric_weight(self, metric: MetricType, weight: float) -> bool:
+        """
+        Set weight for specific metric
+        
+        Arguments:
+        ----------
+            metric { MetricType } : Metric to modify
+            
+            weight   { float }    : New weight [0.0, 1.0]
+        
+        Returns:
+        --------
+            { bool }              : Success status
+        """
+        if not (0.0 <= weight <= 1.0):
+            logger.error(f"Invalid weight: {weight}")
+            return False
+        
+        old_weight                   = self._metric_weights.get(metric, 0.0)
+        self._metric_weights[metric] = weight
+        
+        # Validate total weight
+        total_weight                 = sum(self._metric_weights.values())
+        
+        if not (0.99 <= total_weight <= 1.01):
+            logger.warning(f"Total metric weights = {total_weight:.3f} (should sum to 1.0)")
+        
+        logger.info(f"Metric weight for {metric.value}: {old_weight:.2f} → {weight:.2f}")
+        
+        return True
+    
+
+    def set_all_metric_weights(self, weights: Dict[MetricType, float]) -> bool:
+        """
+        Set all metric weights at once (ensures sum = 1.0)
+        
+        Arguments:
+        ----------
+            weights { dict } : Complete metric weights mapping
+        
+        Returns:
+        --------
+            { bool }         : Success status
+        """
+        # Validate input
+        if (not all(0.0 <= w <= 1.0 for w in weights.values())):
+            logger.error("All weights must be between 0.0 and 1.0")
+            return False
+        
+        total_weight         = sum(weights.values())
+        
+        if not (0.99 <= total_weight <= 1.01):
+            logger.error(f"Weights must sum to 1.0, got {total_weight:.3f}")
+            return False
+        
+        self._metric_weights = dict(weights)
+        
+        logger.info(f"All metric weights updated: {self._metric_weights}")
+        
+        return True
+    
+
+    def get_recommendations(self, score: float) -> Dict[str, str]:
+        """
+        Get action recommendations based on score
+        
+        Arguments:
+        ----------
+            score { float } : Overall suspicion score [0.0, 1.0]
+        
+        Returns:
+        --------
+            { dict }        : Recommendation details
+        """
+        if (score >= 0.85):
+            return {"priority"   : "HIGH",
+                    "action"     : "Immediate manual verification recommended",
+                    "confidence" : "Very high likelihood of AI generation",
+                    "next_steps" : "Forensic analysis, reverse image search, metadata inspection",
+                   }
+        
+        elif (score >= 0.70):
+            return {"priority"   : "MEDIUM",
+                    "action"     : "Manual verification recommended",
+                    "confidence" : "High likelihood of AI generation",
+                    "next_steps" : "Visual inspection, compare with similar authentic images",
+                   }
+        
+        elif (score >= 0.50):
+            return {"priority"   : "LOW",
+                    "action"     : "Optional review",
+                    "confidence" : "Moderate indicators of AI generation",
+                    "next_steps" : "May be heavily edited real photo, check source",
+                   }
+        
+        else:
+            return {"priority"   : "NONE",
+                    "action"     : "No immediate action needed",
+                    "confidence" : "Low likelihood of AI generation",
+                    "next_steps" : "Likely authentic, proceed normally",
+                   }
+    
+
+    def get_current_config(self) -> Dict[str, object]:
+        """
+        Get complete current configuration
+        
+        Returns:
+        --------
+            { dict } : All current threshold and weight settings
+        """
+        return {"review_threshold"  : self._review_threshold,
+                "signal_thresholds" : self._signal_thresholds.copy(),
+                "metric_weights"    : self._metric_weights.copy(),
+               }
+    
+
+    def reset_to_defaults(self) -> None:
+        """
+        Reset all thresholds to default settings
+        """
+        self._review_threshold  = settings.REVIEW_THRESHOLD
+        self._signal_thresholds = dict(SIGNAL_THRESHOLDS)
+        self._metric_weights    = dict(settings.get_metric_weights())
+        
+        logger.info("All thresholds reset to default values")

metrics/aggregator.py

@@ -0,0 +1,288 @@
+# Dependencies
+import time
+import numpy as np
+from typing import List
+from pathlib import Path
+from types import MappingProxyType
+from utils.logger import get_logger
+from config.settings import settings
+from config.schemas import MetricResult
+from config.constants import MetricType
+from config.constants import SignalStatus
+from config.schemas import AnalysisResult
+from config.schemas import DetectionSignal
+from config.constants import DetectionStatus
+from config.constants import SIGNAL_THRESHOLDS
+from utils.image_processor import ImageProcessor
+from config.constants import METRIC_EXPLANATIONS
+from metrics.noise_analyzer import NoiseAnalyzer
+from metrics.color_analyzer import ColorAnalyzer
+from metrics.texture_analyzer import TextureAnalyzer
+from features.threshold_manager import ThresholdManager
+from config.constants import IMAGE_RESIZE_MAX_DIMENSION
+from metrics.frequency_analyzer import FrequencyAnalyzer
+from metrics.gradient_field_pca import GradientFieldPCADetector
+
+# Suppress NumPy warning 
+np.seterr(divide  = 'ignore', 
+          invalid = 'ignore',
+         )
+
+         
+# Setup Logging
+logger = get_logger(__name__)
+
+
+class MetricsAggregator:
+    """
+    Main detector that orchestrates all detection methods
+
+    Combines multiple unsupervised metrics:
+    ----------------------------------------
+    1. Gradient-Field PCA
+    2. Frequency Domain Analysis (FFT)
+    3. Noise Pattern Analysis
+    4. Texture Analysis
+    5. Color Distribution Analysis
+
+    Note: Each metric produces a suspicion score [0.0, 1.0] : scores are combined using weighted average to produce final assessment
+    """
+    def __init__(self, threshold_manager: ThresholdManager | None = None):
+        """
+        Initialize all detectors
+        """
+        logger.info("Initializing AI Image Detector")
+
+        # Optional runtime threshold manager
+        self.threshold_manager           = threshold_manager
+        
+        self.gradient_field_pca_detector = GradientFieldPCADetector()
+        self.frequency_analyzer          = FrequencyAnalyzer()
+        self.noise_analyzer              = NoiseAnalyzer()
+        self.texture_analyzer            = TextureAnalyzer()
+        self.color_analyzer              = ColorAnalyzer()
+        self.image_processor             = ImageProcessor()
+
+        # Create detector registry
+        self.detector_registry          = MappingProxyType({MetricType.GRADIENT  : ("Gradient Field PCA", self.gradient_field_pca_detector),
+                                                            MetricType.FREQUENCY : ("Frequency Analysis", self.frequency_analyzer),
+                                                            MetricType.NOISE     : ("Noise Analysis", self.noise_analyzer),
+                                                            MetricType.TEXTURE   : ("Texture Analysis", self.texture_analyzer),
+                                                            MetricType.COLOR     : ("Color Analysis", self.color_analyzer),
+                                                          })
+        
+        # Get metric weights either from runtime UI or default to settings
+        self.weights                    = (self.threshold_manager.get_metric_weights() if self.threshold_manager else settings.get_metric_weights())
+        
+        logger.info(f"Metric weights: {self.weights}")
+
+
+    def analyze_image(self, image_path: Path, filename: str, image_size: tuple) -> AnalysisResult:
+        """
+        Analyze single image for AI generation
+        
+        Arguments:
+        ----------
+            image_path { Path }  : Path to image file
+            
+            filename   { str }   : Original filename
+            
+            image_size { tuple } : (width, height) tuple
+        
+        Returns:
+        --------
+            { AnalysisResult }   : AnalysisResult with detection outcome
+        """
+        logger.info(f"Analyzing image: {filename}")
+        
+        start_time = time.time()
+        
+        try:
+            # Load image
+            image           = self.image_processor.load_image(file_path = image_path)
+            
+            # Resize if needed for performance
+            image           = self.image_processor.resize_if_needed(image         = image, 
+                                                                    max_dimension = IMAGE_RESIZE_MAX_DIMENSION,
+                                                                   )
+            
+            # Run all detectors and get raw scores
+            metric_results  = self._run_all_detectors(image = image)
+            
+            # Create signals from scores (aggregator's responsibility)
+            signals         = self._create_signals_from_scores(metric_results = metric_results)
+            
+            # Aggregate results
+            overall_score   = self._aggregate_scores(metric_results = metric_results)
+            
+            # Determine status
+            status          = self._determine_status(overall_score = overall_score)
+            
+            # Calculate processing time
+            processing_time = time.time() - start_time
+            
+            # Create result
+            result          = AnalysisResult(filename        = filename,
+                                             overall_score   = overall_score,
+                                             status          = status,
+                                             confidence      = int(overall_score * 100),
+                                             signals         = signals,
+                                             metric_results  = metric_results,
+                                             processing_time = processing_time,
+                                             image_size      = image_size,
+                                            )
+            
+            logger.info(f"Analysis complete for {filename}: status={status.value}, score={overall_score:.3f}, time={processing_time:.2f}s")
+            
+            return result
+            
+        except Exception as e:
+            logger.error(f"Analysis failed for {filename}: {e}")
+            raise
+
+
+    def _run_all_detectors(self, image: np.ndarray) -> dict[MetricType, MetricResult]:
+        """
+        Run all detection methods and collect raw scores
+        
+        Arguments:
+        ----------
+            image { np.ndarray } : RGB image array
+        
+        Returns:
+        --------
+                  { dict }       : Dictionary mapping MetricType to MetricResult
+        """
+        metric_results = dict()
+        
+        # Run eaach detector one by one
+        for metric_type, (detector_name, detector) in self.detector_registry.items():
+            try:
+                result                      = detector.detect(image = image)
+                result.metric_type          = metric_type
+                metric_results[metric_type] = result
+                
+                logger.debug(f"{detector_name} | {metric_type.value} | score={result.score:.3f} | confidence={result.confidence:.3f}")
+                
+            except Exception as e:
+                logger.error(f"{detector.__class__.__name__} failed: {e}")
+
+                # Same Failure Score by all metrics with same confidence 
+                metric_results[metric_type] = MetricResult(metric_type = metric_type,
+                                                           score       = settings.REVIEW_THRESHOLD,
+                                                           confidence  = 0.0,
+                                                           details     = {"error": "detector_failed"},
+                                                          )
+        
+        return metric_results
+
+
+    def _create_signals_from_scores(self, metric_results: dict) -> List[DetectionSignal]:
+        """
+        Convert MetricResults to DetectionSignals with status and explanations
+        
+        This is the aggregator's responsibility - metrics don't know about signals
+        
+        Arguments:
+        ----------
+            metric_results { dict }   : Dictionary mapping MetricType to float score
+        
+        Returns:
+        --------
+                    { list }          : List of complete detection signals
+        """
+        signals           = list()
+
+        signal_thresholds = (self.threshold_manager.get_signal_thresholds() if self.threshold_manager else SIGNAL_THRESHOLDS)
+        
+        for metric_type, result in metric_results.items():
+            # Extract score of the metric
+            score = result.score
+
+            # Determine status based on thresholds
+            if (score >= signal_thresholds[SignalStatus.FLAGGED]):
+                status   = SignalStatus.FLAGGED
+                severity = 'high'
+                
+            elif (score >= signal_thresholds[SignalStatus.WARNING]):
+                status   = SignalStatus.WARNING
+                severity = 'moderate'
+                
+            else:
+                status   = SignalStatus.PASSED
+                severity = 'normal'
+            
+            # Get explanation from constants
+            explanation = METRIC_EXPLANATIONS[metric_type][severity]
+            
+            # Create signal
+            signal      = DetectionSignal(name        = self.detector_registry[metric_type][0],
+                                          metric_type = metric_type,
+                                          score       = score,
+                                          status      = status,
+                                          explanation = explanation,
+                                         )
+            
+            signals.append(signal)
+        
+        # Sort signals by score (highest first)
+        signals.sort(key = lambda s: s.score, reverse = True)
+        
+        return signals
+
+
+    def _aggregate_scores(self, metric_results: dict) -> float:
+        """
+        Aggregate individual metric scores using weighted average
+        
+        Arguments:
+        ----------
+            metric_results { dict } : Dictionary mapping MetricType to float score
+        
+        Returns:
+        --------
+                { float }           : Overall suspicion score [0.0, 1.0]
+        """
+        total_score  = 0.0
+        total_weight = 0.0
+        
+        for metric_type, result in metric_results.items():
+            weight        = self.weights.get(metric_type, 0.0)
+            total_score  += result.score * weight
+            total_weight += weight
+        
+        
+        # Get Aggregated Score
+        if (total_weight > 0):
+            # Normalize
+            overall_score = total_score / total_weight
+        
+        else:
+            # Neutral if no valid weights
+            overall_score = 0.5  
+        
+        logger.debug(f"Aggregated score: {overall_score:.3f}")
+        
+        return float(np.clip(overall_score, 0.0, 1.0))
+
+
+    def _determine_status(self, overall_score: float) -> DetectionStatus:
+        """
+        Determine binary status from overall score
+        
+        Arguments:
+        ----------
+            overall_score { float } : Aggregated suspicion score
+        
+        Returns:
+        --------
+            { DetectionStatus }     : LIKELY_AUTHENTIC or REVIEW_REQUIRED
+        """
+        # Extract review threshold either from threshold_manager or deault to settings value
+        review_threshold = (self.threshold_manager.get_review_threshold() if self.threshold_manager else settings.REVIEW_THRESHOLD)
+
+        if (overall_score >= review_threshold):
+            return DetectionStatus.REVIEW_REQUIRED
+        
+        else:
+            return DetectionStatus.LIKELY_AUTHENTIC

metrics/color_analyzer.py

@@ -0,0 +1,352 @@
+# Dependencies
+import numpy as np
+from utils.logger import get_logger
+from config.schemas import MetricResult
+from config.constants import MetricType
+from utils.image_processor import ImageProcessor
+from config.constants import COLOR_ANALYSIS_PARAMS
+
+# Suppress NumPy warning 
+np.seterr(divide  = 'ignore', 
+          invalid = 'ignore',
+         )
+
+
+# Setup Logging
+logger = get_logger(__name__)
+
+
+class ColorAnalyzer:
+    """
+    Color distribution analysis for AI detection
+
+    Core principle:
+    ---------------
+    - Real photos : Natural color distributions constrained by physics
+    - AI images   : Can create unnatural saturation, hue shifts, or impossible color relationships
+
+    Method:
+    -------
+    1. Convert to multiple color spaces (RGB, HSV)
+    2. Analyze color histogram distributions
+    3. Check for oversaturation
+    4. Detect unnatural color relationships
+    """
+    def __init__(self):
+        self.image_processor = ImageProcessor()
+
+
+    def detect(self, image: np.ndarray) -> MetricResult:
+        """
+        Run color distribution analysis
+        
+        Arguments:
+        ----------
+            image { np.ndarray } : RGB image array (H, W, 3)
+        
+        Returns:
+        --------
+            { MetricResult }     : Structured Color-domain metric result containing:
+                                   - score      : Suspicion score [0.0, 1.0]
+                                   - confidence : Reliability of color analysis evidence
+                                   - details    : Color Analysis forensics and statistics
+        """
+        try:
+            logger.debug(f"Running color analysis on image shape {image.shape}")
+            
+            # Normalize image to [0, 1]
+            image_norm                           = self.image_processor.normalize_image(image = image)
+            
+            # Convert to HSV
+            hsv                                  = self._rgb_to_hsv(rgb = image_norm)
+            
+            # Analyze saturation
+            saturation_score, saturation_details = self._analyze_saturation(hsv = hsv)
+            
+            # Analyze color histogram
+            histogram_score, histogram_details   = self._analyze_color_histogram(rgb = image_norm)
+            
+            # Analyze hue distribution
+            hue_score, hue_details               = self._analyze_hue_distribution(hsv = hsv)
+            
+            # Combine scores
+            weights                              = COLOR_ANALYSIS_PARAMS.MAIN_WEIGHTS
+            final_score                          = (weights['saturation'] * saturation_score + weights['histogram'] * histogram_score + weights['hue'] * hue_score)
+
+            # Calculate Confidence
+            confidence                           = float(np.clip((abs(final_score - COLOR_ANALYSIS_PARAMS.NEUTRAL_SCORE) * 2.0), 0.0, 1.0))
+            
+            logger.debug(f"Color analysis: saturation={saturation_score:.3f}, histogram={histogram_score:.3f}, hue={hue_score:.3f}, Score={final_score:.3f}")
+            
+            return MetricResult(metric_type = MetricType.COLOR,
+                                score       = float(final_score),
+                                confidence  = confidence,
+                                details     = {"saturation_stats" : saturation_details,
+                                               "histogram_stats"  : histogram_details,
+                                               "hue_stats"        : hue_details,
+                                              },
+                               )
+            
+        except Exception as e:
+            logger.error(f"Color analysis failed: {e}")
+
+            # Return neutral score on error
+            return MetricResult(metric_type = MetricType.COLOR,
+                                score       = COLOR_ANALYSIS_PARAMS.NEUTRAL_SCORE,
+                                confidence  = 0.0,
+                                details     = {"error": "color_analysis_failed"},
+                               )
+
+
+    def _rgb_to_hsv(self, rgb: np.ndarray) -> np.ndarray:
+        """
+        Convert RGB to HSV color space
+        
+        Arguments:
+        ----------
+            rgb { np.ndarray } : RGB image normalized to [0, 1]
+        
+        Returns:
+        --------
+            { np.ndarray }     : HSV image (H in [0, 360], S and V in [0, 1])
+        """
+        r, g, b = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
+        
+        maxc    = np.maximum(np.maximum(r, g), b)
+        minc    = np.minimum(np.minimum(r, g), b)
+        delta   = maxc - minc
+        
+        # Value
+        v       = maxc
+        
+        # Saturation
+        s       = np.where(maxc != 0, delta / maxc, 0)
+        
+        # Hue
+        h       = np.zeros_like(maxc)
+        
+        # Red is max
+        mask    = (maxc == r) & (delta != 0)
+        h[mask] = 60 * (((g[mask] - b[mask]) / delta[mask]) % 6)
+        
+        # Green is max
+        mask    = (maxc == g) & (delta != 0)
+        h[mask] = 60 * (((b[mask] - r[mask]) / delta[mask]) + 2)
+        
+        # Blue is max
+        mask    = (maxc == b) & (delta != 0)
+        h[mask] = 60 * (((r[mask] - g[mask]) / delta[mask]) + 4)
+        
+        hsv     = np.stack([h, s, v], axis = 2)
+        
+        return hsv
+
+
+    def _analyze_saturation(self, hsv: np.ndarray) -> tuple[float, dict]:
+        """
+        Analyze saturation distribution for anomalies
+        
+        Real photos: Most pixels have moderate saturation (0.2-0.7)
+        AI images: Can have too many highly saturated pixels (>0.8)
+        
+        Arguments:
+        ----------
+            hsv { np.ndarray } : HSV image
+        
+        Returns:
+        --------
+            { tuple }          : A tuple containing:
+                                 - Suspicion score [0.0, 1.0]
+                                 - Saturation Stats
+        """
+        saturation          = hsv[:, :, 1]
+        
+        if (np.mean(saturation) < 0.05):
+            logger.debug("Low global saturation; skipping saturation analysis")
+            return COLOR_ANALYSIS_PARAMS.NEUTRAL_SCORE, {"reason": "insufficient_color_information"}
+
+        # Compute saturation statistics
+        mean_sat            = np.mean(saturation)
+        high_sat_ratio      = np.mean(saturation > COLOR_ANALYSIS_PARAMS.SAT_HIGH_THRESHOLD)
+        very_high_sat_ratio = np.mean(saturation > COLOR_ANALYSIS_PARAMS.SAT_VERY_HIGH_THRESHOLD)
+        
+        # Overall saturation level Analysis
+        mean_anomaly        = 0.0
+        
+        if (mean_sat > COLOR_ANALYSIS_PARAMS.SAT_MEAN_THRESHOLD):
+            mean_anomaly = min(1.0, (mean_sat - COLOR_ANALYSIS_PARAMS.SAT_MEAN_THRESHOLD) * COLOR_ANALYSIS_PARAMS.SAT_MEAN_SCALE)
+        
+        # High saturation pixels Analysis
+        high_sat_anomaly = 0.0
+        
+        if (high_sat_ratio > COLOR_ANALYSIS_PARAMS.HIGH_SAT_RATIO_THRESHOLD):
+            high_sat_anomaly = min(1.0, (high_sat_ratio - COLOR_ANALYSIS_PARAMS.HIGH_SAT_RATIO_THRESHOLD) * COLOR_ANALYSIS_PARAMS.HIGH_SAT_SCALE)
+
+        # Very high saturation Analysis (clipping)
+        clip_anomaly = 0.0
+        
+        if (very_high_sat_ratio > COLOR_ANALYSIS_PARAMS.CLIP_RATIO_THRESHOLD):
+            clip_anomaly = min(1.0, (very_high_sat_ratio - COLOR_ANALYSIS_PARAMS.CLIP_RATIO_THRESHOLD) * COLOR_ANALYSIS_PARAMS.CLIP_SCALE)
+
+        # Combine Scores
+        weights          = COLOR_ANALYSIS_PARAMS.SAT_SUBMETRIC_WEIGHTS
+
+        color_score      = (weights['mean_anomaly'] * mean_anomaly + weights['high_sat_anomaly'] * high_sat_anomaly + weights['clip_anomaly'] * clip_anomaly)
+
+        final_score      = float(np.clip(color_score, 0.0, 1.0))
+
+        saturation_stats = {"mean_saturation"     : float(mean_sat),
+                            "high_sat_ratio"      : float(high_sat_ratio),
+                            "very_high_sat_ratio" : float(very_high_sat_ratio),
+                            "mean_anomaly"        : float(mean_anomaly),
+                            "high_sat_anomaly"    : float(high_sat_anomaly),
+                            "clip_anomaly"        : float(clip_anomaly),
+                           }
+
+        logger.debug(f"Saturation - mean: {mean_sat:.3f}, high_ratio: {high_sat_ratio:.3f}, clip_ratio: {very_high_sat_ratio:.3f}")
+        
+        return final_score, saturation_stats
+
+
+    def _analyze_color_histogram(self, rgb: np.ndarray) -> tuple[float, dict]:
+        """
+        Analyze RGB histogram distributions for anomalies
+        
+        Arguments:
+        ----------
+            rgb { np.ndarray } : RGB image normalized to [0, 1]
+        
+        Returns:
+        --------
+            { tuple }          : A tuple containing:
+                                 - Suspicion score [0.0, 1.0]
+                                 - Histogram Analysis stats
+        """
+        anomalies      = list()
+        roughness_vals = list()
+        low_clip_vals  = list()
+        high_clip_vals = list()
+        
+        for channel_idx, channel_name in enumerate(['R', 'G', 'B']):
+            channel = rgb[:, :, channel_idx]
+            
+            # Compute histogram
+            hist, bins = np.histogram(channel, 
+                                      bins  = COLOR_ANALYSIS_PARAMS.HISTOGRAM_BINS, 
+                                      range = COLOR_ANALYSIS_PARAMS.HISTOGRAM_RANGE,
+                                     )
+
+            hist       = hist / (np.sum(hist) + 1e-10)
+            
+            # Measure histogram roughness
+            hist_diff  = np.abs(np.diff(hist))
+            roughness  = np.mean(hist_diff)
+            roughness_vals.append(roughness)
+
+            # High roughness = suspicious
+            if (roughness > COLOR_ANALYSIS_PARAMS.ROUGHNESS_THRESHOLD):
+                anomalies.append(np.clip(((roughness - COLOR_ANALYSIS_PARAMS.ROUGHNESS_THRESHOLD) * COLOR_ANALYSIS_PARAMS.ROUGHNESS_SCALE), 0.0, 1.0))
+            
+            # Check for clipping (peaks at extremes)
+            low_clip  = hist[0] + hist[1]
+            high_clip = hist[-1] + hist[-2]
+
+            # Append values to their respective storages
+            low_clip_vals.append(low_clip)
+            high_clip_vals.append(high_clip)
+            
+            if (low_clip > COLOR_ANALYSIS_PARAMS.CLIP_THRESHOLD):
+                # More than 10% near black
+                anomalies.append(min(1.0, (low_clip - COLOR_ANALYSIS_PARAMS.CLIP_THRESHOLD) * COLOR_ANALYSIS_PARAMS.CLIP_SCALE_FACTOR))
+
+            if (high_clip > COLOR_ANALYSIS_PARAMS.CLIP_THRESHOLD):
+                # More than 10% near white
+                anomalies.append(min(1.0, (high_clip - COLOR_ANALYSIS_PARAMS.CLIP_THRESHOLD) * COLOR_ANALYSIS_PARAMS.CLIP_SCALE_FACTOR))
+
+        if (len(anomalies) == 0):
+            logger.debug("No color histogram anomalies detected")
+            return COLOR_ANALYSIS_PARAMS.NEUTRAL_SCORE, {"reason": "insufficient_color_information"}
+        
+        # Take mean of detected anomalies
+        score           = np.mean(anomalies)
+        final_score     = float(np.clip(score, 0.0, 1.0))
+
+        histogram_stats = {"roughness_mean"    : float(np.mean(roughness_vals)),
+                           "low_clip_mean"     : float(np.mean(low_clip_vals)),
+                           "high_clip_mean"    : float(np.mean(high_clip_vals)),
+                           "channels_analyzed" : 3,
+                          }
+        
+        return final_score, histogram_stats
+
+
+    def _analyze_hue_distribution(self, hsv: np.ndarray) -> tuple[float, dict]:
+        """
+        Analyze hue distribution for unnatural patterns
+        
+        Arguments:
+        ----------
+            hsv { np.ndarray } : HSV image
+        
+        Returns:
+        --------
+            { tuple }          : A tuple containing:
+                                 - Suspicion score [0.0, 1.0]
+                                 - hue analysis stats
+        """
+        hue            = hsv[:, :, 0]
+        saturation     = hsv[:, :, 1]
+        
+        # Only consider pixels with sufficient saturation (avoid gray)
+        saturated_mask = saturation > COLOR_ANALYSIS_PARAMS.HUE_SAT_MASK_THRESHOLD
+        
+        if (np.sum(saturated_mask) < COLOR_ANALYSIS_PARAMS.HUE_MIN_PIXELS):
+            # Not enough colored pixels to analyze
+            return COLOR_ANALYSIS_PARAMS.NEUTRAL_SCORE, {"reason": "insufficient_color_information"}
+        
+        hue_saturated         = hue[saturated_mask]
+
+        # Prevents false positives on monotone objects
+        if (np.ptp(hue_saturated) < 5.0):
+            logger.debug("Hue range too narrow; returning neutral score")
+            return COLOR_ANALYSIS_PARAMS.NEUTRAL_SCORE, {"reason": "insufficient_color_information"}
+        
+        # Compute hue histogram
+        hist, bins            = np.histogram(a     = hue_saturated, 
+                                             bins  = COLOR_ANALYSIS_PARAMS.HUE_BINS, 
+                                             range = COLOR_ANALYSIS_PARAMS.HUE_RANGE,
+                                            )
+
+        hist                  = hist / (np.sum(hist) + 1e-10)
+        
+        # Unnatural hue concentration Analysis
+        sorted_hist           = np.sort(hist)[::-1]
+        top3_concentration    = np.sum(sorted_hist[:3])
+        concentration_anomaly = 0.0
+         
+        if (top3_concentration > COLOR_ANALYSIS_PARAMS.HUE_CONCENTRATION_THRESHOLD): 
+            # More than 60% in 3 hue bins
+            concentration_anomaly = min(1.0, (top3_concentration - COLOR_ANALYSIS_PARAMS.HUE_CONCENTRATION_THRESHOLD) * COLOR_ANALYSIS_PARAMS.HUE_CONCENTRATION_SCALE)
+        
+        # Hue gaps Analysis
+        zero_bins             = np.sum(hist < COLOR_ANALYSIS_PARAMS.HUE_EMPTY_BIN_THRESHOLD)
+        gap_ratio             = zero_bins / len(hist)
+        gap_anomaly           = 0.0
+        
+        if (gap_ratio > COLOR_ANALYSIS_PARAMS.HUE_GAP_RATIO_THRESHOLD):  
+            # More than 40% empty bins
+            gap_anomaly = min(1.0, (gap_ratio - COLOR_ANALYSIS_PARAMS.HUE_GAP_RATIO_THRESHOLD) * COLOR_ANALYSIS_PARAMS.HUE_GAP_SCALE)
+        
+        weights               = COLOR_ANALYSIS_PARAMS.HUE_SUBMETRIC_WEIGHTS
+        score                 = (weights['concentration_anomaly'] * concentration_anomaly + weights['gap_anomaly'] * gap_anomaly)
+        final_score           = float(np.clip(score, 0.0, 1.0))
+
+        hue_stats             = {"top3_concentration"    : float(top3_concentration),
+                                 "gap_ratio"             : float(gap_ratio),
+                                 "concentration_anomaly" : float(concentration_anomaly),
+                                 "gap_anomaly"           : float(gap_anomaly),
+                                }
+
+        logger.debug(f"Hue - concentration: {top3_concentration:.3f}, gap_ratio: {gap_ratio:.3f}")
+        
+        return final_score, hue_stats

metrics/frequency_analyzer.py

@@ -0,0 +1,260 @@
+# Dependencies
+import numpy as np
+from scipy import fft
+from utils.logger import get_logger
+from config.schemas import MetricResult
+from config.constants import MetricType
+from utils.image_processor import ImageProcessor
+from config.constants import FREQUENCY_ANALYSIS_PARAMS
+
+# Suppress NumPy warning 
+np.seterr(divide  = 'ignore', 
+          invalid = 'ignore',
+         )
+         
+
+# Setup Logging
+logger = get_logger(__name__)
+
+
+class FrequencyAnalyzer:
+    """
+    FFT-based frequency domain analysis for AI detection
+    
+    Core principle:
+    ---------------
+    - Real photos : Smooth frequency falloff (natural optical blur)
+    - AI images   : Unnatural frequency spikes or gaps (artifacts from generation)
+    
+    Method:
+    -------
+    1. Convert to luminance
+    2. Compute 2D FFT
+    3. Compute radial frequency spectrum
+    4. Analyze high-frequency content and distribution patterns
+    """
+    def __init__(self):
+        self.image_processor = ImageProcessor()
+    
+
+    def detect(self, image: np.ndarray) -> MetricResult:
+        """
+        Run frequency domain analysis
+        
+        Arguments:
+        ----------
+            image { np.ndarray } : RGB image array (H, W, 3)
+        
+        Returns:
+        --------
+            { MetricResult }     : Structured frequency-domain metric result containing:
+                                   - score      : Suspicion score [0.0, 1.0]
+                                   - confidence : Reliability of frequency evidence
+                                   - details    : FFT and spectrum diagnostics
+        """
+        try:
+            logger.debug(f"Running frequency analysis on image shape {image.shape}")
+            
+            # Convert to luminance
+            luminance                   = self.image_processor.rgb_to_luminance(image = image)
+
+            # Normalize luminance (remove DC component for FFT stability)
+            normalized_luminance        = luminance - np.mean(luminance)
+
+            if not np.any(normalized_luminance):
+                logger.debug("FFT skipped: zero-variance luminance")
+                
+                return MetricResult(metric_type = MetricType.FREQUENCY,
+                                    score       = FREQUENCY_ANALYSIS_PARAMS.NEUTRAL_SCORE,
+                                    confidence  = 0.0,
+                                    details     = {"reason": "zero_variance_luminance"}
+                                   )
+            
+            # Compute FFT on normalized_luminance
+            fft_magnitude               = self._compute_fft_magnitude(luminance = normalized_luminance)
+            
+            # Analyze radial frequency spectrum
+            radial_spectrum             = self._compute_radial_spectrum(fft_magnitude = fft_magnitude)
+            
+            # Detect anomalies
+            anomaly_score, freq_details = self._analyze_frequency_anomalies(radial_spectrum = radial_spectrum)
+            
+            logger.debug(f"Frequency analysis: Anomaly Score={anomaly_score:.3f}")
+            
+            # Distance from neutral = stronger evidence = higher confidence
+            confidence                  = float(np.clip((abs(anomaly_score - FREQUENCY_ANALYSIS_PARAMS.NEUTRAL_SCORE) * 2.0), 0.0, 1.0))
+            
+            return MetricResult(metric_type = MetricType.FREQUENCY,
+                                score       = float(anomaly_score),
+                                confidence  = confidence,
+                                details     = {"spectrum_bins" : int(len(radial_spectrum)),
+                                                **freq_details,
+                                              }
+                               )
+            
+        except Exception as e:
+            logger.error(f"Frequency analysis failed: {e}")
+
+            # Return neutral score on error
+            return MetricResult(metric_type = MetricType.FREQUENCY,
+                                score       = FREQUENCY_ANALYSIS_PARAMS.NEUTRAL_SCORE,
+                                confidence  = 0.0,
+                                details     = {"error" : "frequency_analysis_failed"},
+                               )
+    
+
+    def _compute_fft_magnitude(self, luminance: np.ndarray) -> np.ndarray:
+        """
+        Compute 2D FFT magnitude spectrum
+        
+        Arguments:
+        ----------
+            luminance { np.ndarray } : Luminance channel (H, W)
+        
+        Returns:
+        --------
+            { np.ndarray }           : FFT magnitude spectrum (centered)
+        """
+        # Compute 2D FFT
+        f             = fft.fft2(luminance)
+        
+        # Shift zero frequency to center
+        f_shifted     = fft.fftshift(f)
+        
+        # Compute magnitude spectrum
+        magnitude     = np.abs(f_shifted)
+        
+        # Log scale for better visualization
+        magnitude_log = np.log1p(magnitude)
+        
+        return magnitude_log
+    
+
+    def _compute_radial_spectrum(self, fft_magnitude: np.ndarray) -> np.ndarray:
+        """
+        Compute radial average of frequency spectrum
+        
+        Arguments:
+        ----------
+            fft_magnitude { np.ndarray } : FFT magnitude spectrum
+        
+        Returns:
+        --------
+            { np.ndarray }               : Radial spectrum (1D array)
+        """
+        h, w                       = fft_magnitude.shape
+        center_y, center_x         = h // 2, w // 2
+        
+        # Create coordinate grids
+        y, x                       = np.ogrid[:h, :w]
+        
+        # Compute radial distances from center
+        r                          = np.sqrt((x - center_x)**2 + (y - center_y)**2).astype(int)
+        
+        # Maximum radius
+        max_radius                 = min(center_x, center_y)
+        
+        # Compute radial bins
+        bins                       = np.linspace(0, max_radius, FREQUENCY_ANALYSIS_PARAMS.BINS + 1)
+        radial_spectrum            = np.zeros(FREQUENCY_ANALYSIS_PARAMS.BINS)
+        
+        # Average magnitude in each radial bin
+        for i in range(FREQUENCY_ANALYSIS_PARAMS.BINS):
+            mask = (r >= bins[i]) & (r < bins[i + 1])
+            
+            if np.any(mask):
+                radial_spectrum[i] = np.mean(fft_magnitude[mask])
+        
+        return radial_spectrum
+    
+
+    def _analyze_frequency_anomalies(self, radial_spectrum: np.ndarray) -> tuple[float, dict]:
+        """
+        Analyze frequency spectrum for AI generation artifacts
+        
+        Checks:
+        -------
+        1. High-frequency content (AI images often have unnatural HF energy)
+        2. Frequency distribution smoothness
+        3. Spectral slope deviation from natural images
+        
+        Arguments:
+        ----------
+            radial_spectrum { np.ndarray } : Radial frequency spectrum
+        
+        Returns:
+        --------
+                { tuple }                  : A tuple containing
+                                             - Suspicion score [0.0, 1.0], and
+                                             - frequency details in a dictionary
+        """
+        if (len(radial_spectrum) < FREQUENCY_ANALYSIS_PARAMS.MIN_SPECTRUM_SAMPLES):
+            return (FREQUENCY_ANALYSIS_PARAMS.NEUTRAL_SCORE,
+                    {"reason"        : "insufficient_frequency_samples",
+                     "spectrum_bins" : int(len(radial_spectrum)),
+                    }
+                   )
+        
+        # Normalize spectrum
+        spectrum_norm    = radial_spectrum / (np.max(radial_spectrum) + 1e-10)
+        
+        # High-frequency Energy Analysis
+        high_freq_start  = int(len(spectrum_norm) * FREQUENCY_ANALYSIS_PARAMS.HIGH_FREQ_THRESHOLD)
+
+        if (high_freq_start >= len(spectrum_norm) - 1):
+            return (FREQUENCY_ANALYSIS_PARAMS.NEUTRAL_SCORE, 
+                    {"reason" : "invalid_frequency_partition"}
+                   )
+
+        high_freq_energy = np.mean(spectrum_norm[high_freq_start:])
+        low_freq_energy  = np.mean(spectrum_norm[:high_freq_start])
+        
+        hf_ratio         = high_freq_energy / (low_freq_energy + 1e-10)
+        
+        # Natural images : HF ratio typically 0.1-0.3
+        # AI images      : Can be higher (0.3-0.6) or lower (<0.1)
+        hf_anomaly       = 0.0
+        
+        if (hf_ratio > FREQUENCY_ANALYSIS_PARAMS.HF_RATIO_UPPER):
+            hf_anomaly  = min(1.0, (hf_ratio - FREQUENCY_ANALYSIS_PARAMS.HF_RATIO_UPPER) * FREQUENCY_ANALYSIS_PARAMS.HF_UPPER_SCALE)
+        
+        elif (hf_ratio < FREQUENCY_ANALYSIS_PARAMS.HF_RATIO_LOWER):
+            hf_anomaly  = min(1.0, (FREQUENCY_ANALYSIS_PARAMS.HF_RATIO_LOWER - hf_ratio) * FREQUENCY_ANALYSIS_PARAMS.HF_LOWER_SCALE)
+        
+        # Spectral Smoothness Analysis
+        spectral_diff   = np.abs(np.diff(spectrum_norm))
+        roughness       = np.mean(spectral_diff)
+        roughness_score = np.clip(roughness * FREQUENCY_ANALYSIS_PARAMS.ROUGHNESS_SCALE, 0.0, 1.0)
+        
+        # Power Law Deviation Analysis
+        x               = np.arange(1, len(spectrum_norm) + 1)
+        log_spectrum    = np.log(spectrum_norm + 1e-10)
+        log_x           = np.log(x)
+        
+        # Linear fit in log-log space
+        coeffs          = np.polyfit(log_x, log_spectrum, 1)
+        fitted          = np.polyval(coeffs, log_x)
+        deviation       = np.mean(np.abs(log_spectrum - fitted))
+        deviation_score = np.clip(deviation * FREQUENCY_ANALYSIS_PARAMS.DEVIATION_SCALE, 0.0, 1.0)
+        
+        # Combine scores
+        weights         = FREQUENCY_ANALYSIS_PARAMS.SUBMETRIC_WEIGHTS
+
+        combined_score  = (weights['hf_anomaly'] * hf_anomaly + weights['roughness'] * roughness_score + weights['deviation'] * deviation_score)
+
+        final_score     = float(np.clip(combined_score, 0.0, 1.0))
+        
+        frequency_dict  = {"low_freq_energy"     : float(low_freq_energy),
+                           "high_freq_energy"    : float(high_freq_energy),
+                           "hf_ratio"            : float(hf_ratio),
+                           "hf_anomaly"          : float(hf_anomaly),
+                           "roughness"           : float(roughness),
+                           "roughness_score"     : float(roughness_score),
+                           "spectral_deviation"  : float(deviation),
+                           "deviation_score"     : float(deviation_score),
+                           "high_freq_start_bin" : int(high_freq_start),
+                          }
+
+        logger.debug(f"FFT scores - HF anomaly: {hf_anomaly:.3f}, roughness: {roughness_score:.3f}, deviation: {deviation_score:.3f}")
+        
+        return (final_score, frequency_dict)

metrics/gradient_field_pca.py

@@ -0,0 +1,236 @@
+# Dependencies
+import numpy as np
+from utils.logger import get_logger
+from config.schemas import MetricResult
+from config.constants import MetricType
+from utils.image_processor import ImageProcessor
+from config.constants import GRADIENT_FIELD_PCA_PARAMS
+
+# Suppress NumPy warning 
+np.seterr(divide  = 'ignore', 
+          invalid = 'ignore',
+         )
+
+
+# Setup Logging
+logger = get_logger(__name__)
+
+
+class GradientFieldPCADetector:
+    """
+    Detects AI-generated images by analyzing gradient field consistency. Real photos have consistent gradient 
+    patterns shaped by physics (lighting, optics). Diffusion models struggle to maintain physically consistent
+    gradients due to denoising
+    
+    Core principle:
+    ---------------
+    - Real photos : Gradients align with physical light sources (low-dimensional structure)
+    - AI images   : Gradients are inconsistent due to patch-based denoising (high-dimensional)
+    
+    Method:
+    -------
+    1. Convert to luminance
+    2. Compute Sobel gradients (Gx, Gy)
+    3. Flatten to gradient vectors per pixel
+    4. Compute covariance matrix
+    5. PCA eigenvalue analysis
+    """
+    def __init__(self):
+        """
+        Initialize Gradient-Field PCA Detector class
+        """
+        self._range          = np.random.default_rng(seed = GRADIENT_FIELD_PCA_PARAMS.RANDOM_SEED)
+        self.image_processor = ImageProcessor()
+    
+
+    def detect(self, image: np.ndarray) -> MetricResult:
+        """
+        Run gradient PCA detection
+        
+        Arguments:
+        ----------
+            image { np.ndarray } : RGB image array (H, W, 3)
+        
+        Returns:
+        --------
+            { MetricResult }     : Structured metric result containing:
+                                   - score      : Suspicion score [0.0, 1.0] (0 = natural, 1 = suspicious)
+                                   - confidence : Confidence of this metric's assessment [0.0, 1.0]
+                                   - details    : Explainability metadata for UI and reports
+        """
+        try:
+            logger.debug(f"Running gradient PCA detection on image shape {image.shape}")
+            
+            # Convert image to luminance
+            luminance             = self.image_processor.rgb_to_luminance(image = image)
+            
+            # Compute gradients
+            gx, gy                = self.image_processor.compute_gradients(luminance = luminance)
+            
+            # Flatten and sample gradient vectors
+            gradient_vectors      = self._prepare_and_sample_gradients(gx = gx, 
+                                                                       gy = gy,
+                                                                      )
+            
+            # Perform PCA
+            eigenvalue_ratio      = self._compute_eigenvalue_ratio(gradient_vectors = gradient_vectors)
+
+            if ((len(gradient_vectors) < GRADIENT_FIELD_PCA_PARAMS.MIN_SAMPLES) or (eigenvalue_ratio == GRADIENT_FIELD_PCA_PARAMS.NEUTRAL_SCORE)):
+                return MetricResult(metric_type = MetricType.GRADIENT,
+                                    score       = GRADIENT_FIELD_PCA_PARAMS.NEUTRAL_SCORE,
+                                    confidence  = 0.0,
+                                    details     = {"reason"           : "insufficient_gradient_information",
+                                                   "original_pixels"  : int(gx.size),
+                                                   "filtered_vectors" : int(len(gradient_vectors)),
+                                                  },
+                                   )
+            
+            # Convert to suspicion score
+            suspicion_score       = self._eigenvalue_to_suspicion(eigenvalue_ratio = eigenvalue_ratio)
+
+            # Confidence inverted relative to suspicion: High eigenvalue_ratio = natural, High suspicion_score = AI-like
+            confidence            = abs(eigenvalue_ratio - GRADIENT_FIELD_PCA_PARAMS.EIGENVALUE_RATIO_THRESHOLD)
+            normalized_confidence = np.clip((confidence / GRADIENT_FIELD_PCA_PARAMS.EIGENVALUE_RATIO_THRESHOLD), 0.0, 1.0)
+            
+            logger.debug(f"Gradient PCA: eigenvalue_ratio={eigenvalue_ratio:.3f}, suspicion_score={suspicion_score:.3f}")
+            
+            return MetricResult(metric_type = MetricType.GRADIENT,
+                                score       = float(suspicion_score),
+                                confidence  = float(normalized_confidence),
+                                details     = {"gradient_vectors_sampled" : len(gradient_vectors),
+                                               "eigenvalue_ratio"         : float(eigenvalue_ratio),
+                                               "threshold"                : GRADIENT_FIELD_PCA_PARAMS.EIGENVALUE_RATIO_THRESHOLD,
+                                               "original_pixels"          : int(gx.size),
+                                               "filtered_vectors"         : int(len(gradient_vectors)),
+                                              },
+                               )
+            
+        except Exception as e:
+            logger.error(f"Gradient PCA detection failed: {e}")
+            
+            # Return neutral score on error
+            return MetricResult(metric_type = MetricType.GRADIENT,
+                                score       = GRADIENT_FIELD_PCA_PARAMS.NEUTRAL_SCORE,
+                                confidence  = 0.0,
+                                details     = {"error" : "Gradient PCA detection failed"},
+                               )
+    
+
+    def _prepare_and_sample_gradients(self, gx: np.ndarray, gy: np.ndarray) -> np.ndarray:
+        """
+        Flatten gradients into vectors and sample
+        
+        Arguments:
+        ----------
+            gx { np.ndarray } : Gradient in x direction
+
+            gy { np.ndarray } : Gradient in y direction
+        
+        Returns:
+        --------
+            { np.ndarray }    : Array of gradient vectors (N, 2) where N <= SAMPLE_SIZE
+        """
+        # Flatten to vectors
+        gx_flat                   = gx.flatten()
+        gy_flat                   = gy.flatten()
+
+        # Stack into (N, 2) array
+        gradient_vectors          = np.stack([gx_flat, gy_flat], axis = 1)
+        original_n                = len(gradient_vectors)
+
+        # Remove zero gradients (uniform regions)
+        magnitude                 = np.linalg.norm(gradient_vectors, axis = 1)
+        non_zero_mask             = (magnitude > GRADIENT_FIELD_PCA_PARAMS.MAGNITUDE_THRESHOLD)
+        finite_mask               = np.isfinite(gradient_vectors).all(axis = 1)
+
+        # Filtering Gradient Vector
+        filtered_gradient_vectors = gradient_vectors[non_zero_mask & finite_mask]
+        filtered_n                = len(filtered_gradient_vectors)
+        
+        # Sample if too many points without replacement
+        if (len(filtered_gradient_vectors) > GRADIENT_FIELD_PCA_PARAMS.SAMPLE_SIZE):
+            indices                   = self._range.choice(a       = len(filtered_gradient_vectors), 
+                                                           size    = GRADIENT_FIELD_PCA_PARAMS.SAMPLE_SIZE, 
+                                                           replace = False,
+                                                          )
+
+            sampled_gradient_vectors  = filtered_gradient_vectors[indices]
+        
+        else:
+            sampled_gradient_vectors  = filtered_gradient_vectors
+
+
+        sampled_n = len(sampled_gradient_vectors)
+
+        logger.debug(f"Gradient PCA sampling: original={original_n}, filtered={filtered_n}, sampled={sampled_n}")
+        
+        return sampled_gradient_vectors
+    
+
+    def _compute_eigenvalue_ratio(self, gradient_vectors: np.ndarray) -> float:
+        """
+        Compute ratio of first eigenvalue to total variance
+        
+        -  Lower ratio  = more diffuse structure = suspicious
+        -  Higher ratio = concentrated structure = natural
+        
+        Arguments:
+        ----------
+            gradient_vectors { np.ndarray } : Array of gradient vectors (N, 2)
+        
+        Returns:
+        --------
+                     { float }              : Ratio of first eigenvalue to sum of eigenvalues
+        """
+        if (len(gradient_vectors) < GRADIENT_FIELD_PCA_PARAMS.MIN_SAMPLES):
+            logger.warning("Insufficient gradient samples for PCA")
+            return GRADIENT_FIELD_PCA_PARAMS.NEUTRAL_SCORE
+        
+        # Compute covariance matrix
+        covariance       = np.cov(m    = gradient_vectors.T,
+                                  bias = True,
+                                 )
+        
+        # Compute eigenvalues
+        eigenvalues      = np.linalg.eigvalsh(covariance)
+
+        # Sort in descending order
+        eigenvalues      = np.sort(eigenvalues)[::-1]  
+        
+        # Ratio of largest eigenvalue to sum
+        total_variance   = np.sum(eigenvalues)
+        
+        if (total_variance < GRADIENT_FIELD_PCA_PARAMS.VARIANCE_THRESHOLD):
+            return GRADIENT_FIELD_PCA_PARAMS.NEUTRAL_SCORE
+        
+        eigenvalue_ratio = eigenvalues[0] / total_variance
+
+        return float(eigenvalue_ratio)
+    
+
+    def _eigenvalue_to_suspicion(self, eigenvalue_ratio: float) -> float:
+        """
+        Convert eigenvalue ratio to suspicion score
+        
+        - Real photos : High ratio (0.85-0.95) -> Low suspicion
+        - AI images   : Low ratio (0.50-0.75) -> High suspicion
+        
+        Arguments:
+        ----------
+            eigenvalue_ratio { float } : PCA eigenvalue ratio
+        
+        Returns:
+        --------
+                    { float }          : Suspicion score [0.0, 1.0]
+        """
+        # Invert and scale: higher ratio = lower suspicion
+        # Real photos typically have ratio > 0.85 & AI images typically have ratio < 0.75
+        if (eigenvalue_ratio >= GRADIENT_FIELD_PCA_PARAMS.EIGENVALUE_RATIO_THRESHOLD):
+            # Strong gradient alignment = likely real
+            suspicion = max(0.0, (1.0 - eigenvalue_ratio) * 2.0)
+
+        else:
+            # Weak alignment = suspicious
+            suspicion = 1.0 - (eigenvalue_ratio / GRADIENT_FIELD_PCA_PARAMS.EIGENVALUE_RATIO_THRESHOLD)
+        
+        return float(np.clip(suspicion, 0.0, 1.0))

metrics/noise_analyzer.py

@@ -0,0 +1,335 @@
+# Dependencies
+import numpy as np
+from utils.logger import get_logger
+from config.schemas import MetricResult
+from config.constants import MetricType
+from utils.image_processor import ImageProcessor
+from config.constants import NOISE_ANALYSIS_PARAMS
+
+# Suppress NumPy warning 
+np.seterr(divide  = 'ignore', 
+          invalid = 'ignore',
+         )
+
+         
+# Setup Logging
+logger = get_logger(__name__)
+
+
+class NoiseAnalyzer:
+    """
+    Noise pattern analysis for AI detection
+    
+    Core principle:
+    ---------------
+    - Real photos : Sensor noise follows Poisson distribution (shot noise) + Gaussian (read noise)
+    - AI images   : Too uniform, artificially smooth, or completely missing noise
+    
+    Method:
+    -------
+    1. Extract local patches
+    2. Estimate noise variance in each patch
+    3. Analyze noise consistency and distribution
+    4. Check for unnatural uniformity
+    """
+    def __init__(self):
+        self.image_processor = ImageProcessor()
+    
+
+    def detect(self, image: np.ndarray) -> MetricResult:
+        """
+        Run noise pattern analysis
+        
+        Arguments:
+        ----------
+            image { np.ndarray } : RGB image array (H, W, 3)
+        
+        Returns:
+        --------
+            { MetricResult }     : Structured Noise-domain metric result containing:
+                                   - score      : Suspicion score [0.0, 1.0]
+                                   - confidence : Reliability of noise evidence
+                                   - details    : Noise related diagnostics
+        """
+        try:
+            logger.debug(f"Running noise analysis on image shape {image.shape}")
+            
+            # Convert to luminance
+            luminance                  = self.image_processor.rgb_to_luminance(image = image)
+            
+            # Extract patches
+            patches                    = self._extract_patches(luminance = luminance)
+            
+            if (len(patches) == 0):
+                logger.warning("No patches extracted for noise analysis")
+                return MetricResult(metric_type = MetricType.NOISE,
+                                    score       = NOISE_ANALYSIS_PARAMS.NEUTRAL_SCORE,
+                                    confidence  = 0.0,
+                                    details     = {"reason": "no_patches_extracted"},
+                                   )
+            
+            # Estimate noise in each patch
+            noise_estimates, mad_values, laplacian_energy = self._estimate_noise_per_patch(patches = patches)
+            
+            # Filter Noise Estimates, MAD and Laplacian Energy for finite values only
+            filtered_mask                                 = np.isfinite(noise_estimates)
+            filtered_noise_estimates                      = noise_estimates[filtered_mask]
+            filtered_mad                                  = mad_values[filtered_mask]
+            filtered_laplacian_energy                     = laplacian_energy[filtered_mask]
+            
+            if (len(filtered_noise_estimates) < NOISE_ANALYSIS_PARAMS.MIN_ESTIMATES):
+                logger.debug("Insufficient valid noise estimates after filtering")
+
+                return MetricResult(metric_type = MetricType.NOISE,
+                                    score       = NOISE_ANALYSIS_PARAMS.NEUTRAL_SCORE,
+                                    confidence  = 0.0,
+                                    details     = {"reason"        : "insufficient_noise_estimates",
+                                                   "patches_total" : int(len(patches)),
+                                                   "patches_valid" : int(len(filtered_noise_estimates)),
+                                                  },
+                                   )
+
+            logger.debug(f"Noise patches: total={len(patches)}, valid={len(filtered_noise_estimates)}")
+            
+            # Analyze noise distribution
+            noise_score, noise_details                    = self._analyze_noise_distribution(noise_estimates  = filtered_noise_estimates,
+                                                                                             mad_values       = filtered_mad,
+                                                                                             laplacian_energy = filtered_laplacian_energy,
+                                                                                            )
+            
+            # Confidence: distance from neutral
+            confidence                                    = float(np.clip((abs(noise_score - NOISE_ANALYSIS_PARAMS.NEUTRAL_SCORE) * 2.0), 0.0, 1.0))
+            
+            logger.debug(f"Noise analysis: score={noise_score:.3f}, patches={len(patches)}, valid={len(filtered_noise_estimates)}")
+            
+            return MetricResult(metric_type = MetricType.NOISE,
+                                score       = float(noise_score),
+                                confidence  = confidence,
+                                details     = {"patches_total" : int(len(patches)),
+                                               "patches_valid" : int(len(filtered_noise_estimates)),
+                                               **noise_details,
+                                              },
+                               )
+            
+        except Exception as e:
+            logger.error(f"Noise analysis failed: {e}")
+            
+            # Return neutral score on error
+            return MetricResult(metric_type = MetricType.NOISE,
+                                score       = NOISE_ANALYSIS_PARAMS.NEUTRAL_SCORE,
+                                confidence  = 0.0,
+                                details     = {"error": "noise_analysis_failed"},
+                               )
+                        
+
+    def _extract_patches(self, luminance: np.ndarray) -> np.ndarray:
+        """
+        Extract patches from image for local noise estimation
+        
+        Arguments:
+        ----------
+            luminance { np.ndarray } : Luminance channel (H, W)
+        
+        Returns:
+        --------
+            { np.ndarray }           : Array of patches
+        """
+        patches = self.image_processor.extract_patches(image       = luminance,
+                                                       patch_size  = NOISE_ANALYSIS_PARAMS.PATCH_SIZE,
+                                                       stride      = NOISE_ANALYSIS_PARAMS.STRIDE,
+                                                       max_patches = NOISE_ANALYSIS_PARAMS.SAMPLES,
+                                                      )
+        
+        return patches
+    
+
+    def _estimate_noise_per_patch(self, patches: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """
+        Estimate noise variance in each patch using median absolute deviation
+        
+        Uses Median Absolute Deviation (MAD) which is robust to edges/textures
+        
+        Arguments:
+        ----------
+            patches { np.ndarray } : Array of image patches (N, patch_size, patch_size)
+        
+        Returns:
+        --------
+                 { tuple }         : A tuple containing
+                                     - Array of noise estimates per patch
+                                     - Array of MAD values
+                                     - Array of Laplacian Energy Values
+        """
+        noise_estimates          = list()
+        mad_values               = list()
+        laplacian_energy_values  = list()
+        
+        for patch in patches:
+            # Skip patches with too much structure (edges, textures)
+            variance = np.var(patch)
+            
+            if (variance < NOISE_ANALYSIS_PARAMS.VARIANCE_LOW_THRESHOLD):     
+                # Too uniform, skip
+                continue
+            
+            if (variance > NOISE_ANALYSIS_PARAMS.VARIANCE_HIGH_THRESHOLD):  
+                # Too much structure, skip
+                continue
+            
+            # Use Median Absolute Deviation for robust noise estimation
+            laplacian   = self._apply_laplacian(patch = patch)
+            mad         = np.median(np.abs(laplacian - np.median(laplacian)))
+            
+            # Convert MAD to noise standard deviation estimate: For Gaussian noise: σ ≈ 1.4826 × MAD
+            noise_std   = NOISE_ANALYSIS_PARAMS.MAD_TO_STD_FACTOR * mad
+
+            # Calculate Laplacian Energy
+            lap_energy  = float(np.mean(laplacian ** 2))
+            
+            # Append corresponding values to their storages
+            mad_values.append(mad)
+            noise_estimates.append(noise_std)
+            laplacian_energy_values.append(lap_energy)
+        
+        return np.array(noise_estimates), np.array(mad_values), np.array(laplacian_energy_values)
+    
+
+    def _apply_laplacian(self, patch: np.ndarray) -> np.ndarray:
+        """
+        Apply Laplacian filter to isolate high-frequency noise
+        
+        Arguments:
+        ----------
+            patch { np.ndarray } : Image patch
+        
+        Returns:
+        --------
+            { np.ndarray }       : Laplacian-filtered patch
+        """
+        # Simple 3x3 Laplacian kernel
+        kernel = np.array([[0,  1, 0],
+                          [1, -4, 1],
+                          [0,  1, 0]],
+                         )
+        
+        # Pad patch
+        padded = np.pad(patch, 1, mode = 'reflect')
+        
+        # Apply convolution
+        h, w   = patch.shape
+        result = np.zeros_like(patch)
+        
+        for i in range(h):
+            for j in range(w):
+                region        = padded[i:i+3, j:j+3]
+                result[i, j]  = np.sum(region * kernel)
+        
+        return result
+    
+
+    def _analyze_noise_distribution(self, noise_estimates: np.ndarray, mad_values: np.ndarray, laplacian_energy: np.ndarray,) -> tuple[float, dict]:
+        """
+        Analyze noise distribution for anomalies
+        
+        Checks:
+        -------
+        1. Coefficient of variation (consistency)
+        2. Overall noise level (too low = suspicious)
+        3. Distribution shape (too uniform = suspicious)
+        
+        Arguments:
+        ----------
+            noise_estimates  { np.ndarray } : Array of noise standard deviations
+
+            mad_values       { np.ndarray } : Array of MAD values
+
+            laplacian_energy { np.ndarray } : Array of Laplacian Energy Values 
+        
+        Returns:
+        --------
+                    { tuple }              : A tuple containing:
+                                             - Suspicion score [0.0, 1.0]
+                                             - Noise Distribution detailed diagnostics
+        """
+        if (len(noise_estimates) < NOISE_ANALYSIS_PARAMS.MIN_ESTIMATES):
+            return (NOISE_ANALYSIS_PARAMS.NEUTRAL_SCORE,
+                    {"reason": "insufficient_noise_samples"},
+                   )
+        
+        # Remove outliers (keep middle 80%)
+        q10                 = np.percentile(noise_estimates, NOISE_ANALYSIS_PARAMS.OUTLIER_PERCENTILE_LOW)
+        q90                 = np.percentile(noise_estimates, NOISE_ANALYSIS_PARAMS.OUTLIER_PERCENTILE_HIGH)
+        filtered            = noise_estimates[(noise_estimates >= q10) & (noise_estimates <= q90)]
+        
+        if (len(filtered) < NOISE_ANALYSIS_PARAMS.MIN_FILTERED_SAMPLES):
+            return (NOISE_ANALYSIS_PARAMS.NEUTRAL_SCORE,
+                    {"reason": "insufficient_filtered_samples"},
+                   ) 
+        
+        mean_noise          = np.mean(filtered)
+        std_noise           = np.std(filtered)
+        
+        # Coefficient of Variation (CV) Analysis
+        cv                  = std_noise / (mean_noise + 1e-10)
+        cv_anomaly          = 0.0
+        
+        if (cv < NOISE_ANALYSIS_PARAMS.CV_UNIFORM_THRESHOLD):
+            # Too uniform
+            cv_anomaly = (NOISE_ANALYSIS_PARAMS.CV_UNIFORM_THRESHOLD - cv) * NOISE_ANALYSIS_PARAMS.CV_UNIFORM_SCALE
+        
+        elif (cv > NOISE_ANALYSIS_PARAMS.CV_VARIABLE_THRESHOLD):
+            # Too variable
+            cv_anomaly = min(1.0, (cv - NOISE_ANALYSIS_PARAMS.CV_VARIABLE_THRESHOLD) * NOISE_ANALYSIS_PARAMS.CV_VARIABLE_SCALE)
+        
+        # Overall noise level Analysis
+        noise_level_anomaly = 0.0
+
+        if (mean_noise < NOISE_ANALYSIS_PARAMS.LEVEL_CLEAN_THRESHOLD):
+            # Too clean
+            noise_level_anomaly = (NOISE_ANALYSIS_PARAMS.LEVEL_CLEAN_THRESHOLD - mean_noise) / NOISE_ANALYSIS_PARAMS.LEVEL_CLEAN_THRESHOLD
+        
+        elif (mean_noise < NOISE_ANALYSIS_PARAMS.LEVEL_LOW_THRESHOLD):
+            # Slightly low
+            noise_level_anomaly = (NOISE_ANALYSIS_PARAMS.LEVEL_LOW_THRESHOLD - mean_noise) / NOISE_ANALYSIS_PARAMS.LEVEL_LOW_THRESHOLD * 0.5
+
+        
+        # Distribution shape Analysis
+        q25                 = np.percentile(filtered, NOISE_ANALYSIS_PARAMS.IQR_PERCENTILE_LOW)
+        q75                 = np.percentile(filtered, NOISE_ANALYSIS_PARAMS.IQR_PERCENTILE_HIGH)
+        iqr                 = q75 - q25
+        iqr_ratio           = iqr / (mean_noise + 1e-10)
+        
+        iqr_anomaly         = 0.0
+        
+        if (iqr_ratio < NOISE_ANALYSIS_PARAMS.IQR_THRESHOLD):
+            iqr_anomaly = (NOISE_ANALYSIS_PARAMS.IQR_THRESHOLD - iqr_ratio) * NOISE_ANALYSIS_PARAMS.IQR_SCALE
+
+        # Clip sub-anomalies for safety
+        cv_anomaly          = np.clip(cv_anomaly, 0.0, 1.0)
+        noise_level_anomaly = np.clip(noise_level_anomaly, 0.0, 1.0)
+        iqr_anomaly         = np.clip(iqr_anomaly, 0.0, 1.0)
+
+        # Combine scores
+        weights             = NOISE_ANALYSIS_PARAMS.SUBMETRIC_WEIGHTS
+        combined_score      = (weights['cv_anomaly'] * cv_anomaly + weights['noise_level_anomaly'] * noise_level_anomaly + weights['iqr_anomaly'] * iqr_anomaly)
+        final_score         = float(np.clip(combined_score, 0.0, 1.0))
+
+        # Calculate Forensic Stats
+        mad_mean            = float(np.mean(mad_values)) if len(mad_values) else 0.0
+        laplacian_energy_mu = float(np.mean(laplacian_energy)) if len(laplacian_energy) else 0.0
+
+        noise_details_dict  = {"mean_noise"          : float(mean_noise),
+                               "std_noise"           : float(std_noise),
+                               "cv"                  : float(cv),
+                               "cv_anomaly"          : float(cv_anomaly),
+                               "noise_level_anomaly" : float(noise_level_anomaly),
+                               "iqr_ratio"           : float(iqr_ratio),
+                               "iqr_anomaly"         : float(iqr_anomaly),
+                               "mad_mean"            : mad_mean,
+                               "laplacian_energy"    : laplacian_energy_mu,
+                              }
+
+        logger.debug(f"Noise scores - CV: {cv:.3f}, mean: {mean_noise:.3f}, IQR ratio: {iqr_ratio:.3f}")
+
+        return final_score, noise_details_dict
+        

metrics/texture_analyzer.py

@@ -0,0 +1,308 @@
+# Dependencies
+import numpy as np
+from scipy.stats import entropy
+from utils.logger import get_logger
+from config.schemas import MetricResult
+from config.constants import MetricType
+from utils.image_processor import ImageProcessor
+from config.constants import TEXTURE_ANALYSIS_PARAMS
+
+# Suppress NumPy warning 
+np.seterr(divide  = 'ignore', 
+          invalid = 'ignore',
+         )
+         
+
+# Setup Logging
+logger = get_logger(__name__)
+
+
+class TextureAnalyzer:
+    """
+    Statistical texture analysis for AI detection
+    
+    Core principle:
+    ---------------
+    - Real photos : Natural texture variation (random but structured)
+    - AI images   : Either too smooth or repetitive patterns
+    
+    Method:
+    -------
+    1. Extract local patches
+    2. Compute texture features (contrast, entropy)
+    3. Analyze texture consistency and distribution
+    4. Detect unnaturally smooth regions
+    """
+    def __init__(self):
+        """
+        Initialize TextureAnalyzer Class
+        """
+        self.patch_size      = TEXTURE_ANALYSIS_PARAMS.PATCH_SIZE
+        self.n_patches       = TEXTURE_ANALYSIS_PARAMS.N_PATCHES
+        self.image_processor = ImageProcessor()
+        self._rng            = np.random.default_rng(seed = TEXTURE_ANALYSIS_PARAMS.RANDOM_SEED)
+    
+
+    def detect(self, image: np.ndarray) -> MetricResult:
+        """
+        Run texture analysis
+        
+        Arguments:
+        ----------
+            image { np.ndarray } : RGB image array (H, W, 3)
+        
+        Returns:
+        --------
+            { MetricResult }     : Structured Texture-domain metric result containing:
+                                   - score      : Suspicion score [0.0, 1.0]
+                                   - confidence : Reliability of texture evidence
+                                   - details    : Texture forensics and statistics
+        """
+        try:
+            logger.debug(f"Running texture analysis on image shape {image.shape}")
+            
+            # Convert to luminance
+            luminance                          = self.image_processor.rgb_to_luminance(image = image)
+            
+            # Extract patches
+            patches                            = self._extract_patches(luminance = luminance)
+            
+            if (len(patches) == 0):
+                logger.warning("No patches extracted for texture analysis")
+                return MetricResult(metric_type = MetricType.TEXTURE,
+                                    score       = TEXTURE_ANALYSIS_PARAMS.NEUTRAL_SCORE,
+                                    confidence  = 0.0,
+                                    details     = {"reason": "no_patches_extracted"},
+                                   )           
+            
+            # Compute texture features
+            texture_features, texture_metadata = self._compute_texture_features(patches = patches)
+            
+            # Analyze for anomalies
+            texture_score, texture_details     = self._analyze_texture_anomalies(features = texture_features,
+                                                                                 metadata = texture_metadata,
+                                                                                )
+            
+            # Calculate Confidence
+            confidence                         = float(np.clip((abs(texture_score - TEXTURE_ANALYSIS_PARAMS.NEUTRAL_SCORE) * 2.0), 0.0, 1.0))
+            
+            logger.debug(f"Texture analysis: Texture Score={texture_score:.3f}, patches={len(patches)}")
+            
+            return MetricResult(metric_type = MetricType.TEXTURE,
+                                score       = float(texture_score),
+                                confidence  = confidence,
+                                details     = {"patches_total" : int(len(patches)),
+                                               **texture_metadata,
+                                               **texture_details,
+                                              },
+                               )
+            
+        except Exception as e:
+            logger.error(f"Texture analysis failed: {e}")
+            
+            # Return neutral score on error
+            return MetricResult(metric_type = MetricType.TEXTURE,
+                                score       = TEXTURE_ANALYSIS_PARAMS.NEUTRAL_SCORE,
+                                confidence  = 0.0,
+                                details     = {"error": "texture_analysis_failed"},
+                               )
+    
+
+    def _extract_patches(self, luminance: np.ndarray) -> np.ndarray:
+        """
+        Extract random patches from image
+        """
+        h, w = luminance.shape
+        
+        if ((h < self.patch_size) or (w < self.patch_size)):
+            logger.warning(f"Image too small for patch size {self.patch_size}")
+            return np.array([])
+        
+        patches = list()
+        
+        for _ in range(self.n_patches):
+            y     = self._rng.integers(0, h - self.patch_size)
+            x     = self._rng.integers(0, w - self.patch_size)
+            
+            patch = luminance[y:y+self.patch_size, x:x+self.patch_size]
+            patches.append(patch)
+        
+        return np.array(patches)
+    
+
+    def _compute_texture_features(self, patches: np.ndarray) -> tuple[dict, dict]:
+        """
+        Compute texture features for each patch
+        
+        Features:
+        ---------
+        1. Local contrast (standard deviation)
+        2. Entropy (randomness)
+        3. Smoothness (inverse of variance)
+        4. Edge density
+        
+        Arguments:
+        ----------
+            patches { np.ndarray } : Array of patches
+        
+        Returns:
+        --------
+            { tuple }              : A tuple containing
+                                     - A dictionary of feature arrays
+                                     - A dictionary of texture analysis metadata
+        """
+        contrasts       = list()
+        entropies       = list()
+        smoothnesses    = list()
+        edge_densities  = list()
+        uniform_skipped = 0
+        
+        for patch in patches:
+            pmin = patch.min()
+            pmax = patch.max()
+
+            if ((pmax - pmin < 1e-6)):
+                # skip fully uniform patch entirely
+                uniform_skipped += 1
+                continue 
+             
+            # Contrast (std deviation)
+            contrast = np.std(patch)
+            contrasts.append(contrast)
+            
+            # Entropy (using histogram)
+            hist, _  = np.histogram(patch,
+                                    bins  = TEXTURE_ANALYSIS_PARAMS.HISTOGRAM_BINS,
+                                    range = TEXTURE_ANALYSIS_PARAMS.HISTOGRAM_RANGE,
+                                   )
+
+            hist     = hist / (np.sum(hist) + 1e-10)
+            ent      = entropy(hist + 1e-10)
+            entropies.append(ent)
+            
+            # Smoothness (inverse of variance, scaled)
+            variance   = np.var(patch)
+            smoothness = 1.0 / (1.0 + variance)
+            smoothnesses.append(smoothness)
+            
+            # Edge density (using Sobel)
+            gx, gy       = self.image_processor.compute_gradients(luminance = patch)
+            gradient_mag = np.sqrt(gx**2 + gy**2)
+
+            edge_density = np.mean(gradient_mag > TEXTURE_ANALYSIS_PARAMS.EDGE_THRESHOLD) 
+            edge_densities.append(edge_density)
+
+        # Construct results in proper format
+        features = {"contrast"     : np.array(contrasts),
+                    "entropy"      : np.array(entropies),
+                    "smoothness"   : np.array(smoothnesses),
+                    "edge_density" : np.array(edge_densities),
+                   }
+
+        metadata = {"patches_used"            : int(len(contrasts)),
+                    "uniform_patches_skipped" : int(uniform_skipped),
+                   }
+
+        
+        return features, metadata
+    
+
+    def _analyze_texture_anomalies(self, features: dict, metadata: dict) -> tuple[float, dict]:
+        """
+        Analyze texture features for AI generation artifacts
+        
+        Checks:
+        -------
+        1. Excessive smoothness (too many overly smooth patches)
+        2. Entropy distribution (too uniform = suspicious)
+        3. Contrast consistency
+        
+        Arguments:
+        ----------
+            features { dict } : Dictionary of texture features
+
+            metadata { dict } : Dictionary of texture analysis metadata
+        
+        Returns:
+        --------
+            { tuple }         : A tuple containing:
+                                - Suspicion score [0.0, 1.0]
+                                - Texture statistics
+        """
+        contrast     = features['contrast']
+        entropy_vals = features['entropy']
+        smoothness   = features['smoothness']
+        edge_density = features['edge_density']
+
+        if ((len(contrast) == 0) or (len(entropy_vals) == 0) or (len(smoothness) == 0) or (len(edge_density) == 0)):
+            logger.debug("All texture features filtered out; returning neutral score")
+            return (TEXTURE_ANALYSIS_PARAMS.NEUTRAL_SCORE,
+                    {"reason": "all_texture_features_filtered"},
+                   )
+
+        # Early exit: all patches nearly uniform
+        if (np.all(contrast < 1e-6)):
+            logger.debug("All texture patches near-uniform; returning neutral score")
+            return (TEXTURE_ANALYSIS_PARAMS.NEUTRAL_SCORE,
+                    {"reason": "all_patches_near_uniform"},
+                   )
+        
+        # Smoothness Analysis
+        smooth_ratio       = np.mean(smoothness > TEXTURE_ANALYSIS_PARAMS.SMOOTHNESS_THRESHOLD)
+        smoothness_anomaly = 0.0
+        
+        if (smooth_ratio > TEXTURE_ANALYSIS_PARAMS.SMOOTH_RATIO_THRESHOLD):
+            # More than 40% very smooth patches
+            smoothness_anomaly = min(1.0, (smooth_ratio - TEXTURE_ANALYSIS_PARAMS.SMOOTH_RATIO_THRESHOLD) * TEXTURE_ANALYSIS_PARAMS.SMOOTH_RATIO_SCALE)
+        
+        # Entropy distribution Analysis
+        entropy_cv      = np.std(entropy_vals) / (np.mean(entropy_vals) + 1e-10)
+        entropy_anomaly = 0.0
+        
+        if (entropy_cv < TEXTURE_ANALYSIS_PARAMS.ENTROPY_CV_THRESHOLD):  
+            # Too uniform
+            entropy_anomaly = (TEXTURE_ANALYSIS_PARAMS.ENTROPY_CV_THRESHOLD - entropy_cv) * TEXTURE_ANALYSIS_PARAMS.ENTROPY_SCALE
+        
+        # Contrast distribution Analysis
+        contrast_cv      = np.std(contrast) / (np.mean(contrast) + 1e-10)
+        contrast_anomaly = 0.0
+        
+        if (contrast_cv < TEXTURE_ANALYSIS_PARAMS.CONTRAST_CV_LOW):
+            # Too uniform
+            contrast_anomaly = (TEXTURE_ANALYSIS_PARAMS.CONTRAST_CV_LOW - contrast_cv) * TEXTURE_ANALYSIS_PARAMS.CONTRAST_LOW_SCALE 
+        
+        elif (contrast_cv > TEXTURE_ANALYSIS_PARAMS.CONTRAST_CV_HIGH):
+            # Too variable (suspicious)
+            contrast_anomaly = min(1.0, (contrast_cv - TEXTURE_ANALYSIS_PARAMS.CONTRAST_CV_HIGH) * TEXTURE_ANALYSIS_PARAMS.CONTRAST_HIGH_SCALE)
+
+        
+        # Edge density consistency Analysis
+        edge_cv      = np.std(edge_density) / (np.mean(edge_density) + 1e-10)
+        edge_anomaly = 0.0
+        
+        if (edge_cv < TEXTURE_ANALYSIS_PARAMS.EDGE_CV_THRESHOLD):
+            edge_anomaly = (TEXTURE_ANALYSIS_PARAMS.EDGE_CV_THRESHOLD - edge_cv) * TEXTURE_ANALYSIS_PARAMS.EDGE_SCALE
+
+        # Clipping Sub-anomalies
+        smoothness_anomaly = np.clip(smoothness_anomaly, 0.0, 1.0)
+        entropy_anomaly    = np.clip(entropy_anomaly, 0.0, 1.0)
+        contrast_anomaly   = np.clip(contrast_anomaly, 0.0, 1.0)
+        edge_anomaly       = np.clip(edge_anomaly, 0.0, 1.0)
+        
+        # Combine scores
+        weights            = TEXTURE_ANALYSIS_PARAMS.SUBMETRIC_WEIGHTS
+        texture_score      = (weights['smoothness_anomaly'] * smoothness_anomaly + weights['entropy_anomaly'] * entropy_anomaly + weights['contrast_anomaly'] * contrast_anomaly + weights['edge_anomaly'] * edge_anomaly)
+        final_score        = float(np.clip(texture_score, 0.0, 1.0))
+
+        detailed_stats     = {"smooth_ratio"      : float(smooth_ratio),
+                              "entropy_mean"      : float(np.mean(entropy_vals)),
+                              "entropy_cv"        : float(entropy_cv),
+                              "contrast_mean"     : float(np.mean(contrast)),
+                              "contrast_cv"       : float(contrast_cv),
+                              "edge_density_mean" : float(np.mean(edge_density)),
+                              "edge_cv"           : float(edge_cv),
+                             }
+
+        logger.debug(f"Texture scores - smoothness: {smoothness_anomaly:.3f}, entropy: {entropy_anomaly:.3f}, contrast: {contrast_anomaly:.3f}, edge: {edge_anomaly:.3f}")
+        
+        return final_score, detailed_stats

notebooks/.ipynb_checkpoints/Unified_Dataset_Builder-checkpoint.ipynb

@@ -0,0 +1,725 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e2d654dc-c431-420e-810a-a985de9172fd",
+   "metadata": {},
+   "source": [
+    "# Unified AI vs Real Image Dataset Builder\n",
+    "\n",
+    "This notebook builds a **clean, labeled, unified dataset** for evaluating\n",
+    "AI image detection systems.\n",
+    "\n",
+    "### Supported sources\n",
+    "- HuggingFace datasets (DiffusionDB, COCO, OpenImages)\n",
+    "- Kaggle public datasets (Midjourney, AI vs Real)\n",
+    "- Unified output format:\n",
+    "  - Normalized PNG images\n",
+    "  - Size-limited (≤1024px)\n",
+    "  - Central metadata CSV\n",
+    "\n",
+    "### Output Structure\n",
+    "\n",
+    "```bash\n",
+    "tests/dataset/\n",
+    "├── ai/\n",
+    "├── real/\n",
+    "├── raw_downloads/\n",
+    "├── metadata/dataset_index.csv\n",
+    "```\n",
+    "\n",
+    "> ⚠️ All datasets used are **public & legally accessible**.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8b43897-9ce5-4f20-8798-7b3aebdf1b36",
+   "metadata": {},
+   "source": [
+    "## Required Dependencies\n",
+    "\n",
+    "Before running, ensure:\n",
+    "\n",
+    "```bash\n",
+    "pip install datasets pillow tqdm kaggle pycocotools\n",
+    "```\n",
+    "\n",
+    "Also configure Kaggle:\n",
+    "\n",
+    "```bash\n",
+    "~/.kaggle/kaggle.json\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00b9f50c-6158-47e9-89cf-5c279d9c63bb",
+   "metadata": {},
+   "source": [
+    "## Imports & Config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9147ace7-162f-4b0d-bd6d-0d92b9bad61e",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# ===============================\n",
+    "# Imports & Global Configuration\n",
+    "# ===============================\n",
+    "import os\n",
+    "import csv\n",
+    "import uuid\n",
+    "import subprocess\n",
+    "from PIL import Image\n",
+    "from tqdm import tqdm\n",
+    "from pathlib import Path\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "\n",
+    "# ===============================\n",
+    "# Directory Configuration\n",
+    "# ===============================\n",
+    "BASE_DIR       = Path(\"tests/dataset\")\n",
+    "AI_DIR         = BASE_DIR / \"ai\"\n",
+    "REAL_DIR       = BASE_DIR / \"real\"\n",
+    "RAW_DIR        = BASE_DIR / \"raw_downloads\"\n",
+    "META_DIR       = BASE_DIR / \"metadata\"\n",
+    "\n",
+    "META_FILE      = META_DIR / \"dataset_index.csv\"\n",
+    "\n",
+    "TARGET_PER_DS  = 1000\n",
+    "IMAGE_SIZE_MAX = 1024\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "329d1c09-0e9c-4bc2-8935-bd50941611c8",
+   "metadata": {},
+   "source": [
+    "## Utility Functions\n",
+    "\n",
+    "These helpers:\n",
+    "- Ensure directory structure\n",
+    "- Normalize images (RGB, resize, PNG)\n",
+    "- Write metadata rows safely"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b352e981-e456-40cf-be84-a1eb0f01ea7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ensure_dirs():\n",
+    "    for d in [AI_DIR, REAL_DIR, RAW_DIR, META_DIR]:\n",
+    "        d.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "\n",
+    "def normalize_and_save(image: Image.Image, path: Path):\n",
+    "    \"\"\"\n",
+    "    Normalize image to RGB PNG and limit size\n",
+    "    \"\"\"\n",
+    "    image = image.convert(\"RGB\")\n",
+    "    image.thumbnail((IMAGE_SIZE_MAX, IMAGE_SIZE_MAX))\n",
+    "    image.save(path, \n",
+    "               format   = \"PNG\", \n",
+    "               optimize = True,\n",
+    "              )\n",
+    "\n",
+    "\n",
+    "def write_meta(writer, **row):\n",
+    "    writer.writerow(row)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34c3bc3b-6bb6-414d-b3fe-85bc43d832c7",
+   "metadata": {},
+   "source": [
+    "## Dataset Registry\n",
+    "\n",
+    "Defines **where data comes from** and **how it is labeled**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "74106705-e2d6-411c-8193-8e02f5ee0fdc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# HuggingFace datasets (safe & stable)\n",
+    "AI_DATASETS     = [{\"name\"      : \"diffusiondb\",\n",
+    "                    \"hf_id\"     : \"poloclub/diffusiondb\",\n",
+    "                    \"config\"    : \"2m_first_1k\",\n",
+    "                    \"split\"     : \"train\",\n",
+    "                    \"image_key\" : \"image\",\n",
+    "                    \"label\"     : \"ai\",\n",
+    "                    \"family\"    : \"diffusion\"\n",
+    "                  }]\n",
+    "        \n",
+    "\n",
+    "REAL_DATASETS   = [{\"name\"      : \"mscoco_2017\",\n",
+    "                    \"hf_id\"     : \"shunk031/MSCOCO\",\n",
+    "                    \"hf_kwargs\" : {\"year\": 2017,\n",
+    "                                   \"coco_task\": \"instances\"\n",
+    "                                  },\n",
+    "                    \"split\"     : \"train\",\n",
+    "                    \"image_key\" : \"image\",\n",
+    "                    \"label\"     : \"real\",\n",
+    "                    \"family\"    : \"photographic\",\n",
+    "                    \"streaming\" : False\n",
+    "                  }]\n",
+    "\n",
+    "# Kaggle datasets (public, non-scraped)\n",
+    "KAGGLE_DATASETS = [{\"name\"      : \"ai_vs_real\",\n",
+    "                    \"kaggle_id\" : \"tristanzhang32/ai-generated-images-vs-real-images\",\n",
+    "                    \"label\"     : \"ai\",\n",
+    "                    \"family\"    : \"mixed\"\n",
+    "                   },\n",
+    "                   {\"name\"      : \"midjourney\",\n",
+    "                    \"kaggle_id\" : \"cyanex1702/midjourney-imagesprompt\",\n",
+    "                    \"label\"     : \"ai\",\n",
+    "                    \"family\"    : \"diffusion\"\n",
+    "                   }\n",
+    "                  ]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f4c6f3b-2a35-415b-9a35-ee52fd3d85be",
+   "metadata": {},
+   "source": [
+    "## HuggingFace Dataset Processor\n",
+    "\n",
+    "Loads datasets via `datasets.load_dataset()` and saves images in unified format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a9ea5276-65bb-49f5-a656-c00ceeb1f4d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process_hf_dataset(ds_cfg, root_dir, writer):\n",
+    "    print(f\"\\n▶ Loading HF dataset: {ds_cfg['name']}\")\n",
+    "\n",
+    "    ds      = load_dataset(ds_cfg[\"hf_id\"],\n",
+    "                           **ds_cfg.get(\"hf_kwargs\", {}),\n",
+    "                           name      = ds_cfg.get(\"config\"),\n",
+    "                           split     = ds_cfg[\"split\"],\n",
+    "                           streaming = ds_cfg.get(\"streaming\", False),\n",
+    "                          )\n",
+    "\n",
+    "    out_dir = root_dir / ds_cfg[\"name\"]\n",
+    "    out_dir.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "    count   = 0\n",
+    "    \n",
+    "    for row in tqdm(ds):\n",
+    "        if (count >= TARGET_PER_DS):\n",
+    "            break\n",
+    "\n",
+    "        try:\n",
+    "            image = row.get(ds_cfg[\"image_key\"])\n",
+    "            if not isinstance(image, Image.Image):\n",
+    "                continue\n",
+    "\n",
+    "            uid   = uuid.uuid4().hex\n",
+    "            path  = out_dir / f\"{uid}.png\"\n",
+    "\n",
+    "            normalize_and_save(image, path)\n",
+    "\n",
+    "            write_meta(writer,\n",
+    "                       id       = uid,\n",
+    "                       filename = str(path),\n",
+    "                       label    = ds_cfg[\"label\"],\n",
+    "                       family   = ds_cfg[\"family\"],\n",
+    "                       source   = ds_cfg[\"name\"],\n",
+    "                      )\n",
+    "\n",
+    "            count += 1\n",
+    "\n",
+    "        except Exception:\n",
+    "            continue\n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb6d23a0-fa98-4351-9e4e-99265a51e8ef",
+   "metadata": {},
+   "source": [
+    "## Kaggle Dataset Downloader\n",
+    "\n",
+    "Requires:\n",
+    "- Kaggle account\n",
+    "- ~/.kaggle/kaggle.json configured\n",
+    "\n",
+    "No scraping. Fully legal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c6eca5e6-0469-4af6-8af8-afe3036cb0a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def download_kaggle_dataset(kaggle_id: str, out_dir: Path):\n",
+    "    out_dir.mkdir(parents = True, exist_ok = True)\n",
+    "\n",
+    "    if any(out_dir.iterdir()):\n",
+    "        print(f\"✔ Kaggle dataset already present: {kaggle_id}\")\n",
+    "        return\n",
+    "\n",
+    "    print(f\"⬇ Downloading Kaggle dataset: {kaggle_id}\")\n",
+    "\n",
+    "    subprocess.run([\"kaggle\", \"datasets\", \"download\",\n",
+    "                    kaggle_id,\n",
+    "                    \"-p\", str(out_dir),\n",
+    "                    \"--unzip\"\n",
+    "                   ],\n",
+    "                   check = True,\n",
+    "                  )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c971767-d20a-4fa3-949a-a655d712b2c1",
+   "metadata": {},
+   "source": [
+    "## Folder Ingestor\n",
+    "\n",
+    "Converts **any folder of images** into the unified dataset format. \n",
+    "Used for Kaggle & future web sources."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b648832e-5025-4851-af21-382051167a04",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "IMAGE_EXTS = {\".png\", \".jpg\", \".jpeg\", \".webp\"}\n",
+    "\n",
+    "def ingest_image_folder(src_dir, out_dir, writer, label, family, source):\n",
+    "    images = [p for p in src_dir.rglob(\"*\") if p.suffix.lower() in IMAGE_EXTS]\n",
+    "\n",
+    "    out_dir.mkdir(parents = True, exist_ok = True)\n",
+    "\n",
+    "    for image_path in tqdm(images[:TARGET_PER_DS]):\n",
+    "        try:\n",
+    "            image = Image.open(image_path)\n",
+    "\n",
+    "            uid   = uuid.uuid4().hex\n",
+    "            dst   = out_dir / f\"{uid}.png\"\n",
+    "\n",
+    "            normalize_and_save(image, dst)\n",
+    "\n",
+    "            write_meta(writer,\n",
+    "                       id       = uid,\n",
+    "                       filename = str(dst),\n",
+    "                       label    = label,\n",
+    "                       family   = family,\n",
+    "                       source   = source,\n",
+    "                      )\n",
+    "            \n",
+    "        except Exception:\n",
+    "            continue\n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53fccdc4-e593-4dbf-a71b-e5b826e4a27a",
+   "metadata": {},
+   "source": [
+    "## Main Pipeline Execution\n",
+    "\n",
+    "This cell:\n",
+    "- Builds directories\n",
+    "- Processes HF datasets\n",
+    "- Downloads & ingests Kaggle datasets\n",
+    "- Writes unified metadata CSV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd8ef771-f39f-4d9d-8eaf-626ecc211141",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "▶ Loading HF dataset: diffusiondb\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 46%|████████████████████████████████████████████████████████████████▊                                                                           | 463/1000 [02:43<04:08,  2.16it/s]"
+     ]
+    }
+   ],
+   "source": [
+    "def main():\n",
+    "    ensure_dirs()\n",
+    "\n",
+    "    with open(META_FILE, \"w\", newline = \"\") as f:\n",
+    "        writer = csv.DictWriter(f, fieldnames=[\"id\", \"filename\", \"label\", \"family\", \"source\"])\n",
+    "        writer.writeheader()\n",
+    "\n",
+    "        # HuggingFace datasets\n",
+    "        for ds in AI_DATASETS:\n",
+    "            process_hf_dataset(ds, AI_DIR, writer)\n",
+    "\n",
+    "        for ds in REAL_DATASETS:\n",
+    "            process_hf_dataset(ds, REAL_DIR, writer)\n",
+    "\n",
+    "        # Kaggle datasets\n",
+    "        for ds in KAGGLE_DATASETS:\n",
+    "            raw_path = RAW_DIR / ds[\"name\"]\n",
+    "            download_kaggle_dataset(ds[\"kaggle_id\"], raw_path)\n",
+    "\n",
+    "            # AI images\n",
+    "            ingest_image_folder(src_dir = raw_path / \"ai\",\n",
+    "                                out_dir = AI_DIR / ds[\"name\"],\n",
+    "                                writer  = writer,\n",
+    "                                label   = \"ai\",\n",
+    "                                family  = ds[\"family\"],\n",
+    "                                source  = ds[\"name\"],\n",
+    "                               )\n",
+    "\n",
+    "            # REAL images\n",
+    "            ingest_image_folder(src_dir = raw_path / \"real\",\n",
+    "                                out_dir = REAL_DIR / ds[\"name\"],\n",
+    "                                writer  = writer,\n",
+    "                                label   = \"real\",\n",
+    "                                family  = \"photographic\",\n",
+    "                                source  = ds[\"name\"],\n",
+    "                               )\n",
+    "\n",
+    "    print(\"\\n✅ Dataset build complete\")\n",
+    "    print(f\"📄 Metadata saved at: {META_FILE}\")\n",
+    "\n",
+    "\n",
+    "# ===============================\n",
+    "# Entry Point\n",
+    "# ===============================\n",
+    "if __name__ == \"__main__\":\n",
+    "    main()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd6e0834-7757-4daf-a8bc-37d58bc8debd",
+   "metadata": {},
+   "source": [
+    "# Post-Processing Attack Generator\n",
+    "\n",
+    "This notebook applies **real-world post-processing attacks** to an existing\n",
+    "image dataset to evaluate robustness of AI-image detectors.\n",
+    "\n",
+    "### Attacks Implemented\n",
+    "- JPEG recompression (quality loss)\n",
+    "- Resize / rescale (down + up)\n",
+    "- Gaussian blur\n",
+    "\n",
+    "### Why this matters\n",
+    "Most AI images in the wild are:\n",
+    "- Screenshot\n",
+    "- Re-encoded\n",
+    "- Uploaded to social media\n",
+    "- Slightly blurred or resized\n",
+    "\n",
+    "If a detector fails here, it fails in production."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd680866-0f5c-4930-9262-5521317044fd",
+   "metadata": {},
+   "source": [
+    "## Imports & Config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b62168b8-aa38-47c6-8a00-0bb31e8774fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ===============================\n",
+    "# Imports\n",
+    "# ===============================\n",
+    "\n",
+    "import csv\n",
+    "import uuid\n",
+    "from PIL import Image\n",
+    "from tqdm import tqdm\n",
+    "from io import BytesIO\n",
+    "from pathlib import Path\n",
+    "from PIL import ImageFilter\n",
+    "\n",
+    "\n",
+    "# ===============================\n",
+    "# Configuration\n",
+    "# ===============================\n",
+    "\n",
+    "BASE_DIR       = Path(\"tests/dataset\")\n",
+    "ATTACK_DIR     = BASE_DIR / \"attacked\"\n",
+    "META_IN        = BASE_DIR / \"metadata/dataset_index.csv\"\n",
+    "META_OUT       = BASE_DIR / \"metadata/dataset_index_attacked.csv\"\n",
+    "\n",
+    "ATTACK_DIR.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "JPEG_QUALITIES = [95, 75, 50]\n",
+    "RESIZE_SCALES  = [0.75, 0.5]\n",
+    "BLUR_RADII     = [0.8, 1.5]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c1de132-8245-42c7-9a82-63d6f0c27270",
+   "metadata": {},
+   "source": [
+    "## Load Existing Metadata\n",
+    "\n",
+    "We read the existing unified dataset index and create\n",
+    "new samples **derived from originals**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a49e5629-ba32-4736-b0ab-e81084f58b78",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_metadata(path):\n",
+    "    with open(path, newline=\"\") as f:\n",
+    "        return list(csv.DictReader(f))\n",
+    "\n",
+    "\n",
+    "records = load_metadata(META_IN)\n",
+    "print(f\"Loaded {len(records)} original samples\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44a0e31a-abdf-4564-8696-90aef3fc5ec4",
+   "metadata": {},
+   "source": [
+    "## Attack Primitives\n",
+    "\n",
+    "Each function:\n",
+    "- Takes a PIL Image\n",
+    "- Returns a new PIL Image\n",
+    "- Does **not** modify the original"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6027902-897a-4a3b-a806-e715fea43050",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def jpeg_attack(image: Image.Image, quality: int) -> Image.Image:\n",
+    "    \"\"\"\n",
+    "    Simulate JPEG recompression\n",
+    "    \"\"\"\n",
+    "    buf = BytesIO()\n",
+    "    image.save(buf, \n",
+    "               format  = \"JPEG\", \n",
+    "               quality = quality,\n",
+    "              )\n",
+    "    \n",
+    "    buf.seek(0)\n",
+    "    return Image.open(buf).convert(\"RGB\")\n",
+    "\n",
+    "\n",
+    "def resize_attack(image: Image.Image, scale: float) -> Image.Image:\n",
+    "    \"\"\"\n",
+    "    Downscale and upscale image\n",
+    "    \"\"\"\n",
+    "    w, h         = image.size\n",
+    "    new_w, new_h = int(w * scale), int(h * scale)\n",
+    "    image_small  = image.resize((new_w, new_h), Image.BICUBIC)\n",
+    "    \n",
+    "    return image_small.resize((w, h), Image.BICUBIC)\n",
+    "\n",
+    "\n",
+    "def blur_attack(image: Image.Image, radius: float) -> Image.Image:\n",
+    "    \"\"\"\n",
+    "    Apply Gaussian blur\n",
+    "    \"\"\"\n",
+    "    return image.filter(ImageFilter.GaussianBlur(radius))\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62d3ca44-b497-4397-bd35-04db9041d1e4",
+   "metadata": {},
+   "source": [
+    "## Attack Application Pipeline\n",
+    "\n",
+    "For each original image:\n",
+    "- Apply all attack variants\n",
+    "- Save attacked images\n",
+    "- Write **attack-aware metadata**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c702ab79-68b1-4191-8e87-f26ad0227348",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def apply_attacks(records, writer):\n",
+    "    for r in tqdm(records):\n",
+    "        src_path = Path(r[\"filename\"])\n",
+    "        \n",
+    "        if not src_path.exists():\n",
+    "            continue\n",
+    "\n",
+    "        try:\n",
+    "            img = Image.open(src_path).convert(\"RGB\")\n",
+    "            \n",
+    "        except Exception:\n",
+    "            continue\n",
+    "\n",
+    "        base_name = src_path.stem\n",
+    "        label     = r[\"label\"]\n",
+    "\n",
+    "        out_base  = ATTACK_DIR / r[\"source\"]\n",
+    "        out_base.mkdir(parents = True, exist_ok = True)\n",
+    "\n",
+    "        # --- JPEG ---\n",
+    "        for q in JPEG_QUALITIES:\n",
+    "            attacked = jpeg_attack(img, q)\n",
+    "            uid      = uuid.uuid4().hex\n",
+    "            out_path = out_base / f\"{uid}.png\"\n",
+    "\n",
+    "            attacked.save(out_path, optimize = True)\n",
+    "\n",
+    "            writer.writerow({**r,\n",
+    "                             \"id\"        : uid,\n",
+    "                             \"filename\"  : str(out_path),\n",
+    "                             \"attack\"    : f\"jpeg_q{q}\",\n",
+    "                             \"parent_id\" : r[\"id\"]\n",
+    "                           })\n",
+    "\n",
+    "        # --- Resize ---\n",
+    "        for s in RESIZE_SCALES:\n",
+    "            attacked = resize_attack(img, s)\n",
+    "            uid      = uuid.uuid4().hex\n",
+    "            out_path = out_base / f\"{uid}.png\"\n",
+    "\n",
+    "            attacked.save(out_path, optimize = True)\n",
+    "\n",
+    "            writer.writerow({**r,\n",
+    "                             \"id\"        : uid,\n",
+    "                             \"filename\"  : str(out_path),\n",
+    "                             \"attack\"    : f\"resize_{int(s*100)}\",\n",
+    "                             \"parent_id\" : r[\"id\"]\n",
+    "                           })\n",
+    "\n",
+    "        # --- Blur ---\n",
+    "        for b in BLUR_RADII:\n",
+    "            attacked = blur_attack(img, b)\n",
+    "            uid      = uuid.uuid4().hex\n",
+    "            out_path = out_base / f\"{uid}.png\"\n",
+    "\n",
+    "            attacked.save(out_path, optimize = True)\n",
+    "\n",
+    "            writer.writerow({**r,\n",
+    "                             \"id\"        : uid,\n",
+    "                             \"filename\"  : str(out_path),\n",
+    "                             \"attack\"    : f\"blur_{b}\",\n",
+    "                             \"parent_id\" : r[\"id\"]\n",
+    "                           })\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3736496a-7710-4593-86fd-818b2d58d535",
+   "metadata": {},
+   "source": [
+    "## Write Attack Metadata\n",
+    "\n",
+    "We preserve:\n",
+    "- Original label (ai / real)\n",
+    "- Source family\n",
+    "- Parent image ID\n",
+    "- Attack type\n",
+    "\n",
+    "This allows **per-attack evaluation later**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17f29f49-4137-4752-a098-1eba404ce352",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(META_OUT, \"w\", newline = \"\") as f:\n",
+    "    fieldnames = list(records[0].keys()) + [\"attack\", \"parent_id\"]\n",
+    "    writer     = csv.DictWriter(f, fieldnames = fieldnames)\n",
+    "    writer.writeheader()\n",
+    "\n",
+    "    apply_attacks(records, writer)\n",
+    "\n",
+    "print(\"✅ Post-processing attacks generated\")\n",
+    "print(f\"Metadata saved to: {META_OUT}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f20b8f36-af23-49b8-8c6b-d93cf2a7ba07",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/Unified_Dataset_Builder.ipynb

@@ -0,0 +1,797 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e2d654dc-c431-420e-810a-a985de9172fd",
+   "metadata": {},
+   "source": [
+    "# Unified AI vs Real Image Dataset Builder\n",
+    "\n",
+    "This notebook builds a **clean, labeled, unified dataset** for evaluating\n",
+    "AI image detection systems.\n",
+    "\n",
+    "### Supported sources\n",
+    "- HuggingFace datasets (DiffusionDB, COCO, OpenImages)\n",
+    "- Kaggle public datasets (Midjourney, AI vs Real)\n",
+    "- Unified output format:\n",
+    "  - Normalized PNG images\n",
+    "  - Size-limited (≤1024px)\n",
+    "  - Central metadata CSV\n",
+    "\n",
+    "### Output Structure\n",
+    "\n",
+    "```bash\n",
+    "tests/dataset/\n",
+    "├── ai/\n",
+    "├── real/\n",
+    "├── raw_downloads/\n",
+    "├── metadata/dataset_index.csv\n",
+    "```\n",
+    "\n",
+    "> ⚠️ All datasets used are **public & legally accessible**.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8b43897-9ce5-4f20-8798-7b3aebdf1b36",
+   "metadata": {},
+   "source": [
+    "## Required Dependencies\n",
+    "\n",
+    "Before running, ensure:\n",
+    "\n",
+    "```bash\n",
+    "pip install datasets pillow tqdm kaggle pycocotools\n",
+    "```\n",
+    "\n",
+    "Also configure Kaggle:\n",
+    "\n",
+    "```bash\n",
+    "~/.kaggle/kaggle.json\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00b9f50c-6158-47e9-89cf-5c279d9c63bb",
+   "metadata": {},
+   "source": [
+    "## Imports & Config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9147ace7-162f-4b0d-bd6d-0d92b9bad61e",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# ===============================\n",
+    "# Imports & Global Configuration\n",
+    "# ===============================\n",
+    "import os\n",
+    "import csv\n",
+    "import uuid\n",
+    "import subprocess\n",
+    "from PIL import Image\n",
+    "from tqdm import tqdm\n",
+    "from pathlib import Path\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "\n",
+    "# ===============================\n",
+    "# Directory Configuration\n",
+    "# ===============================\n",
+    "BASE_DIR       = Path(\"tests/dataset\")\n",
+    "AI_DIR         = BASE_DIR / \"ai\"\n",
+    "REAL_DIR       = BASE_DIR / \"real\"\n",
+    "RAW_DIR        = BASE_DIR / \"raw_downloads\"\n",
+    "META_DIR       = BASE_DIR / \"metadata\"\n",
+    "\n",
+    "META_FILE      = META_DIR / \"dataset_index.csv\"\n",
+    "\n",
+    "TARGET_PER_DS  = 1000\n",
+    "IMAGE_SIZE_MAX = 1024\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "329d1c09-0e9c-4bc2-8935-bd50941611c8",
+   "metadata": {},
+   "source": [
+    "## Utility Functions\n",
+    "\n",
+    "These helpers:\n",
+    "- Ensure directory structure\n",
+    "- Normalize images (RGB, resize, PNG)\n",
+    "- Write metadata rows safely"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b352e981-e456-40cf-be84-a1eb0f01ea7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ensure_dirs():\n",
+    "    for d in [AI_DIR, REAL_DIR, RAW_DIR, META_DIR]:\n",
+    "        d.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "\n",
+    "def normalize_and_save(image: Image.Image, path: Path):\n",
+    "    \"\"\"\n",
+    "    Normalize image to RGB PNG and limit size\n",
+    "    \"\"\"\n",
+    "    image = image.convert(\"RGB\")\n",
+    "    image.thumbnail((IMAGE_SIZE_MAX, IMAGE_SIZE_MAX))\n",
+    "    image.save(path, \n",
+    "               format   = \"PNG\", \n",
+    "               optimize = True,\n",
+    "              )\n",
+    "\n",
+    "\n",
+    "def write_meta(writer, **row):\n",
+    "    writer.writerow(row)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34c3bc3b-6bb6-414d-b3fe-85bc43d832c7",
+   "metadata": {},
+   "source": [
+    "## Dataset Registry\n",
+    "\n",
+    "Defines **where data comes from** and **how it is labeled**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "74106705-e2d6-411c-8193-8e02f5ee0fdc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# HuggingFace datasets (safe & stable)\n",
+    "AI_DATASETS     = [{\"name\"      : \"diffusiondb\",\n",
+    "                    \"hf_id\"     : \"poloclub/diffusiondb\",\n",
+    "                    \"config\"    : \"2m_first_1k\",\n",
+    "                    \"split\"     : \"train\",\n",
+    "                    \"image_key\" : \"image\",\n",
+    "                    \"label\"     : \"ai\",\n",
+    "                    \"family\"    : \"diffusion\"\n",
+    "                  }]\n",
+    "        \n",
+    "\n",
+    "REAL_DATASETS   = [{\"name\"      : \"mscoco_2017\",\n",
+    "                    \"hf_id\"     : \"shunk031/MSCOCO\",\n",
+    "                    \"hf_kwargs\" : {\"year\": 2017,\n",
+    "                                   \"coco_task\": \"instances\"\n",
+    "                                  },\n",
+    "                    \"split\"     : \"train\",\n",
+    "                    \"image_key\" : \"image\",\n",
+    "                    \"label\"     : \"real\",\n",
+    "                    \"family\"    : \"photographic\",\n",
+    "                    \"streaming\" : False\n",
+    "                  }]\n",
+    "\n",
+    "# Kaggle datasets (public, non-scraped)\n",
+    "KAGGLE_DATASETS = [{\"name\"      : \"ai_vs_real\",\n",
+    "                    \"kaggle_id\" : \"tristanzhang32/ai-generated-images-vs-real-images\",\n",
+    "                    \"label\"     : \"ai\",\n",
+    "                    \"family\"    : \"mixed\"\n",
+    "                   },\n",
+    "                   {\"name\"      : \"midjourney\",\n",
+    "                    \"kaggle_id\" : \"cyanex1702/midjourney-imagesprompt\",\n",
+    "                    \"label\"     : \"ai\",\n",
+    "                    \"family\"    : \"diffusion\"\n",
+    "                   }\n",
+    "                  ]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f4c6f3b-2a35-415b-9a35-ee52fd3d85be",
+   "metadata": {},
+   "source": [
+    "## HuggingFace Dataset Processor\n",
+    "\n",
+    "Loads datasets via `datasets.load_dataset()` and saves images in unified format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a9ea5276-65bb-49f5-a656-c00ceeb1f4d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process_hf_dataset(ds_cfg, root_dir, writer):\n",
+    "    print(f\"\\n▶ Loading HF dataset: {ds_cfg['name']}\")\n",
+    "\n",
+    "    ds      = load_dataset(ds_cfg[\"hf_id\"],\n",
+    "                           **ds_cfg.get(\"hf_kwargs\", {}),\n",
+    "                           name      = ds_cfg.get(\"config\"),\n",
+    "                           split     = ds_cfg[\"split\"],\n",
+    "                           streaming = ds_cfg.get(\"streaming\", False),\n",
+    "                          )\n",
+    "\n",
+    "    out_dir = root_dir / ds_cfg[\"name\"]\n",
+    "    out_dir.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "    count   = 0\n",
+    "    \n",
+    "    for row in tqdm(ds):\n",
+    "        if (count >= TARGET_PER_DS):\n",
+    "            break\n",
+    "\n",
+    "        try:\n",
+    "            image = row.get(ds_cfg[\"image_key\"])\n",
+    "            if not isinstance(image, Image.Image):\n",
+    "                continue\n",
+    "\n",
+    "            uid   = uuid.uuid4().hex\n",
+    "            path  = out_dir / f\"{uid}.png\"\n",
+    "\n",
+    "            normalize_and_save(image, path)\n",
+    "\n",
+    "            write_meta(writer,\n",
+    "                       id       = uid,\n",
+    "                       filename = str(path),\n",
+    "                       label    = ds_cfg[\"label\"],\n",
+    "                       family   = ds_cfg[\"family\"],\n",
+    "                       source   = ds_cfg[\"name\"],\n",
+    "                      )\n",
+    "\n",
+    "            count += 1\n",
+    "\n",
+    "        except Exception:\n",
+    "            continue\n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb6d23a0-fa98-4351-9e4e-99265a51e8ef",
+   "metadata": {},
+   "source": [
+    "## Kaggle Dataset Downloader\n",
+    "\n",
+    "Requires:\n",
+    "- Kaggle account\n",
+    "- ~/.kaggle/kaggle.json configured\n",
+    "\n",
+    "No scraping. Fully legal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c6eca5e6-0469-4af6-8af8-afe3036cb0a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def download_kaggle_dataset(kaggle_id: str, out_dir: Path):\n",
+    "    out_dir.mkdir(parents = True, exist_ok = True)\n",
+    "\n",
+    "    if any(out_dir.iterdir()):\n",
+    "        print(f\"✔ Kaggle dataset already present: {kaggle_id}\")\n",
+    "        return\n",
+    "\n",
+    "    print(f\"⬇ Downloading Kaggle dataset: {kaggle_id}\")\n",
+    "\n",
+    "    subprocess.run([\"kaggle\", \"datasets\", \"download\",\n",
+    "                    kaggle_id,\n",
+    "                    \"-p\", str(out_dir),\n",
+    "                    \"--unzip\"\n",
+    "                   ],\n",
+    "                   check = True,\n",
+    "                  )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c971767-d20a-4fa3-949a-a655d712b2c1",
+   "metadata": {},
+   "source": [
+    "## Folder Ingestor\n",
+    "\n",
+    "Converts **any folder of images** into the unified dataset format. \n",
+    "Used for Kaggle & future web sources."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b648832e-5025-4851-af21-382051167a04",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "IMAGE_EXTS = {\".png\", \".jpg\", \".jpeg\", \".webp\"}\n",
+    "\n",
+    "def ingest_image_folder(src_dir, out_dir, writer, label, family, source):\n",
+    "    images = [p for p in src_dir.rglob(\"*\") if p.suffix.lower() in IMAGE_EXTS]\n",
+    "\n",
+    "    out_dir.mkdir(parents = True, exist_ok = True)\n",
+    "\n",
+    "    for image_path in tqdm(images[:TARGET_PER_DS]):\n",
+    "        try:\n",
+    "            image = Image.open(image_path)\n",
+    "\n",
+    "            uid   = uuid.uuid4().hex\n",
+    "            dst   = out_dir / f\"{uid}.png\"\n",
+    "\n",
+    "            normalize_and_save(image, dst)\n",
+    "\n",
+    "            write_meta(writer,\n",
+    "                       id       = uid,\n",
+    "                       filename = str(dst),\n",
+    "                       label    = label,\n",
+    "                       family   = family,\n",
+    "                       source   = source,\n",
+    "                      )\n",
+    "            \n",
+    "        except Exception:\n",
+    "            continue\n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53fccdc4-e593-4dbf-a71b-e5b826e4a27a",
+   "metadata": {},
+   "source": [
+    "## Main Pipeline Execution\n",
+    "\n",
+    "This cell:\n",
+    "- Builds directories\n",
+    "- Processes HF datasets\n",
+    "- Downloads & ingests Kaggle datasets\n",
+    "- Writes unified metadata CSV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "dd8ef771-f39f-4d9d-8eaf-626ecc211141",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "▶ Loading HF dataset: diffusiondb\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [05:24<00:00,  3.08it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "▶ Loading HF dataset: mscoco_2017\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "15b93e24384a49da9e46dceda9bc3f6b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e34eabfceb61496ebbd9336c9ed060f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/19.3G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[7], line 46\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[38;5;66;03m# ===============================\u001b[39;00m\n\u001b[1;32m     43\u001b[0m \u001b[38;5;66;03m# Entry Point\u001b[39;00m\n\u001b[1;32m     44\u001b[0m \u001b[38;5;66;03m# ===============================\u001b[39;00m\n\u001b[1;32m     45\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__main__\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m---> 46\u001b[0m     \u001b[43mmain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[7], line 13\u001b[0m, in \u001b[0;36mmain\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m     process_hf_dataset(ds, AI_DIR, writer)\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ds \u001b[38;5;129;01min\u001b[39;00m REAL_DATASETS:\n\u001b[0;32m---> 13\u001b[0m     \u001b[43mprocess_hf_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mREAL_DIR\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwriter\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;66;03m# Kaggle datasets\u001b[39;00m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ds \u001b[38;5;129;01min\u001b[39;00m KAGGLE_DATASETS:\n",
+      "Cell \u001b[0;32mIn[4], line 4\u001b[0m, in \u001b[0;36mprocess_hf_dataset\u001b[0;34m(ds_cfg, root_dir, writer)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mprocess_hf_dataset\u001b[39m(ds_cfg, root_dir, writer):\n\u001b[1;32m      2\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m��� Loading HF dataset: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mds_cfg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 4\u001b[0m     ds      \u001b[38;5;241m=\u001b[39m \u001b[43mload_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mds_cfg\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mhf_id\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m                           \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mds_cfg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mhf_kwargs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m                           \u001b[49m\u001b[43mname\u001b[49m\u001b[43m      \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mds_cfg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mconfig\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m                           \u001b[49m\u001b[43msplit\u001b[49m\u001b[43m     \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mds_cfg\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msplit\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[43m                           \u001b[49m\u001b[43mstreaming\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mds_cfg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstreaming\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[43m                          \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m     out_dir \u001b[38;5;241m=\u001b[39m root_dir \u001b[38;5;241m/\u001b[39m ds_cfg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m     12\u001b[0m     out_dir\u001b[38;5;241m.\u001b[39mmkdir(parents\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
+      "File \u001b[0;32m~/.conda/envs/mvp_env/lib/python3.10/site-packages/datasets/load.py:2153\u001b[0m, in \u001b[0;36mload_dataset\u001b[0;34m(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)\u001b[0m\n\u001b[1;32m   2150\u001b[0m try_from_hf_gcs \u001b[38;5;241m=\u001b[39m path \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m _PACKAGED_DATASETS_MODULES\n\u001b[1;32m   2152\u001b[0m \u001b[38;5;66;03m# Download and prepare data\u001b[39;00m\n\u001b[0;32m-> 2153\u001b[0m \u001b[43mbuilder_instance\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2154\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2155\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdownload_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2156\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverification_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverification_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2157\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtry_from_hf_gcs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtry_from_hf_gcs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2158\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_proc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2159\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2160\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2162\u001b[0m \u001b[38;5;66;03m# Build dataset for splits\u001b[39;00m\n\u001b[1;32m   2163\u001b[0m keep_in_memory \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m   2164\u001b[0m     keep_in_memory \u001b[38;5;28;01mif\u001b[39;00m keep_in_memory \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m is_small_dataset(builder_instance\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mdataset_size)\n\u001b[1;32m   2165\u001b[0m )\n",
+      "File \u001b[0;32m~/.conda/envs/mvp_env/lib/python3.10/site-packages/datasets/builder.py:954\u001b[0m, in \u001b[0;36mDatasetBuilder.download_and_prepare\u001b[0;34m(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)\u001b[0m\n\u001b[1;32m    952\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m num_proc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    953\u001b[0m         prepare_split_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnum_proc\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m num_proc\n\u001b[0;32m--> 954\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_download_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    955\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    956\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverification_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverification_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    957\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mprepare_split_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    958\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdownload_and_prepare_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    959\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    960\u001b[0m \u001b[38;5;66;03m# Sync info\u001b[39;00m\n\u001b[1;32m    961\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mdataset_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msum\u001b[39m(split\u001b[38;5;241m.\u001b[39mnum_bytes \u001b[38;5;28;01mfor\u001b[39;00m split \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39msplits\u001b[38;5;241m.\u001b[39mvalues())\n",
+      "File \u001b[0;32m~/.conda/envs/mvp_env/lib/python3.10/site-packages/datasets/builder.py:1717\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._download_and_prepare\u001b[0;34m(self, dl_manager, verification_mode, **prepare_splits_kwargs)\u001b[0m\n\u001b[1;32m   1716\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_download_and_prepare\u001b[39m(\u001b[38;5;28mself\u001b[39m, dl_manager, verification_mode, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mprepare_splits_kwargs):\n\u001b[0;32m-> 1717\u001b[0m     \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_download_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1718\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1719\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverification_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1720\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcheck_duplicate_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverification_mode\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mVerificationMode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mBASIC_CHECKS\u001b[49m\n\u001b[1;32m   1721\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mverification_mode\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mVerificationMode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mALL_CHECKS\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1722\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mprepare_splits_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1723\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.conda/envs/mvp_env/lib/python3.10/site-packages/datasets/builder.py:1027\u001b[0m, in \u001b[0;36mDatasetBuilder._download_and_prepare\u001b[0;34m(self, dl_manager, verification_mode, **prepare_split_kwargs)\u001b[0m\n\u001b[1;32m   1025\u001b[0m split_dict \u001b[38;5;241m=\u001b[39m SplitDict(dataset_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset_name)\n\u001b[1;32m   1026\u001b[0m split_generators_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_split_generators_kwargs(prepare_split_kwargs)\n\u001b[0;32m-> 1027\u001b[0m split_generators \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_split_generators\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msplit_generators_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1029\u001b[0m \u001b[38;5;66;03m# Checksums verification\u001b[39;00m\n\u001b[1;32m   1030\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m verification_mode \u001b[38;5;241m==\u001b[39m VerificationMode\u001b[38;5;241m.\u001b[39mALL_CHECKS \u001b[38;5;129;01mand\u001b[39;00m dl_manager\u001b[38;5;241m.\u001b[39mrecord_checksums:\n",
+      "File \u001b[0;32m~/.cache/huggingface/modules/datasets_modules/datasets/shunk031--MSCOCO/9a9d3cb1e5e1927e03f5448bc4e3dd95d17101d142ba4b94d6973770757f535f/MSCOCO.py:977\u001b[0m, in \u001b[0;36mMsCocoDataset._split_generators\u001b[0;34m(self, dl_manager)\u001b[0m\n\u001b[1;32m    976\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_split_generators\u001b[39m(\u001b[38;5;28mself\u001b[39m, dl_manager: ds\u001b[38;5;241m.\u001b[39mDownloadManager):\n\u001b[0;32m--> 977\u001b[0m     file_paths \u001b[38;5;241m=\u001b[39m \u001b[43mdl_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_and_extract\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_URLS\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43myear\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    979\u001b[0m     imgs \u001b[38;5;241m=\u001b[39m file_paths[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimages\u001b[39m\u001b[38;5;124m\"\u001b[39m]  \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m    980\u001b[0m     anns \u001b[38;5;241m=\u001b[39m file_paths[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mannotations\u001b[39m\u001b[38;5;124m\"\u001b[39m]  \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n",
+      "File \u001b[0;32m~/.conda/envs/mvp_env/lib/python3.10/site-packages/datasets/download/download_manager.py:565\u001b[0m, in \u001b[0;36mDownloadManager.download_and_extract\u001b[0;34m(self, url_or_urls)\u001b[0m\n\u001b[1;32m    549\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mdownload_and_extract\u001b[39m(\u001b[38;5;28mself\u001b[39m, url_or_urls):\n\u001b[1;32m    550\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Download and extract given `url_or_urls`.\u001b[39;00m\n\u001b[1;32m    551\u001b[0m \n\u001b[1;32m    552\u001b[0m \u001b[38;5;124;03m    Is roughly equivalent to:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    563\u001b[0m \u001b[38;5;124;03m        extracted_path(s): `str`, extracted paths of given URL(s).\u001b[39;00m\n\u001b[1;32m    564\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 565\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mextract(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl_or_urls\u001b[49m\u001b[43m)\u001b[49m)\n",
+      "File \u001b[0;32m~/.conda/envs/mvp_env/lib/python3.10/site-packages/datasets/download/download_manager.py:428\u001b[0m, in \u001b[0;36mDownloadManager.download\u001b[0;34m(self, url_or_urls)\u001b[0m\n\u001b[1;32m    425\u001b[0m download_func \u001b[38;5;241m=\u001b[39m partial(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_download, download_config\u001b[38;5;241m=\u001b[39mdownload_config)\n\u001b[1;32m    427\u001b[0m start_time \u001b[38;5;241m=\u001b[39m datetime\u001b[38;5;241m.\u001b[39mnow()\n\u001b[0;32m--> 428\u001b[0m downloaded_path_or_paths \u001b[38;5;241m=\u001b[39m \u001b[43mmap_nested\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    429\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdownload_func\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    430\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl_or_urls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    431\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmap_tuple\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    432\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnum_proc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    433\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdisable_tqdm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mis_progress_bar_enabled\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    434\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdesc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mDownloading data files\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    435\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    436\u001b[0m duration \u001b[38;5;241m=\u001b[39m datetime\u001b[38;5;241m.\u001b[39mnow() \u001b[38;5;241m-\u001b[39m start_time\n\u001b[1;32m    437\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDownloading took \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mduration\u001b[38;5;241m.\u001b[39mtotal_seconds()\u001b[38;5;250m \u001b[39m\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;241m60\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m min\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m~/.conda/envs/mvp_env/lib/python3.10/site-packages/datasets/utils/py_utils.py:464\u001b[0m, in \u001b[0;36mmap_nested\u001b[0;34m(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, types, disable_tqdm, desc)\u001b[0m\n\u001b[1;32m    462\u001b[0m     num_proc \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    463\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_proc \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m num_proc \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(iterable) \u001b[38;5;241m<\u001b[39m parallel_min_length:\n\u001b[0;32m--> 464\u001b[0m     mapped \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    465\u001b[0m         _single_map_nested((function, obj, types, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    466\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m logging\u001b[38;5;241m.\u001b[39mtqdm(iterable, disable\u001b[38;5;241m=\u001b[39mdisable_tqdm, desc\u001b[38;5;241m=\u001b[39mdesc)\n\u001b[1;32m    467\u001b[0m     ]\n\u001b[1;32m    468\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    469\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m warnings\u001b[38;5;241m.\u001b[39mcatch_warnings():\n",
+      "File \u001b[0;32m~/.conda/envs/mvp_env/lib/python3.10/site-packages/datasets/utils/py_utils.py:465\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    462\u001b[0m     num_proc \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    463\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_proc \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m num_proc \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(iterable) \u001b[38;5;241m<\u001b[39m parallel_min_length:\n\u001b[1;32m    464\u001b[0m     mapped \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m--> 465\u001b[0m         \u001b[43m_single_map_nested\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtypes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    466\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m logging\u001b[38;5;241m.\u001b[39mtqdm(iterable, disable\u001b[38;5;241m=\u001b[39mdisable_tqdm, desc\u001b[38;5;241m=\u001b[39mdesc)\n\u001b[1;32m    467\u001b[0m     ]\n\u001b[1;32m    468\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    469\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m warnings\u001b[38;5;241m.\u001b[39mcatch_warnings():\n",
+      "File \u001b[0;32m~/.conda/envs/mvp_env/lib/python3.10/site-packages/datasets/utils/py_utils.py:382\u001b[0m, in \u001b[0;36m_single_map_nested\u001b[0;34m(args)\u001b[0m\n\u001b[1;32m    380\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m logging\u001b[38;5;241m.\u001b[39mtqdm(pbar_iterable, disable\u001b[38;5;241m=\u001b[39mdisable_tqdm, position\u001b[38;5;241m=\u001b[39mrank, unit\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobj\u001b[39m\u001b[38;5;124m\"\u001b[39m, desc\u001b[38;5;241m=\u001b[39mpbar_desc) \u001b[38;5;28;01mas\u001b[39;00m pbar:\n\u001b[1;32m    381\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_struct, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m--> 382\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m {k: _single_map_nested((function, v, types, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m)) \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m pbar}\n\u001b[1;32m    383\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    384\u001b[0m         mapped \u001b[38;5;241m=\u001b[39m [_single_map_nested((function, v, types, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m)) \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m pbar]\n",
+      "File \u001b[0;32m~/.conda/envs/mvp_env/lib/python3.10/site-packages/datasets/utils/py_utils.py:382\u001b[0m, in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    380\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m logging\u001b[38;5;241m.\u001b[39mtqdm(pbar_iterable, disable\u001b[38;5;241m=\u001b[39mdisable_tqdm, position\u001b[38;5;241m=\u001b[39mrank, unit\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobj\u001b[39m\u001b[38;5;124m\"\u001b[39m, desc\u001b[38;5;241m=\u001b[39mpbar_desc) \u001b[38;5;28;01mas\u001b[39;00m pbar:\n\u001b[1;32m    381\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_struct, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m--> 382\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m {k: \u001b[43m_single_map_nested\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtypes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m pbar}\n\u001b[1;32m    383\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    384\u001b[0m         mapped \u001b[38;5;241m=\u001b[39m [_single_map_nested((function, v, types, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m)) \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m pbar]\n",
+      "File \u001b[0;32m~/.conda/envs/mvp_env/lib/python3.10/site-packages/datasets/utils/py_utils.py:367\u001b[0m, in \u001b[0;36m_single_map_nested\u001b[0;34m(args)\u001b[0m\n\u001b[1;32m    365\u001b[0m \u001b[38;5;66;03m# Singleton first to spare some computation\u001b[39;00m\n\u001b[1;32m    366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_struct, \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_struct, types):\n\u001b[0;32m--> 367\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_struct\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    369\u001b[0m \u001b[38;5;66;03m# Reduce logging to keep things readable in multiprocessing with tqdm\u001b[39;00m\n\u001b[1;32m    370\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m rank \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m logging\u001b[38;5;241m.\u001b[39mget_verbosity() \u001b[38;5;241m<\u001b[39m logging\u001b[38;5;241m.\u001b[39mWARNING:\n",
+      "File \u001b[0;32m~/.conda/envs/mvp_env/lib/python3.10/site-packages/datasets/download/download_manager.py:454\u001b[0m, in \u001b[0;36mDownloadManager._download\u001b[0;34m(self, url_or_filename, download_config)\u001b[0m\n\u001b[1;32m    451\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_relative_path(url_or_filename):\n\u001b[1;32m    452\u001b[0m     \u001b[38;5;66;03m# append the relative path to the base_path\u001b[39;00m\n\u001b[1;32m    453\u001b[0m     url_or_filename \u001b[38;5;241m=\u001b[39m url_or_path_join(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_base_path, url_or_filename)\n\u001b[0;32m--> 454\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcached_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl_or_filename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.conda/envs/mvp_env/lib/python3.10/site-packages/datasets/utils/file_utils.py:182\u001b[0m, in \u001b[0;36mcached_path\u001b[0;34m(url_or_filename, download_config, **download_kwargs)\u001b[0m\n\u001b[1;32m    178\u001b[0m     url_or_filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(url_or_filename)\n\u001b[1;32m    180\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_remote_url(url_or_filename):\n\u001b[1;32m    181\u001b[0m     \u001b[38;5;66;03m# URL, so get it from the cache (downloading if necessary)\u001b[39;00m\n\u001b[0;32m--> 182\u001b[0m     output_path \u001b[38;5;241m=\u001b[39m \u001b[43mget_from_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    183\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl_or_filename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    184\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    185\u001b[0m \u001b[43m        \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforce_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    186\u001b[0m \u001b[43m        \u001b[49m\u001b[43mproxies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    187\u001b[0m \u001b[43m        \u001b[49m\u001b[43mresume_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresume_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    188\u001b[0m \u001b[43m        \u001b[49m\u001b[43muser_agent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43muser_agent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    189\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    190\u001b[0m \u001b[43m        \u001b[49m\u001b[43muse_etag\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43muse_etag\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    191\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    192\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    193\u001b[0m \u001b[43m        \u001b[49m\u001b[43mignore_url_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mignore_url_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    194\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    195\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdownload_desc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_desc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    196\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    197\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(url_or_filename):\n\u001b[1;32m    198\u001b[0m     \u001b[38;5;66;03m# File, and it exists.\u001b[39;00m\n\u001b[1;32m    199\u001b[0m     output_path \u001b[38;5;241m=\u001b[39m url_or_filename\n",
+      "File \u001b[0;32m~/.conda/envs/mvp_env/lib/python3.10/site-packages/datasets/utils/file_utils.py:644\u001b[0m, in \u001b[0;36mget_from_cache\u001b[0;34m(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, token, use_auth_token, ignore_url_params, storage_options, download_desc)\u001b[0m\n\u001b[1;32m    642\u001b[0m         fsspec_get(url, temp_file, storage_options\u001b[38;5;241m=\u001b[39mstorage_options, desc\u001b[38;5;241m=\u001b[39mdownload_desc)\n\u001b[1;32m    643\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 644\u001b[0m         \u001b[43mhttp_get\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    645\u001b[0m \u001b[43m            \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    646\u001b[0m \u001b[43m            \u001b[49m\u001b[43mtemp_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    647\u001b[0m \u001b[43m            \u001b[49m\u001b[43mproxies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    648\u001b[0m \u001b[43m            \u001b[49m\u001b[43mresume_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    649\u001b[0m \u001b[43m            \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    650\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcookies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcookies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    651\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmax_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    652\u001b[0m \u001b[43m            \u001b[49m\u001b[43mdesc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_desc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    653\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    655\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstoring \u001b[39m\u001b[38;5;132;01m{\u001b[39;00murl\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m in cache at \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcache_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    656\u001b[0m shutil\u001b[38;5;241m.\u001b[39mmove(temp_file\u001b[38;5;241m.\u001b[39mname, cache_path)\n",
+      "File \u001b[0;32m~/.conda/envs/mvp_env/lib/python3.10/site-packages/datasets/utils/file_utils.py:419\u001b[0m, in \u001b[0;36mhttp_get\u001b[0;34m(url, temp_file, proxies, resume_size, headers, cookies, timeout, max_retries, desc)\u001b[0m\n\u001b[1;32m    410\u001b[0m total \u001b[38;5;241m=\u001b[39m resume_size \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mint\u001b[39m(content_length) \u001b[38;5;28;01mif\u001b[39;00m content_length \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    411\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m logging\u001b[38;5;241m.\u001b[39mtqdm(\n\u001b[1;32m    412\u001b[0m     unit\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mB\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    413\u001b[0m     unit_scale\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    417\u001b[0m     disable\u001b[38;5;241m=\u001b[39m\u001b[38;5;129;01mnot\u001b[39;00m logging\u001b[38;5;241m.\u001b[39mis_progress_bar_enabled(),\n\u001b[1;32m    418\u001b[0m ) \u001b[38;5;28;01mas\u001b[39;00m progress:\n\u001b[0;32m--> 419\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m response\u001b[38;5;241m.\u001b[39miter_content(chunk_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1024\u001b[39m):\n\u001b[1;32m    420\u001b[0m         progress\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28mlen\u001b[39m(chunk))\n\u001b[1;32m    421\u001b[0m         temp_file\u001b[38;5;241m.\u001b[39mwrite(chunk)\n",
+      "File \u001b[0;32m~/.conda/envs/mvp_env/lib/python3.10/site-packages/requests/models.py:816\u001b[0m, in \u001b[0;36mResponse.iter_content.<locals>.generate\u001b[0;34m()\u001b[0m\n\u001b[1;32m    814\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    815\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 816\u001b[0m         \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw\u001b[38;5;241m.\u001b[39mstream(chunk_size, decode_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m    817\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m ProtocolError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    818\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ChunkedEncodingError(e)\n",
+      "File \u001b[0;32m~/.conda/envs/mvp_env/lib/python3.10/site-packages/urllib3/response.py:1091\u001b[0m, in \u001b[0;36mHTTPResponse.stream\u001b[0;34m(self, amt, decode_content)\u001b[0m\n\u001b[1;32m   1089\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1090\u001b[0m     \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_fp_closed(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_decoded_buffer) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m-> 1091\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mamt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mamt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1093\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m data:\n\u001b[1;32m   1094\u001b[0m             \u001b[38;5;28;01myield\u001b[39;00m data\n",
+      "File \u001b[0;32m~/.conda/envs/mvp_env/lib/python3.10/site-packages/urllib3/response.py:980\u001b[0m, in \u001b[0;36mHTTPResponse.read\u001b[0;34m(self, amt, decode_content, cache_content)\u001b[0m\n\u001b[1;32m    977\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_decoded_buffer) \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m amt:\n\u001b[1;32m    978\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_decoded_buffer\u001b[38;5;241m.\u001b[39mget(amt)\n\u001b[0;32m--> 980\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raw_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mamt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    982\u001b[0m flush_decoder \u001b[38;5;241m=\u001b[39m amt \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m (amt \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m data)\n\u001b[1;32m    984\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m data \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_decoded_buffer) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
+      "File \u001b[0;32m~/.conda/envs/mvp_env/lib/python3.10/site-packages/urllib3/response.py:904\u001b[0m, in \u001b[0;36mHTTPResponse._raw_read\u001b[0;34m(self, amt, read1)\u001b[0m\n\u001b[1;32m    901\u001b[0m fp_closed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclosed\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m    903\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_error_catcher():\n\u001b[0;32m--> 904\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fp_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mamt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mread1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mread1\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m fp_closed \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    905\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m amt \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m amt \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m data:\n\u001b[1;32m    906\u001b[0m         \u001b[38;5;66;03m# Platform-specific: Buggy versions of Python.\u001b[39;00m\n\u001b[1;32m    907\u001b[0m         \u001b[38;5;66;03m# Close the connection when no data is returned\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    912\u001b[0m         \u001b[38;5;66;03m# not properly close the connection in all cases. There is\u001b[39;00m\n\u001b[1;32m    913\u001b[0m         \u001b[38;5;66;03m# no harm in redundantly calling close.\u001b[39;00m\n\u001b[1;32m    914\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp\u001b[38;5;241m.\u001b[39mclose()\n",
+      "File \u001b[0;32m~/.conda/envs/mvp_env/lib/python3.10/site-packages/urllib3/response.py:887\u001b[0m, in \u001b[0;36mHTTPResponse._fp_read\u001b[0;34m(self, amt, read1)\u001b[0m\n\u001b[1;32m    884\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp\u001b[38;5;241m.\u001b[39mread1(amt) \u001b[38;5;28;01mif\u001b[39;00m amt \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp\u001b[38;5;241m.\u001b[39mread1()\n\u001b[1;32m    885\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    886\u001b[0m     \u001b[38;5;66;03m# StringIO doesn't like amt=None\u001b[39;00m\n\u001b[0;32m--> 887\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mamt\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m amt \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp\u001b[38;5;241m.\u001b[39mread()\n",
+      "File \u001b[0;32m~/.conda/envs/mvp_env/lib/python3.10/http/client.py:466\u001b[0m, in \u001b[0;36mHTTPResponse.read\u001b[0;34m(self, amt)\u001b[0m\n\u001b[1;32m    463\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlength \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m amt \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlength:\n\u001b[1;32m    464\u001b[0m     \u001b[38;5;66;03m# clip the read to the \"end of response\"\u001b[39;00m\n\u001b[1;32m    465\u001b[0m     amt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlength\n\u001b[0;32m--> 466\u001b[0m s \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mamt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    467\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m s \u001b[38;5;129;01mand\u001b[39;00m amt:\n\u001b[1;32m    468\u001b[0m     \u001b[38;5;66;03m# Ideally, we would raise IncompleteRead if the content-length\u001b[39;00m\n\u001b[1;32m    469\u001b[0m     \u001b[38;5;66;03m# wasn't satisfied, but it might break compatibility.\u001b[39;00m\n\u001b[1;32m    470\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_close_conn()\n",
+      "File \u001b[0;32m~/.conda/envs/mvp_env/lib/python3.10/socket.py:717\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    715\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m    716\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 717\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecv_into\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    718\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m timeout:\n\u001b[1;32m    719\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_timeout_occurred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "def main():\n",
+    "    ensure_dirs()\n",
+    "\n",
+    "    with open(META_FILE, \"w\", newline = \"\") as f:\n",
+    "        writer = csv.DictWriter(f, fieldnames=[\"id\", \"filename\", \"label\", \"family\", \"source\"])\n",
+    "        writer.writeheader()\n",
+    "\n",
+    "        # HuggingFace datasets\n",
+    "        for ds in AI_DATASETS:\n",
+    "            process_hf_dataset(ds, AI_DIR, writer)\n",
+    "\n",
+    "        for ds in REAL_DATASETS:\n",
+    "            process_hf_dataset(ds, REAL_DIR, writer)\n",
+    "\n",
+    "        # Kaggle datasets\n",
+    "        for ds in KAGGLE_DATASETS:\n",
+    "            raw_path = RAW_DIR / ds[\"name\"]\n",
+    "            download_kaggle_dataset(ds[\"kaggle_id\"], raw_path)\n",
+    "\n",
+    "            # AI images\n",
+    "            ingest_image_folder(src_dir = raw_path / \"ai\",\n",
+    "                                out_dir = AI_DIR / ds[\"name\"],\n",
+    "                                writer  = writer,\n",
+    "                                label   = \"ai\",\n",
+    "                                family  = ds[\"family\"],\n",
+    "                                source  = ds[\"name\"],\n",
+    "                               )\n",
+    "\n",
+    "            # REAL images\n",
+    "            ingest_image_folder(src_dir = raw_path / \"real\",\n",
+    "                                out_dir = REAL_DIR / ds[\"name\"],\n",
+    "                                writer  = writer,\n",
+    "                                label   = \"real\",\n",
+    "                                family  = \"photographic\",\n",
+    "                                source  = ds[\"name\"],\n",
+    "                               )\n",
+    "\n",
+    "    print(\"\\n✅ Dataset build complete\")\n",
+    "    print(f\"📄 Metadata saved at: {META_FILE}\")\n",
+    "\n",
+    "\n",
+    "# ===============================\n",
+    "# Entry Point\n",
+    "# ===============================\n",
+    "if __name__ == \"__main__\":\n",
+    "    main()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd6e0834-7757-4daf-a8bc-37d58bc8debd",
+   "metadata": {},
+   "source": [
+    "# Post-Processing Attack Generator\n",
+    "\n",
+    "This notebook applies **real-world post-processing attacks** to an existing\n",
+    "image dataset to evaluate robustness of AI-image detectors.\n",
+    "\n",
+    "### Attacks Implemented\n",
+    "- JPEG recompression (quality loss)\n",
+    "- Resize / rescale (down + up)\n",
+    "- Gaussian blur\n",
+    "\n",
+    "### Why this matters\n",
+    "Most AI images in the wild are:\n",
+    "- Screenshot\n",
+    "- Re-encoded\n",
+    "- Uploaded to social media\n",
+    "- Slightly blurred or resized\n",
+    "\n",
+    "If a detector fails here, it fails in production."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd680866-0f5c-4930-9262-5521317044fd",
+   "metadata": {},
+   "source": [
+    "## Imports & Config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b62168b8-aa38-47c6-8a00-0bb31e8774fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ===============================\n",
+    "# Imports\n",
+    "# ===============================\n",
+    "\n",
+    "import csv\n",
+    "import uuid\n",
+    "from PIL import Image\n",
+    "from tqdm import tqdm\n",
+    "from io import BytesIO\n",
+    "from pathlib import Path\n",
+    "from PIL import ImageFilter\n",
+    "\n",
+    "\n",
+    "# ===============================\n",
+    "# Configuration\n",
+    "# ===============================\n",
+    "\n",
+    "BASE_DIR       = Path(\"tests/dataset\")\n",
+    "ATTACK_DIR     = BASE_DIR / \"attacked\"\n",
+    "META_IN        = BASE_DIR / \"metadata/dataset_index.csv\"\n",
+    "META_OUT       = BASE_DIR / \"metadata/dataset_index_attacked.csv\"\n",
+    "\n",
+    "ATTACK_DIR.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "JPEG_QUALITIES = [95, 75, 50]\n",
+    "RESIZE_SCALES  = [0.75, 0.5]\n",
+    "BLUR_RADII     = [0.8, 1.5]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c1de132-8245-42c7-9a82-63d6f0c27270",
+   "metadata": {},
+   "source": [
+    "## Load Existing Metadata\n",
+    "\n",
+    "We read the existing unified dataset index and create\n",
+    "new samples **derived from originals**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a49e5629-ba32-4736-b0ab-e81084f58b78",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_metadata(path):\n",
+    "    with open(path, newline=\"\") as f:\n",
+    "        return list(csv.DictReader(f))\n",
+    "\n",
+    "\n",
+    "records = load_metadata(META_IN)\n",
+    "print(f\"Loaded {len(records)} original samples\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44a0e31a-abdf-4564-8696-90aef3fc5ec4",
+   "metadata": {},
+   "source": [
+    "## Attack Primitives\n",
+    "\n",
+    "Each function:\n",
+    "- Takes a PIL Image\n",
+    "- Returns a new PIL Image\n",
+    "- Does **not** modify the original"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6027902-897a-4a3b-a806-e715fea43050",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def jpeg_attack(image: Image.Image, quality: int) -> Image.Image:\n",
+    "    \"\"\"\n",
+    "    Simulate JPEG recompression\n",
+    "    \"\"\"\n",
+    "    buf = BytesIO()\n",
+    "    image.save(buf, \n",
+    "               format  = \"JPEG\", \n",
+    "               quality = quality,\n",
+    "              )\n",
+    "    \n",
+    "    buf.seek(0)\n",
+    "    return Image.open(buf).convert(\"RGB\")\n",
+    "\n",
+    "\n",
+    "def resize_attack(image: Image.Image, scale: float) -> Image.Image:\n",
+    "    \"\"\"\n",
+    "    Downscale and upscale image\n",
+    "    \"\"\"\n",
+    "    w, h         = image.size\n",
+    "    new_w, new_h = int(w * scale), int(h * scale)\n",
+    "    image_small  = image.resize((new_w, new_h), Image.BICUBIC)\n",
+    "    \n",
+    "    return image_small.resize((w, h), Image.BICUBIC)\n",
+    "\n",
+    "\n",
+    "def blur_attack(image: Image.Image, radius: float) -> Image.Image:\n",
+    "    \"\"\"\n",
+    "    Apply Gaussian blur\n",
+    "    \"\"\"\n",
+    "    return image.filter(ImageFilter.GaussianBlur(radius))\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62d3ca44-b497-4397-bd35-04db9041d1e4",
+   "metadata": {},
+   "source": [
+    "## Attack Application Pipeline\n",
+    "\n",
+    "For each original image:\n",
+    "- Apply all attack variants\n",
+    "- Save attacked images\n",
+    "- Write **attack-aware metadata**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c702ab79-68b1-4191-8e87-f26ad0227348",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def apply_attacks(records, writer):\n",
+    "    for r in tqdm(records):\n",
+    "        src_path = Path(r[\"filename\"])\n",
+    "        \n",
+    "        if not src_path.exists():\n",
+    "            continue\n",
+    "\n",
+    "        try:\n",
+    "            img = Image.open(src_path).convert(\"RGB\")\n",
+    "            \n",
+    "        except Exception:\n",
+    "            continue\n",
+    "\n",
+    "        base_name = src_path.stem\n",
+    "        label     = r[\"label\"]\n",
+    "\n",
+    "        out_base  = ATTACK_DIR / r[\"source\"]\n",
+    "        out_base.mkdir(parents = True, exist_ok = True)\n",
+    "\n",
+    "        # --- JPEG ---\n",
+    "        for q in JPEG_QUALITIES:\n",
+    "            attacked = jpeg_attack(img, q)\n",
+    "            uid      = uuid.uuid4().hex\n",
+    "            out_path = out_base / f\"{uid}.png\"\n",
+    "\n",
+    "            attacked.save(out_path, optimize = True)\n",
+    "\n",
+    "            writer.writerow({**r,\n",
+    "                             \"id\"        : uid,\n",
+    "                             \"filename\"  : str(out_path),\n",
+    "                             \"attack\"    : f\"jpeg_q{q}\",\n",
+    "                             \"parent_id\" : r[\"id\"]\n",
+    "                           })\n",
+    "\n",
+    "        # --- Resize ---\n",
+    "        for s in RESIZE_SCALES:\n",
+    "            attacked = resize_attack(img, s)\n",
+    "            uid      = uuid.uuid4().hex\n",
+    "            out_path = out_base / f\"{uid}.png\"\n",
+    "\n",
+    "            attacked.save(out_path, optimize = True)\n",
+    "\n",
+    "            writer.writerow({**r,\n",
+    "                             \"id\"        : uid,\n",
+    "                             \"filename\"  : str(out_path),\n",
+    "                             \"attack\"    : f\"resize_{int(s*100)}\",\n",
+    "                             \"parent_id\" : r[\"id\"]\n",
+    "                           })\n",
+    "\n",
+    "        # --- Blur ---\n",
+    "        for b in BLUR_RADII:\n",
+    "            attacked = blur_attack(img, b)\n",
+    "            uid      = uuid.uuid4().hex\n",
+    "            out_path = out_base / f\"{uid}.png\"\n",
+    "\n",
+    "            attacked.save(out_path, optimize = True)\n",
+    "\n",
+    "            writer.writerow({**r,\n",
+    "                             \"id\"        : uid,\n",
+    "                             \"filename\"  : str(out_path),\n",
+    "                             \"attack\"    : f\"blur_{b}\",\n",
+    "                             \"parent_id\" : r[\"id\"]\n",
+    "                           })\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3736496a-7710-4593-86fd-818b2d58d535",
+   "metadata": {},
+   "source": [
+    "## Write Attack Metadata\n",
+    "\n",
+    "We preserve:\n",
+    "- Original label (ai / real)\n",
+    "- Source family\n",
+    "- Parent image ID\n",
+    "- Attack type\n",
+    "\n",
+    "This allows **per-attack evaluation later**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17f29f49-4137-4752-a098-1eba404ce352",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(META_OUT, \"w\", newline = \"\") as f:\n",
+    "    fieldnames = list(records[0].keys()) + [\"attack\", \"parent_id\"]\n",
+    "    writer     = csv.DictWriter(f, fieldnames = fieldnames)\n",
+    "    writer.writeheader()\n",
+    "\n",
+    "    apply_attacks(records, writer)\n",
+    "\n",
+    "print(\"✅ Post-processing attacks generated\")\n",
+    "print(f\"Metadata saved to: {META_OUT}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f20b8f36-af23-49b8-8c6b-d93cf2a7ba07",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

reporter/csv_reporter.py

@@ -0,0 +1,462 @@
+# Dependencies
+import csv
+from pathlib import Path
+from typing import Optional
+from datetime import datetime
+from utils.logger import get_logger
+from config.settings import settings
+from config.constants import MetricType
+from config.schemas import AnalysisResult
+from utils.helpers import generate_unique_id
+from config.constants import DetectionStatus
+from config.schemas import BatchAnalysisResult
+from features.detailed_result_maker import DetailedResultMaker
+
+
+# Setup Logging
+logger = get_logger(__name__)
+
+
+class CSVReporter:
+    """
+    Professional CSV report generator
+    
+    Features:
+    ---------
+    - Single image detailed reports
+    - Batch summary reports with statistics
+    - Detailed forensic data export
+    - Excel-compatible formatting
+    - UTF-8 encoding with BOM for international compatibility
+    """
+    def __init__(self):
+        """
+        Initialize CSV Reporter
+        """
+        self.detailed_maker = DetailedResultMaker()
+        logger.debug("CSVReporter initialized")
+    
+
+    def export_batch_summary(self, batch_result: BatchAnalysisResult, output_dir: Optional[Path] = None) -> Path:
+        """
+        Export batch analysis summary as CSV
+        
+        Arguments:
+        ----------
+            batch_result { BatchAnalysisResult } : Complete batch analysis result
+            
+            output_dir   { Path }                : Output directory (defaults to settings.REPORTS_DIR)
+        
+        Returns:
+        --------
+                       { Path }                  : Path to generated CSV file
+        """
+        output_dir  = output_dir or settings.REPORTS_DIR
+        report_id   = generate_unique_id()
+        filename    = f"batch_summary_{report_id}.csv"
+        output_path = output_dir / filename
+        
+        logger.info(f"Generating batch summary CSV: {filename}")
+        
+        try:
+            with open(output_path, 'w', newline = '', encoding = 'utf-8-sig') as f:
+                writer = csv.writer(f)
+                
+                # Report Header
+                self._write_report_header(writer      = writer,
+                                          report_type = "Batch Analysis Summary",
+                                          timestamp   = batch_result.timestamp,
+                                         )
+                
+                # Batch Statistics
+                self._write_batch_statistics(writer       = writer,
+                                             batch_result = batch_result,
+                                            )
+                
+                # Main Results Table
+                self._write_batch_results_table(writer       = writer,
+                                                batch_result = batch_result,
+                                               )
+                
+                # Footer
+                self._write_footer(writer = writer)
+            
+            logger.info(f"Batch summary CSV generated: {output_path}")
+            return output_path
+            
+        except Exception as e:
+            logger.error(f"Failed to generate batch summary CSV: {e}")
+            raise
+    
+
+    def export_batch_detailed(self, batch_result: BatchAnalysisResult, output_dir: Optional[Path] = None) -> Path:
+        """
+        Export detailed batch analysis with forensic data
+        
+        Arguments:
+        ----------
+            batch_result { BatchAnalysisResult } : Complete batch analysis result
+            
+            output_dir   { Path }                : Output directory (defaults to settings.REPORTS_DIR)
+        
+        Returns:
+        --------
+                      { Path }                   : Path to generated CSV file
+        """
+        output_dir  = output_dir or settings.REPORTS_DIR
+        report_id   = generate_unique_id()
+        filename    = f"batch_detailed_{report_id}.csv"
+        output_path = output_dir / filename
+        
+        logger.info(f"Generating detailed batch CSV: {filename}")
+        
+        try:
+            with open(output_path, 'w', newline = '', encoding = 'utf-8-sig') as f:
+                writer = csv.writer(f)
+                
+                # Report Header
+                self._write_report_header(writer      = writer,
+                                          report_type = "Detailed Batch Analysis",
+                                          timestamp   = batch_result.timestamp,
+                                         )
+                
+                # Process each image with full details
+                for idx, result in enumerate(batch_result.results, 1):
+                    self._write_detailed_image_section(writer        = writer,
+                                                       result        = result,
+                                                       image_number  = idx,
+                                                       total_images  = batch_result.processed,
+                                                      )
+                    
+                    # Add separator between images
+                    if (idx < batch_result.processed):
+                        writer.writerow([])
+                        writer.writerow(['=' * 100])
+                        writer.writerow([])
+                
+                # Footer
+                self._write_footer(writer = writer)
+            
+            logger.info(f"Detailed batch CSV generated: {output_path}")
+            return output_path
+            
+        except Exception as e:
+            logger.error(f"Failed to generate detailed batch CSV: {e}")
+            raise
+    
+
+    def export_single_detailed(self, result: AnalysisResult, output_dir: Optional[Path] = None) -> Path:
+        """
+        Export single image detailed analysis as CSV
+        
+        Arguments:
+        ----------
+            result     { AnalysisResult } : Single image analysis result
+            
+            output_dir { Path }           : Output directory (defaults to settings.REPORTS_DIR)
+        
+        Returns:
+        --------
+                     { Path }             : Path to generated CSV file
+        """
+        output_dir  = output_dir or settings.REPORTS_DIR
+        report_id   = generate_unique_id()
+        filename    = f"single_analysis_{report_id}.csv"
+        output_path = output_dir / filename
+        
+        logger.info(f"Generating single image CSV: {filename}")
+        
+        try:
+            with open(output_path, 'w', newline = '', encoding = 'utf-8-sig') as f:
+                writer = csv.writer(f)
+                
+                # Report Header
+                self._write_report_header(writer     = writer,
+                                          report_type = "Single Image Analysis",
+                                          timestamp   = result.timestamp,
+                                         )
+                
+                # Image Details
+                self._write_detailed_image_section(writer       = writer,
+                                                   result       = result,
+                                                   image_number = 1,
+                                                   total_images = 1,
+                                                  )
+                
+                # Footer
+                self._write_footer(writer = writer)
+            
+            logger.info(f"Single image CSV generated: {output_path}")
+            return output_path
+            
+        except Exception as e:
+            logger.error(f"Failed to generate single image CSV: {e}")
+            raise
+    
+
+    def export_metrics_comparison(self, batch_result: BatchAnalysisResult, output_dir: Optional[Path] = None) -> Path:
+        """
+        Export metrics comparison table across all images
+        
+        Arguments:
+        ----------
+            batch_result { BatchAnalysisResult } : Complete batch analysis result
+            
+            output_dir   { Path }                : Output directory (defaults to settings.REPORTS_DIR)
+        
+        Returns:
+        --------
+                       { Path }                  : Path to generated CSV file
+        """
+        output_dir  = output_dir or settings.REPORTS_DIR
+        report_id   = generate_unique_id()
+        filename    = f"metrics_comparison_{report_id}.csv"
+        output_path = output_dir / filename
+        
+        logger.info(f"Generating metrics comparison CSV: {filename}")
+        
+        try:
+            with open(output_path, 'w', newline = '', encoding = 'utf-8-sig') as f:
+                writer = csv.writer(f)
+                
+                # Report Header
+                self._write_report_header(writer     = writer,
+                                          report_type = "Metrics Comparison",
+                                          timestamp   = batch_result.timestamp,
+                                         )
+                
+                # Comparison Table Header
+                writer.writerow(['Metrics Comparison Across All Images'])
+                writer.writerow([])
+                
+                header = ['Filename', 
+                          'Overall Score', 
+                          'Analysis Status', 
+                          'Gradient Analysis Score', 
+                          'Gradient Analysis Confidence', 
+                          'Frequency Analysis Score', 
+                          'Frequency Analysis Confidence',
+                          'Noise Analysis Score', 
+                          'Noise Analysis Confidence',
+                          'Texture Analysis Score', 
+                          'Texture Analysis Confidence',
+                          'Color Analysis Score', 
+                          'Color Analysis Confidence',
+                          'Processing Time',
+                         ]
+                
+                writer.writerow(header)
+                
+                # Data rows
+                for result in batch_result.results:
+                    row = [result.filename,
+                           f"{result.overall_score:.3f}",
+                           result.status.value,
+                          ]
+                    
+                    # Add each metric's score and confidence
+                    for metric_type in [MetricType.GRADIENT, MetricType.FREQUENCY, MetricType.NOISE, MetricType.TEXTURE, MetricType.COLOR]:
+                        metric_result = result.metric_results.get(metric_type)
+                        
+                        if metric_result:
+                            row.append(f"{metric_result.score:.3f}")
+                            row.append(f"{metric_result.confidence:.3f}" if metric_result.confidence is not None else "N/A")
+                        
+                        else:
+                            row.extend(["N/A", "N/A"])
+                    
+                    row.append(f"{result.processing_time:.2f}s")
+                    writer.writerow(row)
+                
+                # Footer
+                writer.writerow([])
+                self._write_footer(writer = writer)
+            
+            logger.info(f"Metrics comparison CSV generated: {output_path}")
+            return output_path
+            
+        except Exception as e:
+            logger.error(f"Failed to generate metrics comparison CSV: {e}")
+            raise
+    
+
+    def _write_report_header(self, writer, report_type: str, timestamp: datetime) -> None:
+        """
+        Write CSV report header
+        """
+        writer.writerow(['=' * 100])
+        writer.writerow([f'AI Image Screener - {report_type}'])
+        writer.writerow([f'Generated: {timestamp.strftime("%Y-%m-%d %H:%M:%S")}'])
+        writer.writerow([f'Version: {settings.VERSION}'])
+        writer.writerow(['=' * 100])
+        writer.writerow([])
+    
+
+    def _write_batch_statistics(self, writer, batch_result: BatchAnalysisResult) -> None:
+        """
+        Write batch statistics section
+        """
+        writer.writerow(['BATCH STATISTICS'])
+        writer.writerow([])
+        
+        stats = [['Total Images', batch_result.total_images],
+                 ['Successfully Processed', batch_result.processed],
+                 ['Failed', batch_result.failed],
+                 ['Success Rate', f"{batch_result.summary.get('success_rate', 0)}%"],
+                 ['' , ''],
+                 ['Likely Authentic', batch_result.summary.get('likely_authentic', 0)],
+                 ['Review Required', batch_result.summary.get('review_required', 0)],
+                 ['', ''],
+                 ['Average Score', f"{batch_result.summary.get('avg_score', 0):.3f}"],
+                 ['Average Confidence', f"{batch_result.summary.get('avg_confidence', 0)}%"],
+                 ['Total Processing Time', f"{batch_result.total_processing_time:.2f}s"],
+                 ['Average Time per Image', f"{batch_result.summary.get('avg_proc_time', 0):.2f}s"],
+                ]
+        
+        for row in stats:
+            writer.writerow(row)
+        
+        writer.writerow([])
+        writer.writerow(['=' * 100])
+        writer.writerow([])
+    
+
+    def _write_batch_results_table(self, writer, batch_result: BatchAnalysisResult) -> None:
+        """
+        Write batch results main table
+        """
+        writer.writerow(['ANALYSIS RESULTS'])
+        writer.writerow([])
+        
+        # Table Header
+        header = ['Filename', 
+                  'Image Size',
+                  'Analysis Status', 
+                  'Overall Score', 
+                  'Analysis Confidence (%)', 
+                  'Top Warning Signals', 
+                  'Recommendation', 
+                  'Processing Time (s)', 
+                 ]
+
+        writer.writerow(header)
+        
+        # Data rows
+        for result in batch_result.results:
+            # Get top warning signals
+            top_signals = [s.name for s in result.signals if s.status.value in ['flagged', 'warning']][:2]
+            signals_str = "; ".join(top_signals) if top_signals else "All tests passed"
+            
+            # Recommendation
+            if (result.status == DetectionStatus.REVIEW_REQUIRED):
+                recommendation = "Manual verification recommended"
+
+            else:
+                recommendation = "No further action needed"
+            
+            row = [result.filename,
+                   f"{result.image_size[0]}×{result.image_size[1]}",
+                   result.status.value,
+                   f"{result.overall_score:.3f}",
+                   f"{result.confidence}%",
+                   signals_str,
+                   recommendation,
+                   f"{result.processing_time:.2f}", 
+                  ]
+            
+            writer.writerow(row)
+        
+        writer.writerow([])
+    
+
+    def _write_detailed_image_section(self, writer, result: AnalysisResult, image_number: int, total_images: int) -> None:
+        """
+        Write detailed section for single image
+        """
+        writer.writerow([f'IMAGE {image_number} OF {total_images}'])
+        writer.writerow([])
+        
+        # Basic Information
+        writer.writerow(['BASIC INFORMATION'])
+        writer.writerow(['Filename', result.filename])
+        writer.writerow(['Status', result.status.value])
+        writer.writerow(['Overall Score', f"{result.overall_score:.3f}"])
+        writer.writerow(['Confidence', f"{result.confidence}%"])
+        writer.writerow(['Image Size', f"{result.image_size[0]}×{result.image_size[1]}"])
+        writer.writerow(['Processing Time', f"{result.processing_time:.2f}s"])
+        writer.writerow(['Timestamp', result.timestamp.isoformat()])
+        writer.writerow([])
+        
+        # Detection Signals
+        writer.writerow(['DETECTION SIGNALS'])
+        writer.writerow([])
+        writer.writerow(['Metric Name', 'Metric Score', 'Analysis Status', 'Metric Confidence', 'Metric Explanation'])
+        
+        for signal in result.signals:
+            metric_result  = result.metric_results.get(signal.metric_type)
+            confidence_str = f"{metric_result.confidence:.3f}" if metric_result.confidence is not None else "N/A"
+            
+            writer.writerow([signal.name,
+                             f"{signal.score:.3f}",
+                             signal.status.value.upper(),
+                             confidence_str,
+                             signal.explanation.replace("\n", " "),
+                           ])
+        
+        writer.writerow([])
+        
+        # Detailed Forensics
+        writer.writerow(['FORENSIC DETAILS'])
+        writer.writerow([])
+
+        for metric_type in MetricType:
+            metric_result = result.metric_results.get(metric_type)
+            
+            if not metric_result:
+                continue
+
+            metric_name = self.detailed_maker.metric_display_names.get(metric_type, metric_type.value)
+            
+            writer.writerow([f'--- {metric_name} ---'])
+            writer.writerow(['Score', f"{metric_result.score:.3f}"])
+            writer.writerow(['Confidence', f"{metric_result.confidence:.3f}" if metric_result.confidence is not None else "N/A"])
+            
+            # Write details
+            if metric_result.details:
+                for key, value in metric_result.details.items():
+                    if isinstance(value, dict):
+                        writer.writerow([f"  {key}:", ""])
+                        for sub_key, sub_value in value.items():
+                            writer.writerow([f"    {sub_key}", str(sub_value)])
+                    
+                    else:
+                        writer.writerow([f"  {key}", str(value)])
+            
+            writer.writerow([])
+        
+        # Recommendation
+        writer.writerow(['RECOMMENDATION'])
+        writer.writerow([])
+        
+        if (result.status == DetectionStatus.REVIEW_REQUIRED):
+            writer.writerow(['Action', 'Manual verification recommended'])
+            writer.writerow(['Priority', 'HIGH' if (result.overall_score >= 0.85) else 'MEDIUM'])
+            writer.writerow(['Next Steps', 'Forensic analysis, reverse image search, metadata inspection'])
+        
+        else:
+            writer.writerow(['Action', 'No immediate action needed'])
+            writer.writerow(['Priority', 'LOW'])
+            writer.writerow(['Next Steps', 'Proceed with normal workflow'])
+        
+        writer.writerow([])
+    
+
+    def _write_footer(self, writer) -> None:
+        """
+        Write CSV report footer
+        """
+        writer.writerow(['=' * 100])
+        writer.writerow(['Report generated by AI Image Screener'])
+        writer.writerow(['For questions or support, contact: support@aiimagescreener.com'])
+        writer.writerow(['DISCLAIMER: Results are indicative and should be verified manually for critical applications'])
+        writer.writerow(['=' * 100])

reporter/json_reporter.py

@@ -0,0 +1,349 @@
+# Dependencies
+import json
+from typing import Dict
+from typing import List
+from pathlib import Path
+from typing import Optional
+from datetime import datetime
+from utils.logger import get_logger
+from config.settings import settings
+from config.schemas import AnalysisResult
+from utils.helpers import generate_unique_id
+from config.schemas import BatchAnalysisResult
+from features.detailed_result_maker import DetailedResultMaker
+
+
+# Setup Logging
+logger = get_logger(__name__)
+
+
+class JSONReporter:
+    """
+    Professional JSON report generator
+    
+    Features:
+    ---------
+    - Machine-readable structured format
+    - API-friendly output
+    - Complete data preservation
+    - Pretty-printed for readability
+    - Nested structure for complex data
+    """
+    def __init__(self):
+        """
+        Initialize JSON Reporter
+        """
+        self.detailed_maker = DetailedResultMaker()
+        logger.debug("JSONReporter initialized")
+    
+
+    def export_batch(self, batch_result: BatchAnalysisResult, output_dir: Optional[Path] = None, include_detailed: bool = True) -> Path:
+        """
+        Export batch analysis as JSON
+        
+        Arguments:
+        ----------
+            batch_result     { BatchAnalysisResult } : Complete batch analysis result
+            
+            output_dir       { Path }                : Output directory (defaults to settings.REPORTS_DIR)
+            
+            include_detailed { bool }                : Include detailed forensic data
+        
+        Returns:
+        --------
+                        { Path }                     : Path to generated JSON file
+        """
+        output_dir  = output_dir or settings.REPORTS_DIR
+        report_id   = generate_unique_id()
+        filename    = f"batch_report_{report_id}.json"
+        output_path = output_dir / filename
+
+        output_dir.mkdir(parents = True, exist_ok = True)
+        
+        logger.info(f"Generating batch JSON: {filename}")
+        
+        try:
+            # Build JSON structure
+            data = self._build_batch_json(batch_result     = batch_result,
+                                          include_detailed = include_detailed,
+                                         )
+            
+            # Write to file
+            with open(output_path, 'w', encoding = 'utf-8') as f:
+                json.dump(obj          = data, 
+                          fp           = f, 
+                          indent       = 4, 
+                          ensure_ascii = False, 
+                          default      = str,
+                         )
+            
+            logger.info(f"Batch JSON generated: {output_path}")
+            return output_path
+            
+        except Exception as e:
+            logger.error(f"Failed to generate batch JSON: {e}")
+            raise
+    
+
+    def export_single(self, result: AnalysisResult, output_dir: Optional[Path] = None, include_detailed: bool = True) -> Path:
+        """
+        Export single image analysis as JSON
+        
+        Arguments:
+        ----------
+            result           { AnalysisResult } : Single image analysis result
+            
+            output_dir            { Path }      : Output directory (defaults to settings.REPORTS_DIR)
+            
+            include_detailed      { bool }      : Include detailed forensic data
+        
+        Returns:
+        --------
+                      { Path }                  : Path to generated JSON file
+        """
+        output_dir  = output_dir or settings.REPORTS_DIR
+        report_id   = generate_unique_id()
+        filename    = f"single_report_{report_id}.json"
+        output_path = output_dir / filename
+
+        output_dir.mkdir(parents = True, exist_ok = True)
+        
+        logger.info(f"Generating single image JSON: {filename}")
+        
+        try:
+            # Build JSON structure
+            data = self._build_single_json(result           = result,
+                                           include_detailed = include_detailed,
+                                          )
+            
+            # Write to file
+            with open(output_path, 'w', encoding = 'utf-8') as f:
+                json.dump(obj          = data, 
+                          fp           = f, 
+                          indent       = 4, 
+                          ensure_ascii = False, 
+                          default      = str,
+                         )
+            
+            logger.info(f"Single image JSON generated: {output_path}")
+            return output_path
+            
+        except Exception as e:
+            logger.error(f"Failed to generate single image JSON: {e}")
+            raise
+    
+
+    def export_api_response(self, result: AnalysisResult) -> Dict:
+        """
+        Generate API-friendly JSON response (in-memory, no file)
+        
+        Arguments:
+        ----------
+            result { AnalysisResult } : Analysis result
+        
+        Returns:
+        --------
+                   { dict }           : API response dictionary
+        """
+        return {"success"   : True,
+                "timestamp" : datetime.now().isoformat(),
+                "version"   : settings.VERSION,
+                "data"      : self._build_single_json(result           = result,
+                                                      include_detailed = False,
+                                                     ),
+               }
+    
+
+    def _build_batch_json(self, batch_result: BatchAnalysisResult, include_detailed: bool) -> Dict:
+        """
+        Build complete batch JSON structure
+        """
+        data = {"report_metadata" : self._build_metadata(report_type = "Batch Analysis",
+                                                         timestamp   = batch_result.timestamp,
+                                                        ),
+                "batch_summary"   : self._build_batch_summary(batch_result = batch_result),
+                "results"         : [],
+               }
+        
+        # Add each image result
+        for result in batch_result.results:
+            image_data = self._build_image_data(result           = result,
+                                                include_detailed = include_detailed,
+                                               )
+            data["results"].append(image_data)
+        
+        return data
+    
+
+    def _build_single_json(self, result: AnalysisResult, include_detailed: bool) -> Dict:
+        """
+        Build single image JSON structure
+        """
+        data = {"report_metadata" : self._build_metadata(report_type = "Single Image Analysis",
+                                                         timestamp   = result.timestamp,
+                                                        ),
+                "analysis"        : self._build_image_data(result           = result,
+                                                           include_detailed = include_detailed,
+                                                          ),
+               }
+        
+        return data
+    
+
+    def _build_metadata(self, report_type: str, timestamp: datetime) -> Dict:
+        """
+        Build report metadata section
+        """
+        return {"report_type"    : report_type,
+                "generated_at"   : timestamp.isoformat(),
+                "generator"      : "AI Image Screener",
+                "version"        : settings.VERSION,
+                "format_version" : "1.0",
+               }
+    
+
+    def _build_batch_summary(self, batch_result: BatchAnalysisResult) -> Dict:
+        """
+        Build batch summary section
+        """
+        return {"total_images"          : batch_result.total_images,
+                "processed"             : batch_result.processed,
+                "failed"                : batch_result.failed,
+                "success_rate"          : batch_result.summary.get('success_rate', 0),
+                "statistics"            : {"likely_authentic" : batch_result.summary.get('likely_authentic', 0),
+                                           "review_required"  : batch_result.summary.get('review_required', 0),
+                                           "avg_score"        : batch_result.summary.get('avg_score', 0.0),
+                                           "avg_confidence"   : batch_result.summary.get('avg_confidence', 0),
+                                           "avg_proc_time"    : batch_result.summary.get('avg_proc_time', 0.0),
+                                          },
+                "total_processing_time" : round(batch_result.total_processing_time, 2),
+               }
+    
+
+    def _build_image_data(self, result: AnalysisResult, include_detailed: bool) -> Dict:
+        """
+        Build complete image data structure
+        """
+        image_data = {"filename"     : result.filename,
+                      "status"       : result.status.value,
+                      "overall"      : {"score"           : round(result.overall_score, 3),
+                                        "confidence"      : result.confidence,
+                                        "interpretation"  : self._interpret_score(score = result.overall_score),
+                                       },
+                      "image_info"   : {"size"            : {"width"  : result.image_size[0],
+                                                             "height" : result.image_size[1],
+                                                            },
+                                        "processing_time" : round(result.processing_time, 2),
+                                        "timestamp"       : result.timestamp.isoformat(),
+                                       },
+                      "signals"      : self._build_signals_data(result = result),
+                     }
+        
+        # Add detailed forensics if requested
+        if include_detailed:
+            image_data["forensics"]       = self._build_forensics_data(result = result)
+            image_data["recommendations"] = self._build_recommendations(result = result)
+        
+        return image_data
+    
+
+    def _build_signals_data(self, result: AnalysisResult) -> List[Dict]:
+        """
+        Build signals data structure
+        """
+        signals = list()
+        
+        for signal in result.signals:
+            metric_result = result.metric_results.get(signal.metric_type)
+            
+            signal_data   = {"metric_name" : signal.name,
+                             "metric_type" : signal.metric_type.value,
+                             "score"       : round(signal.score, 3),
+                             "status"      : signal.status.value,
+                             "confidence"  : round(metric_result.confidence, 3) if (metric_result and metric_result.confidence is not None) else None,
+                             "explanation" : signal.explanation,
+                            }
+            
+            signals.append(signal_data)
+        
+        return signals
+    
+
+    def _build_forensics_data(self, result: AnalysisResult) -> Dict:
+        """
+        Build detailed forensics data structure
+        """
+        forensics = dict()
+        
+        for metric_type, metric_result in result.metric_results.items():
+            metric_name                  = self.detailed_maker.metric_display_names.get(metric_type, metric_type.value)
+            
+            forensics[metric_type.value] = {"display_name" : metric_name,
+                                            "score"        : round(metric_result.score, 3),
+                                            "confidence"   : round(metric_result.confidence, 3) if (metric_result and metric_result.confidence is not None) else None,
+                                            "details"      : metric_result.details or {},
+                                            "key_findings" : self.detailed_maker.extract_key_findings(metric_type   = metric_type,
+                                                                                                      metric_result = metric_result,
+                                                                                                     ),
+                                           }
+        
+        return forensics
+    
+
+    def _build_recommendations(self, result: AnalysisResult) -> Dict:
+        """
+        Build recommendations structure
+        """
+        score = result.overall_score
+        
+        if (score >= 0.85):
+            return {"action"      : "Immediate manual verification required",
+                    "priority"    : "HIGH",
+                    "risk_level"  : "CRITICAL",
+                    "next_steps"  : ["Forensic analysis", "Reverse image search", "Metadata inspection"],
+                    "confidence"  : "Very high likelihood of AI generation",
+                   }
+        
+        elif (score >= 0.70):
+            return {"action"      : "Manual verification recommended",
+                    "priority"    : "MEDIUM",
+                    "risk_level"  : "HIGH",
+                    "next_steps"  : ["Visual inspection", "Compare with authentic samples"],
+                    "confidence"  : "High likelihood of AI generation",
+                   }
+        
+        elif (score >= 0.50):
+            return {"action"      : "Optional review suggested",
+                    "priority"    : "LOW",
+                    "risk_level"  : "MEDIUM",
+                    "next_steps"  : ["Verify image source", "Check for inconsistencies"],
+                    "confidence"  : "Moderate indicators present",
+                   }
+        
+        else:
+            return {"action"      : "No immediate action required",
+                    "priority"    : "NONE",
+                    "risk_level"  : "LOW",
+                    "next_steps"  : ["Proceed with normal workflow"],
+                    "confidence"  : "Low likelihood of AI generation",
+                   }
+    
+
+    def _interpret_score(self, score: float) -> str:
+        """
+        Interpret score for human readability
+        """
+        if (score >= 0.85):
+            return "Very high suspicion"
+
+        elif (score >= 0.70):
+            return "High suspicion"
+        
+        elif (score >= 0.50):
+            return "Moderate suspicion"
+        
+        elif (score >= 0.30):
+            return "Low suspicion"
+        
+        else:
+            return "Very low suspicion"

reporter/pdf_reporter.py

@@ -0,0 +1,843 @@
+# Dependencies
+from pathlib import Path
+from typing import Optional, List, Dict, Any
+from datetime import datetime
+from utils.logger import get_logger
+from config.settings import settings
+from reportlab.platypus import Table, Spacer, Paragraph, PageBreak, Image as RLImage
+from reportlab.lib import colors
+from reportlab.lib.pagesizes import A4, LETTER
+from reportlab.lib.enums import TA_LEFT, TA_RIGHT, TA_CENTER, TA_JUSTIFY
+from reportlab.platypus import TableStyle
+from config.schemas import AnalysisResult
+from utils.helpers import generate_unique_id
+from config.constants import DetectionStatus
+from config.schemas import BatchAnalysisResult
+from reportlab.lib.styles import ParagraphStyle, getSampleStyleSheet
+from reportlab.platypus import SimpleDocTemplate
+from features.detailed_result_maker import DetailedResultMaker
+from reportlab.lib.units import inch
+from reportlab.pdfgen import canvas
+import textwrap
+
+
+# Setup Logging
+logger = get_logger(__name__)
+
+
+class PDFReporter:
+    """
+    Professional-Grade PDF Report Generator for AI Image Analysis
+    
+    Features:
+    ---------
+    - Comprehensive single image reports with full forensic details
+    - Multi-page batch reports with executive summary
+    - Enhanced visual hierarchy and color coding
+    - Detailed metric breakdowns with explanations
+    - Professional formatting and layout
+    - Statistical summaries and insights
+    """
+    
+    # Enhanced Color Scheme
+    COLOR_PRIMARY = colors.HexColor('#0D47A1')        # Deep Blue
+    COLOR_SUCCESS = colors.HexColor('#1B5E20')        # Dark Green
+    COLOR_WARNING = colors.HexColor('#E65100')        # Deep Orange
+    COLOR_DANGER = colors.HexColor('#B71C1C')         # Dark Red
+    COLOR_INFO = colors.HexColor('#01579B')           # Light Blue
+    COLOR_NEUTRAL = colors.HexColor('#424242')        # Dark Grey
+    COLOR_HEADER_BG = colors.HexColor('#1565C0')      # Blue
+    COLOR_SUBHEADER_BG = colors.HexColor('#1976D2')   # Lighter Blue
+    COLOR_ALT_ROW = colors.HexColor('#F5F5F5')        # Light Grey
+    COLOR_LIGHT_BLUE = colors.HexColor('#E3F2FD')     # Very Light Blue
+    COLOR_LIGHT_GREEN = colors.HexColor('#E8F5E9')    # Very Light Green
+    COLOR_LIGHT_ORANGE = colors.HexColor('#FFF3E0')   # Very Light Orange
+    COLOR_LIGHT_RED = colors.HexColor('#FFEBEE')      # Very Light Red
+    
+    def __init__(self):
+        self.detailed_maker = DetailedResultMaker()
+        self.styles = self._build_styles()
+        logger.debug("Enhanced PDFReporter initialized")
+
+    def export_single(self, result: AnalysisResult, output_dir: Optional[Path] = None) -> Path:
+        """Export comprehensive single image analysis report"""
+        output_dir = output_dir or settings.REPORTS_DIR
+        output_dir.mkdir(parents=True, exist_ok=True)
+        
+        report_id = generate_unique_id()
+        filename = f"ai_screener_report_{report_id}.pdf"
+        output_path = output_dir / filename
+        
+        logger.info(f"Generating comprehensive single image PDF: {filename}")
+        
+        doc = SimpleDocTemplate(
+            str(output_path),
+            pagesize=LETTER,
+            rightMargin=30,
+            leftMargin=30,
+            topMargin=20,
+            bottomMargin=35
+        )
+        
+        story = []
+        self._add_professional_header(story, "AI Image Analysis Report")
+        self._add_executive_summary_single(story, result)
+        story.append(PageBreak())
+        self._add_detailed_metrics_analysis(story, result)
+        story.append(PageBreak())
+        self._add_forensic_breakdown(story, result)
+        self._add_recommendations(story, result)
+        self._add_professional_footer(story)
+        
+        doc.build(story, onFirstPage=self._add_watermark, onLaterPages=self._add_watermark)
+        logger.info(f"Single image report generated: {output_path}")
+        return output_path
+
+    def export_batch(self, batch_result: BatchAnalysisResult, output_dir: Optional[Path] = None) -> Path:
+        """Export comprehensive batch analysis report"""
+        output_dir = output_dir or settings.REPORTS_DIR
+        output_dir.mkdir(parents=True, exist_ok=True)
+        
+        report_id = generate_unique_id()
+        filename = f"ai_screener_report_{report_id}.pdf"
+        output_path = output_dir / filename
+        
+        num_images = len(batch_result.results)
+        logger.info(f"Generating batch PDF report: {filename} ({num_images} images)")
+        
+        doc = SimpleDocTemplate(
+            str(output_path),
+            pagesize=LETTER,
+            rightMargin=30,
+            leftMargin=30,
+            topMargin=20,
+            bottomMargin=35
+        )
+        
+        story = []
+        self._add_professional_header(story, "Batch Image Analysis Report")
+        self._add_batch_executive_summary(story, batch_result)
+        story.append(PageBreak())
+        self._add_batch_overview_table(story, batch_result.results)
+        story.append(PageBreak())
+        self._add_batch_metrics_analysis(story, batch_result.results)
+        story.append(PageBreak())
+        self._add_individual_results_summary(story, batch_result.results)
+        self._add_batch_recommendations(story, batch_result)
+        self._add_professional_footer(story)
+        
+        doc.build(story, onFirstPage=self._add_watermark, onLaterPages=self._add_watermark)
+        logger.info(f"Batch report generated: {output_path}")
+        return output_path
+
+    def _build_styles(self):
+        """Build comprehensive style definitions"""
+        styles = getSampleStyleSheet()
+        
+        styles.add(ParagraphStyle(
+            name='ReportTitle',
+            fontSize=18,
+            textColor=self.COLOR_PRIMARY,
+            alignment=TA_CENTER,
+            spaceAfter=4,
+            spaceBefore=2,
+            fontName='Helvetica-Bold'
+        ))
+        
+        styles.add(ParagraphStyle(
+            name='ReportSubtitle',
+            fontSize=10,
+            textColor=self.COLOR_NEUTRAL,
+            alignment=TA_CENTER,
+            spaceAfter=6,
+            fontName='Helvetica'
+        ))
+        
+        styles.add(ParagraphStyle(
+            name='SectionTitle',
+            fontSize=13,
+            textColor=self.COLOR_PRIMARY,
+            spaceBefore=10,
+            spaceAfter=6,
+            fontName='Helvetica-Bold'
+        ))
+        
+        styles.add(ParagraphStyle(
+            name='SectionHeader',
+            fontSize=11,
+            textColor=self.COLOR_PRIMARY,
+            spaceBefore=8,
+            spaceAfter=5,
+            fontName='Helvetica-Bold'
+        ))
+        
+        styles.add(ParagraphStyle(
+            name='SubHeader',
+            fontSize=9.5,
+            textColor=self.COLOR_PRIMARY,
+            spaceBefore=5,
+            spaceAfter=3,
+            fontName='Helvetica-Bold'
+        ))
+        
+        styles.add(ParagraphStyle(
+            name='CustomBodyText',
+            fontSize=9,
+            leading=12,
+            alignment=TA_JUSTIFY,
+            spaceAfter=6
+        ))
+        
+        styles.add(ParagraphStyle(
+            name='TableCell',
+            fontSize=8,
+            leading=10
+        ))
+        
+        styles.add(ParagraphStyle(
+            name='TableCellSmall',
+            fontSize=7.5,
+            leading=9
+        ))
+        
+        styles.add(ParagraphStyle(
+            name='TableHeader',
+            fontSize=8.5,
+            textColor=colors.white,
+            fontName='Helvetica-Bold',
+            leading=10,
+            alignment=TA_CENTER
+        ))
+        
+        styles.add(ParagraphStyle(
+            name='Footer',
+            fontSize=7.5,
+            textColor=colors.grey,
+            alignment=TA_CENTER,
+            spaceAfter=2
+        ))
+        
+        styles.add(ParagraphStyle(
+            name='Timestamp',
+            fontSize=8,
+            textColor=self.COLOR_NEUTRAL,
+            alignment=TA_CENTER,
+            spaceAfter=8
+        ))
+        
+        return styles
+
+    def _add_watermark(self, canvas, doc):
+        """Add professional watermark"""
+        canvas.saveState()
+        canvas.setFont('Helvetica-Bold', 70)
+        canvas.setFillColorRGB(0.85, 0.85, 0.85, alpha=0.15)
+        canvas.rotate(45)
+        canvas.drawString(2.5*inch, -0.5*inch, "AI IMAGE SCREENER")
+        canvas.restoreState()
+
+    def _add_professional_header(self, story, title: str):
+        """Professional header with branding"""
+        story.append(Paragraph("🔍 AI IMAGE SCREENER", self.styles['ReportTitle']))
+        story.append(Spacer(1, 3))
+        
+        timestamp_text = f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} | Version: {settings.VERSION}"
+        story.append(Paragraph(timestamp_text, self.styles['Timestamp']))
+        
+        story.append(Paragraph(title, self.styles['SectionTitle']))
+        story.append(Spacer(1, 10))
+
+    def _add_executive_summary_single(self, story, result: AnalysisResult):
+        """Executive summary for single image"""
+        story.append(Paragraph("Executive Summary", self.styles['SectionTitle']))
+        story.append(Spacer(1, 5))
+        
+        # Key findings box
+        status_color = self.COLOR_DANGER if result.status == DetectionStatus.REVIEW_REQUIRED else self.COLOR_SUCCESS
+        status_bg = self.COLOR_LIGHT_RED if result.status == DetectionStatus.REVIEW_REQUIRED else self.COLOR_LIGHT_GREEN
+        status_text = "⚠️ REVIEW REQUIRED" if result.status == DetectionStatus.REVIEW_REQUIRED else "✅ LIKELY AUTHENTIC"
+        
+        key_findings = [
+            [Paragraph("<b>Overall Assessment</b>", self.styles['TableHeader'])],
+            [Paragraph(f"<font size=12 color='{status_color.hexval()}'><b>{status_text}</b></font>", self.styles['CustomBodyText'])],
+            [Paragraph(f"<b>Confidence:</b> {result.confidence}%", self.styles['CustomBodyText'])],
+            [Paragraph(f"<b>Overall Score:</b> {result.overall_score:.4f}", self.styles['CustomBodyText'])]
+        ]
+        
+        findings_table = Table(key_findings, colWidths=[530])
+        findings_table.setStyle(TableStyle([
+            ('BACKGROUND', (0, 0), (-1, 0), self.COLOR_INFO),
+            ('BACKGROUND', (0, 1), (-1, -1), status_bg),
+            ('ALIGN', (0, 0), (-1, -1), 'CENTER'),
+            ('VALIGN', (0, 0), (-1, -1), 'MIDDLE'),
+            ('LEFTPADDING', (0, 0), (-1, -1), 12),
+            ('RIGHTPADDING', (0, 0), (-1, -1), 12),
+            ('TOPPADDING', (0, 0), (-1, -1), 8),
+            ('BOTTOMPADDING', (0, 0), (-1, -1), 8),
+            ('BOX', (0, 0), (-1, -1), 1.5, self.COLOR_PRIMARY)
+        ]))
+        story.append(findings_table)
+        story.append(Spacer(1, 12))
+        
+        # Image information
+        story.append(Paragraph("Image Information", self.styles['SectionHeader']))
+        
+        info_data = [
+            [Paragraph("<b>Property</b>", self.styles['TableHeader']), 
+             Paragraph("<b>Value</b>", self.styles['TableHeader'])],
+            [Paragraph("Filename", self.styles['TableCell']), 
+             Paragraph(result.filename, self.styles['TableCell'])],
+            [Paragraph("Dimensions", self.styles['TableCell']), 
+             Paragraph(f"{result.image_size[0]} × {result.image_size[1]} pixels", self.styles['TableCell'])],
+            [Paragraph("Aspect Ratio", self.styles['TableCell']), 
+             Paragraph(f"{result.image_size[0]/result.image_size[1]:.2f}:1", self.styles['TableCell'])],
+            [Paragraph("Processing Time", self.styles['TableCell']), 
+             Paragraph(f"{result.processing_time:.3f} seconds", self.styles['TableCell'])],
+            [Paragraph("Analysis Date", self.styles['TableCell']), 
+             Paragraph(datetime.now().strftime('%Y-%m-%d %H:%M:%S'), self.styles['TableCell'])]
+        ]
+        
+        info_table = Table(info_data, colWidths=[180, 350])
+        info_table.setStyle(self._get_standard_table_style(len(info_data)))
+        story.append(info_table)
+        story.append(Spacer(1, 12))
+        
+        # Detection signals summary
+        story.append(Paragraph("Detection Signals Summary", self.styles['SectionHeader']))
+        
+        flagged = sum(1 for s in result.signals if s.status.value == 'flagged')
+        warning = sum(1 for s in result.signals if s.status.value == 'warning')
+        passed = sum(1 for s in result.signals if s.status.value == 'passed')
+        
+        signals_data = [
+            [Paragraph("<b>Status</b>", self.styles['TableHeader']), 
+             Paragraph("<b>Count</b>", self.styles['TableHeader']),
+             Paragraph("<b>Percentage</b>", self.styles['TableHeader'])],
+            [Paragraph("🔴 Flagged", self.styles['TableCell']), 
+             Paragraph(f"<font color='red'><b>{flagged}</b></font>", self.styles['TableCell']),
+             Paragraph(f"{flagged/len(result.signals)*100:.1f}%", self.styles['TableCell'])],
+            [Paragraph("🟡 Warning", self.styles['TableCell']), 
+             Paragraph(f"<font color='orange'><b>{warning}</b></font>", self.styles['TableCell']),
+             Paragraph(f"{warning/len(result.signals)*100:.1f}%", self.styles['TableCell'])],
+            [Paragraph("🟢 Passed", self.styles['TableCell']), 
+             Paragraph(f"<font color='green'><b>{passed}</b></font>", self.styles['TableCell']),
+             Paragraph(f"{passed/len(result.signals)*100:.1f}%", self.styles['TableCell'])]
+        ]
+        
+        signals_table = Table(signals_data, colWidths=[200, 165, 165])
+        signals_table.setStyle(self._get_standard_table_style(len(signals_data)))
+        story.append(signals_table)
+
+    def _add_detailed_metrics_analysis(self, story, result: AnalysisResult):
+        """Comprehensive metrics analysis"""
+        story.append(Paragraph("Detailed Metrics Analysis", self.styles['SectionTitle']))
+        story.append(Spacer(1, 8))
+        
+        # All detection signals with full details
+        story.append(Paragraph("Detection Signals Breakdown", self.styles['SectionHeader']))
+        
+        signal_data = [
+            [Paragraph("<b>Metric</b>", self.styles['TableHeader']),
+             Paragraph("<b>Score</b>", self.styles['TableHeader']),
+             Paragraph("<b>Status</b>", self.styles['TableHeader']),
+             Paragraph("<b>Explanation</b>", self.styles['TableHeader'])]
+        ]
+        
+        for signal in result.signals:
+            status_badge = self._get_status_badge_html(signal.status.value)
+            
+            # Wrap long explanations
+            explanation = signal.explanation
+            if len(explanation) > 120:
+                explanation = explanation[:120] + "..."
+            
+            signal_data.append([
+                Paragraph(f"<b>{signal.name}</b>", self.styles['TableCell']),
+                Paragraph(f"{signal.score:.4f}", self.styles['TableCell']),
+                Paragraph(status_badge, self.styles['TableCell']),
+                Paragraph(explanation, self.styles['TableCellSmall'])
+            ])
+        
+        signal_table = Table(signal_data, colWidths=[120, 60, 80, 270])
+        signal_table.setStyle(self._get_signal_table_style(len(signal_data)))
+        story.append(signal_table)
+
+    def _add_forensic_breakdown(self, story, result: AnalysisResult):
+        """Detailed forensic analysis breakdown"""
+        story.append(Paragraph("Forensic Analysis Breakdown", self.styles['SectionTitle']))
+        story.append(Spacer(1, 8))
+        
+        for metric_type, metric_result in result.metric_results.items():
+            metric_name = self.detailed_maker.metric_display_names.get(metric_type, metric_type.value)
+            details = metric_result.details or {}
+            
+            # Skip if error
+            if 'error' in details:
+                continue
+            
+            story.append(Paragraph(metric_name, self.styles['SectionHeader']))
+            
+            # Metric overview
+            overview_data = [
+                [Paragraph("<b>Property</b>", self.styles['TableHeader']), 
+                 Paragraph("<b>Value</b>", self.styles['TableHeader'])],
+                [Paragraph("Score", self.styles['TableCell']), 
+                 Paragraph(f"<b>{metric_result.score:.4f}</b>", self.styles['TableCell'])],
+                [Paragraph("Confidence", self.styles['TableCell']), 
+                 Paragraph(f"{metric_result.confidence:.4f}" if metric_result.confidence else "N/A", self.styles['TableCell'])],
+                [Paragraph("Status", self.styles['TableCell']), 
+                 Paragraph(self._get_metric_status_html(metric_result.score), self.styles['TableCell'])]
+            ]
+            
+            overview_table = Table(overview_data, colWidths=[130, 400])
+            overview_table.setStyle(self._get_standard_table_style(len(overview_data)))
+            story.append(overview_table)
+            story.append(Spacer(1, 5))
+            
+            # Detailed parameters
+            if details and len(details) > 0:
+                story.append(Paragraph("Detailed Parameters:", self.styles['SubHeader']))
+                
+                param_data = [[Paragraph("<b>Parameter</b>", self.styles['TableHeader']), 
+                              Paragraph("<b>Value</b>", self.styles['TableHeader'])]]
+                
+                for key, value in details.items():
+                    if key in ['error', 'reason']:
+                        continue
+                    
+                    if isinstance(value, dict):
+                        for sub_key, sub_value in value.items():
+                            if sub_key not in ['reason', 'error']:
+                                formatted_value = self._format_value(sub_value)
+                                param_data.append([
+                                    Paragraph(f"  └─ {sub_key}", self.styles['TableCellSmall']),
+                                    Paragraph(formatted_value, self.styles['TableCellSmall'])
+                                ])
+                    else:
+                        formatted_value = self._format_value(value)
+                        param_data.append([
+                            Paragraph(key, self.styles['TableCell']),
+                            Paragraph(formatted_value, self.styles['TableCell'])
+                        ])
+                
+                param_table = Table(param_data, colWidths=[200, 330])
+                param_table.setStyle(TableStyle([
+                    ('BACKGROUND', (0, 0), (-1, 0), self.COLOR_SUBHEADER_BG),
+                    ('TEXTCOLOR', (0, 0), (-1, 0), colors.white),
+                    ('GRID', (0, 0), (-1, -1), 0.5, colors.grey),
+                    ('ROWBACKGROUNDS', (0, 1), (-1, -1), [colors.white, self.COLOR_ALT_ROW]),
+                    ('VALIGN', (0, 0), (-1, -1), 'TOP'),
+                    ('LEFTPADDING', (0, 0), (-1, -1), 8),
+                    ('RIGHTPADDING', (0, 0), (-1, -1), 8),
+                    ('TOPPADDING', (0, 0), (-1, -1), 4),
+                    ('BOTTOMPADDING', (0, 0), (-1, -1), 4)
+                ]))
+                story.append(param_table)
+            
+            story.append(Spacer(1, 8))
+
+    def _add_recommendations(self, story, result: AnalysisResult):
+        """Add actionable recommendations"""
+        story.append(Paragraph("Recommendations & Next Steps", self.styles['SectionTitle']))
+        story.append(Spacer(1, 8))
+        
+        if result.status == DetectionStatus.REVIEW_REQUIRED:
+            rec_text = """
+            <b>⚠️ MANUAL REVIEW REQUIRED</b><br/>
+            This image has been flagged for manual review based on multiple detection signals. 
+            Recommended actions:<br/>
+            • Conduct visual inspection by trained personnel<br/>
+            • Cross-reference with source verification<br/>
+            • Consider additional forensic analysis if high stakes<br/>
+            • Document findings for audit trail
+            """
+            rec_color = self.COLOR_LIGHT_RED
+            border_color = self.COLOR_DANGER
+        else:
+            rec_text = """
+            <b>✅ NO IMMEDIATE ACTION REQUIRED</b><br/>
+            This image appears to be authentic based on current analysis. However:<br/>
+            • Continue monitoring for evolving AI techniques<br/>
+            • Consider periodic re-screening for critical assets<br/>
+            • Maintain chain of custody documentation<br/>
+            • Stay updated on latest detection methodologies
+            """
+            rec_color = self.COLOR_LIGHT_GREEN
+            border_color = self.COLOR_SUCCESS
+        
+        rec_table = Table([[Paragraph(rec_text, self.styles['CustomBodyText'])]], colWidths=[530])
+        rec_table.setStyle(TableStyle([
+            ('BACKGROUND', (0, 0), (-1, -1), rec_color),
+            ('BOX', (0, 0), (-1, -1), 2, border_color),
+            ('LEFTPADDING', (0, 0), (-1, -1), 15),
+            ('RIGHTPADDING', (0, 0), (-1, -1), 15),
+            ('TOPPADDING', (0, 0), (-1, -1), 12),
+            ('BOTTOMPADDING', (0, 0), (-1, -1), 12)
+        ]))
+        story.append(rec_table)
+
+    def _add_batch_executive_summary(self, story, batch_result: BatchAnalysisResult):
+        """Executive summary for batch analysis"""
+        story.append(Paragraph("Executive Summary", self.styles['SectionTitle']))
+        story.append(Spacer(1, 8))
+        
+        # Key metrics
+        summary_data = [
+            [Paragraph("<b>Metric</b>", self.styles['TableHeader']), 
+             Paragraph("<b>Value</b>", self.styles['TableHeader']),
+             Paragraph("<b>Details</b>", self.styles['TableHeader'])],
+            [Paragraph("Total Images", self.styles['TableCell']), 
+             Paragraph(f"<b>{batch_result.total_images}</b>", self.styles['TableCell']),
+             Paragraph("Images submitted for analysis", self.styles['TableCellSmall'])],
+            [Paragraph("Successfully Processed", self.styles['TableCell']), 
+             Paragraph(f"<font color='green'><b>{batch_result.processed}</b></font>", self.styles['TableCell']),
+             Paragraph(f"{batch_result.summary.get('success_rate', 0)}% success rate", self.styles['TableCellSmall'])],
+            [Paragraph("Failed", self.styles['TableCell']), 
+             Paragraph(f"<font color='red'><b>{batch_result.failed}</b></font>", self.styles['TableCell']),
+             Paragraph("Processing errors encountered", self.styles['TableCellSmall'])],
+            [Paragraph("Likely Authentic", self.styles['TableCell']), 
+             Paragraph(f"<font color='green'><b>{batch_result.summary.get('likely_authentic', 0)}</b></font>", self.styles['TableCell']),
+             Paragraph("Images passing authenticity checks", self.styles['TableCellSmall'])],
+            [Paragraph("Review Required", self.styles['TableCell']), 
+             Paragraph(f"<font color='red'><b>{batch_result.summary.get('review_required', 0)}</b></font>", self.styles['TableCell']),
+             Paragraph("Images flagged for manual review", self.styles['TableCellSmall'])],
+            [Paragraph("Average Score", self.styles['TableCell']), 
+             Paragraph(f"<b>{batch_result.summary.get('avg_score', 0):.4f}</b>", self.styles['TableCell']),
+             Paragraph("Mean authenticity score across batch", self.styles['TableCellSmall'])],
+            [Paragraph("Average Processing Time", self.styles['TableCell']), 
+             Paragraph(f"<b>{batch_result.summary.get('avg_proc_time', 0):.3f}s</b>", self.styles['TableCell']),
+             Paragraph("Per-image processing duration", self.styles['TableCellSmall'])],
+        ]
+        
+        summary_table = Table(summary_data, colWidths=[150, 130, 250])
+        summary_table.setStyle(self._get_standard_table_style(len(summary_data)))
+        story.append(summary_table)
+
+    def _add_batch_overview_table(self, story, results: List[AnalysisResult]):
+        """Comprehensive batch overview"""
+        story.append(Paragraph("Batch Overview Matrix", self.styles['SectionTitle']))
+        story.append(Spacer(1, 8))
+        
+        header = [
+            Paragraph("<b>#</b>", self.styles['TableHeader']),
+            Paragraph("<b>Filename</b>", self.styles['TableHeader']),
+            Paragraph("<b>Image Size</b>", self.styles['TableHeader']),
+            Paragraph("<b>Score</b>", self.styles['TableHeader']),
+            Paragraph("<b>Status</b>", self.styles['TableHeader']),
+            Paragraph("<b>Top Signal</b>", self.styles['TableHeader']),
+            Paragraph("<b>Time(s)</b>", self.styles['TableHeader'])
+        ]
+        
+        data = [header]
+        
+        for idx, result in enumerate(results, 1):
+            top_signal = max(result.signals, key=lambda s: s.score)
+            status_badge = self._get_status_badge_short(result.status.value)
+            
+            data.append([
+                Paragraph(str(idx), self.styles['TableCell']),
+                Paragraph(result.filename, self.styles['TableCellSmall']),
+                Paragraph(f"{result.image_size[0]}×{result.image_size[1]}", self.styles['TableCellSmall']),
+                Paragraph(f"<b>{result.overall_score:.3f}</b>", self.styles['TableCell']),
+                Paragraph(status_badge, self.styles['TableCellSmall']),
+                Paragraph(f"{top_signal.name}: {top_signal.score:.2f}", self.styles['TableCellSmall']),
+                Paragraph(f"{result.processing_time:.2f}", self.styles['TableCell'])
+            ])
+        
+        table = Table(data, colWidths=[25, 155, 65, 50, 70, 120, 45])
+        table.setStyle(self._get_pivot_table_style(len(data)))
+        story.append(table)
+
+    def _add_batch_metrics_analysis(self, story, results: List[AnalysisResult]):
+        """Detailed metrics analysis for batch"""
+        story.append(Paragraph("Metric-wise Analysis", self.styles['SectionTitle']))
+        story.append(Spacer(1, 8))
+        
+        metric_configs = {
+            'gradient': {
+                'name': 'Gradient-Field PCA Analysis',
+                'keys': ['eigenvalue_ratio', 'gradient_vectors_sampled'],
+                'labels': ['Eigenvalue\nRatio', 'Vectors\nSampled']
+            },
+            'frequency': {
+                'name': 'Frequency Domain Analysis (FFT)',
+                'keys': ['hf_ratio', 'roughness', 'spectral_deviation'],
+                'labels': ['HF Ratio', 'Roughness', 'Spec.\nDeviation']
+            },
+            'noise': {
+                'name': 'Noise Pattern Analysis',
+                'keys': ['mean_noise', 'cv', 'patches_valid'],
+                'labels': ['Mean Noise', 'CV', 'Patches\nValid']
+            },
+            'texture': {
+                'name': 'Texture Statistical Analysis',
+                'keys': ['smooth_ratio', 'contrast_mean', 'entropy_mean'],
+                'labels': ['Smooth\nRatio', 'Mean\nContrast', 'Mean\nEntropy']
+            },
+            'color': {
+                'name': 'Color Distribution Analysis',
+                'keys': ['saturation_stats.mean_saturation', 'saturation_stats.high_sat_ratio'],
+                'labels': ['Mean\nSaturation', 'High Saturation\nRatio']
+            }
+        }
+        
+        for metric_key, config in metric_configs.items():
+            story.append(Paragraph(config['name'], self.styles['SectionHeader']))
+            
+            # Build header
+            header = [
+                Paragraph("<b>#</b>", self.styles['TableHeader']),
+                Paragraph("<b>Filename</b>", self.styles['TableHeader']),
+                Paragraph("<b>Score</b>", self.styles['TableHeader']),
+                Paragraph("<b>Confidence</b>", self.styles['TableHeader'])
+            ]
+            
+            for label in config['labels']:
+                header.append(Paragraph(f"<b>{label}</b>", self.styles['TableHeader']))
+            
+            data = [header]
+            
+            for idx, result in enumerate(results, 1):
+                metric_result = result.metric_results.get(metric_key)
+                if not metric_result:
+                    continue
+                
+                details = metric_result.details or {}
+                
+                row = [
+                    Paragraph(str(idx), self.styles['TableCellSmall']),
+                    Paragraph(result.filename, self.styles['TableCellSmall']),
+                    Paragraph(f"<b>{metric_result.score:.3f}</b>", self.styles['TableCellSmall']),
+                    Paragraph(f"{metric_result.confidence:.2f}" if metric_result.confidence else "N/A", 
+                             self.styles['TableCellSmall'])
+                ]
+                
+                # Extract values
+                for key in config['keys']:
+                    value = self._extract_nested_value(details, key)
+                    formatted_value = self._format_value(value, decimal_places=3)
+                    row.append(Paragraph(formatted_value, self.styles['TableCellSmall']))
+                
+                data.append(row)
+            
+            # Dynamic column widths
+            num_detail_cols = len(config['labels'])
+            detail_col_width = (530 - 25 - 140 - 45 - 35) // num_detail_cols
+            col_widths = [25, 140, 45, 35] + [detail_col_width] * num_detail_cols
+            
+            table = Table(data, colWidths=col_widths)
+            table.setStyle(self._get_pivot_table_style(len(data)))
+            story.append(table)
+            story.append(Spacer(1, 10))
+
+    def _add_individual_results_summary(self, story, results: List[AnalysisResult]):
+        """Individual image summaries in batch"""
+        story.append(Paragraph("Individual Image Summaries", self.styles['SectionTitle']))
+        story.append(Spacer(1, 8))
+        
+        for idx, result in enumerate(results, 1):
+            if idx > 1:
+                story.append(Spacer(1, 12))
+            
+            story.append(Paragraph(f"Image {idx}: {result.filename}", self.styles['SectionHeader']))
+            
+            # Quick stats
+            quick_data = [
+                [Paragraph("<b>Property</b>", self.styles['TableHeader']), 
+                 Paragraph("<b>Value</b>", self.styles['TableHeader'])],
+                [Paragraph("Score", self.styles['TableCell']), 
+                 Paragraph(f"<b>{result.overall_score:.4f}</b>", self.styles['TableCell'])],
+                [Paragraph("Status", self.styles['TableCell']), 
+                 Paragraph(self._get_status_badge_html(result.status.value), self.styles['TableCell'])],
+                [Paragraph("Confidence", self.styles['TableCell']), 
+                 Paragraph(f"{result.confidence}%", self.styles['TableCell'])],
+                [Paragraph("Dimensions", self.styles['TableCell']), 
+                 Paragraph(f"{result.image_size[0]} × {result.image_size[1]}", self.styles['TableCell'])],
+            ]
+            
+            quick_table = Table(quick_data, colWidths=[120, 410])
+            quick_table.setStyle(self._get_standard_table_style(len(quick_data)))
+            story.append(quick_table)
+            story.append(Spacer(1, 5))
+            
+            # Top 3 signals
+            story.append(Paragraph("Top Detection Signals:", self.styles['SubHeader']))
+            
+            top_signals = sorted(result.signals, key=lambda s: s.score, reverse=True)[:3]
+            signal_data = [[
+                Paragraph("<b>Signal</b>", self.styles['TableHeader']),
+                Paragraph("<b>Score</b>", self.styles['TableHeader']),
+                Paragraph("<b>Status</b>", self.styles['TableHeader'])
+            ]]
+            
+            for signal in top_signals:
+                signal_data.append([
+                    Paragraph(signal.name, self.styles['TableCellSmall']),
+                    Paragraph(f"{signal.score:.3f}", self.styles['TableCellSmall']),
+                    Paragraph(self._get_status_badge_html(signal.status.value), self.styles['TableCellSmall'])
+                ])
+            
+            signal_table = Table(signal_data, colWidths=[200, 165, 165])
+            signal_table.setStyle(self._get_standard_table_style(len(signal_data)))
+            story.append(signal_table)
+
+    def _add_batch_recommendations(self, story, batch_result: BatchAnalysisResult):
+        """Batch-level recommendations"""
+        story.append(Paragraph("Batch Analysis Recommendations", self.styles['SectionTitle']))
+        story.append(Spacer(1, 8))
+        
+        review_count = batch_result.summary.get('review_required', 0)
+        total = batch_result.total_images
+        
+        if review_count > 0:
+            rec_text = f"""
+            <b>⚠️ ACTION REQUIRED</b><br/>
+            {review_count} out of {total} images require manual review ({review_count/total*100:.1f}%).<br/>
+            <br/>
+            <b>Recommended Actions:</b><br/>
+            • Prioritize high-risk images for immediate review<br/>
+            • Assign qualified personnel for verification<br/>
+            • Document review findings and decisions<br/>
+            • Consider additional forensic analysis for flagged images<br/>
+            • Update screening protocols based on findings
+            """
+            rec_color = self.COLOR_LIGHT_ORANGE
+            border_color = self.COLOR_WARNING
+        else:
+            rec_text = f"""
+            <b>✅ BATCH PASSED SCREENING</b><br/>
+            All {total} images appear to be authentic based on current analysis.<br/>
+            <br/>
+            <b>Recommended Actions:</b><br/>
+            • Archive results for audit trail<br/>
+            • Maintain periodic re-screening schedule<br/>
+            • Monitor for evolving AI generation techniques<br/>
+            • Update detection models regularly<br/>
+            • Document chain of custody
+            """
+            rec_color = self.COLOR_LIGHT_GREEN
+            border_color = self.COLOR_SUCCESS
+        
+        rec_table = Table([[Paragraph(rec_text, self.styles['CustomBodyText'])]], colWidths=[530])
+        rec_table.setStyle(TableStyle([
+            ('BACKGROUND', (0, 0), (-1, -1), rec_color),
+            ('BOX', (0, 0), (-1, -1), 2, border_color),
+            ('LEFTPADDING', (0, 0), (-1, -1), 15),
+            ('RIGHTPADDING', (0, 0), (-1, -1), 15),
+            ('TOPPADDING', (0, 0), (-1, -1), 12),
+            ('BOTTOMPADDING', (0, 0), (-1, -1), 12)
+        ]))
+        story.append(rec_table)
+
+    def _add_professional_footer(self, story):
+        """Professional footer with disclaimers"""
+        story.append(Spacer(1, 15))
+        
+        disclaimer_lines = [
+            "⚠️ <b>DISCLAIMER</b>: This report provides probabilistic screening results based on current AI detection methodologies, not definitive verdicts.",
+            "Results should be manually verified for critical applications. False positive rate: ~10-20%. Accuracy may vary with image quality and AI generation techniques.",
+            "This analysis should be used as one component of a comprehensive verification process, not as the sole basis for decision-making.",
+            "© 2025 AI Image Screener | Confidential Report | For Authorized Use Only"
+        ]
+        
+        for line in disclaimer_lines:
+            story.append(Paragraph(line, self.styles['Footer']))
+            story.append(Spacer(1, 2))
+
+    # Helper methods
+    
+    def _get_standard_table_style(self, num_rows):
+        """Standard table styling"""
+        return TableStyle([
+            ('BACKGROUND', (0, 0), (-1, 0), self.COLOR_HEADER_BG),
+            ('TEXTCOLOR', (0, 0), (-1, 0), colors.white),
+            ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
+            ('GRID', (0, 0), (-1, -1), 0.5, colors.grey),
+            ('ROWBACKGROUNDS', (0, 1), (-1, -1), [colors.white, self.COLOR_ALT_ROW]),
+            ('VALIGN', (0, 0), (-1, -1), 'MIDDLE'),
+            ('LEFTPADDING', (0, 0), (-1, -1), 8),
+            ('RIGHTPADDING', (0, 0), (-1, -1), 8),
+            ('TOPPADDING', (0, 0), (-1, -1), 5),
+            ('BOTTOMPADDING', (0, 0), (-1, -1), 5)
+        ])
+
+    def _get_signal_table_style(self, num_rows):
+        """Signal table styling with color coding"""
+        return TableStyle([
+            ('BACKGROUND', (0, 0), (-1, 0), self.COLOR_HEADER_BG),
+            ('TEXTCOLOR', (0, 0), (-1, 0), colors.white),
+            ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
+            ('GRID', (0, 0), (-1, -1), 0.5, colors.grey),
+            ('ROWBACKGROUNDS', (0, 1), (-1, -1), [colors.white, self.COLOR_ALT_ROW]),
+            ('VALIGN', (0, 0), (-1, -1), 'TOP'),
+            ('LEFTPADDING', (0, 0), (-1, -1), 6),
+            ('RIGHTPADDING', (0, 0), (-1, -1), 6),
+            ('TOPPADDING', (0, 0), (-1, -1), 5),
+            ('BOTTOMPADDING', (0, 0), (-1, -1), 5)
+        ])
+
+    def _get_pivot_table_style(self, num_rows):
+        """Pivot table styling"""
+        return TableStyle([
+            ('BACKGROUND', (0, 0), (-1, 0), self.COLOR_HEADER_BG),
+            ('TEXTCOLOR', (0, 0), (-1, 0), colors.white),
+            ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
+            ('GRID', (0, 0), (-1, -1), 0.5, colors.grey),
+            ('ROWBACKGROUNDS', (0, 1), (-1, -1), [colors.white, self.COLOR_ALT_ROW]),
+            ('VALIGN', (0, 0), (-1, -1), 'MIDDLE'),
+            ('ALIGN', (0, 0), (0, -1), 'CENTER'),
+            ('LEFTPADDING', (0, 0), (-1, -1), 4),
+            ('RIGHTPADDING', (0, 0), (-1, -1), 4),
+            ('TOPPADDING', (0, 0), (-1, -1), 4),
+            ('BOTTOMPADDING', (0, 0), (-1, -1), 4)
+        ])
+
+    def _get_status_badge_html(self, status: str) -> str:
+        """Generate status badge HTML"""
+        if status == "REVIEW_REQUIRED" or status == "flagged":
+            return "<font color='#B71C1C'><b>🔴 FLAGGED</b></font>"
+        elif status == "warning":
+            return "<font color='#E65100'><b>🟡 WARNING</b></font>"
+        else:
+            return "<font color='#1B5E20'><b>🟢 PASSED</b></font>"
+
+    def _get_status_badge_short(self, status: str) -> str:
+        """Short status badge"""
+        if status == "REVIEW_REQUIRED":
+            return "<font color='#B71C1C'><b>⚠️ REVIEW REQUIRED</b></font>"
+        else:
+            return "<font color='#1B5E20'><b>✓ LIKELY AUTHENTIC</b></font>"
+
+    def _get_metric_status_html(self, score: float) -> str:
+        """Metric status based on score"""
+        if score > 0.7:
+            return "<font color='#B71C1C'><b>High Risk</b></font>"
+        elif score > 0.5:
+            return "<font color='#E65100'><b>Moderate Risk</b></font>"
+        else:
+            return "<font color='#1B5E20'><b>Low Risk</b></font>"
+
+    def _format_value(self, value: Any, decimal_places: int = 4) -> str:
+        """Format value for display"""
+        if value is None or (isinstance(value, dict) and 'reason' in value):
+            return "N/A"
+        elif isinstance(value, float):
+            return f"{value:.{decimal_places}f}"
+        elif isinstance(value, (int, str, bool)):
+            return str(value)
+        else:
+            return "N/A"
+
+    def _extract_nested_value(self, details: dict, key: str) -> Any:
+        """Extract nested dictionary values"""
+        if '.' in key:
+            parts = key.split('.')
+            value = details
+            for part in parts:
+                if isinstance(value, dict):
+                    value = value.get(part, None)
+                else:
+                    return None
+            return value
+        else:
+            return details.get(key, None)

requirements.txt

@@ -0,0 +1,72 @@
+# =========================================
+# AI Image Screener - Requirements
+# Python 3.11+
+# =========================================
+
+# Core Web Framework
+fastapi==0.104.1
+uvicorn[standard]==0.24.0
+python-multipart==0.0.6
+
+# Data Validation & Settings
+pydantic==2.5.0
+pydantic-settings==2.1.0
+python-dotenv==1.0.0
+
+# Image Processing
+opencv-python-headless==4.8.1.78
+Pillow==10.1.0
+numpy==1.26.2
+scipy==1.11.4
+pandas==2.0.3
+
+# File Type Detection
+python-magic==0.4.27
+
+# PDF Generation
+reportlab==4.0.7
+
+# ASGI Server Production
+gunicorn==21.2.0
+
+# Logging & Monitoring
+colorama==0.4.6
+
+# Security
+python-jose[cryptography]==3.3.0
+passlib[bcrypt]==1.7.4
+
+# CORS & Middleware
+starlette==0.27.0
+
+# Testing (optional but recommended)
+pytest==7.4.3
+pytest-cov==4.1.0
+pytest-asyncio==0.21.1
+httpx==0.25.2
+
+# Code Quality (optional)
+black==23.12.0
+flake8==6.1.0
+isort==5.13.2
+mypy==1.7.1
+
+# Development Tools (optional)
+ipython==8.18.1
+ipdb==0.13.13
+
+# =========================================
+# Platform-Specific Notes:
+# =========================================
+# 
+# Linux (Ubuntu/Debian):
+#   sudo apt-get install -y libmagic1
+#
+# macOS:
+#   brew install libmagic
+#
+# Windows:
+#   pip install python-magic-bin==0.4.14
+#   (alternative to python-magic for Windows)
+#
+# =========================================

setup.sh

@@ -0,0 +1,138 @@
+#!/bin/bash
+
+# =========================================
+# AI Image Screener - Setup Script
+# Run this after cloning the repository
+# =========================================
+
+set -e  # Exit on error
+
+echo "================================================"
+echo "AI Image Screener - Setup"
+echo "================================================"
+echo ""
+
+# Check Python version
+echo "📌 Checking Python version..."
+python_version=$(python3 --version 2>&1 | awk '{print $2}')
+required_version="3.11"
+
+if [ "$(printf '%s\n' "$required_version" "$python_version" | sort -V | head -n1)" != "$required_version" ]; then 
+    echo "❌ Error: Python 3.11+ required (found $python_version)"
+    exit 1
+fi
+echo "✅ Python $python_version detected"
+echo ""
+
+# Create virtual environment
+echo "📦 Creating virtual environment..."
+if [ ! -d "venv" ]; then
+    python3 -m venv venv
+    echo "✅ Virtual environment created"
+else
+    echo "⚠️  Virtual environment already exists"
+fi
+echo ""
+
+# Activate virtual environment
+echo "🔌 Activating virtual environment..."
+source venv/bin/activate || {
+    echo "❌ Failed to activate virtual environment"
+    exit 1
+}
+echo "✅ Virtual environment activated"
+echo ""
+
+# Upgrade pip
+echo "⬆️  Upgrading pip..."
+pip install --upgrade pip setuptools wheel > /dev/null 2>&1
+echo "✅ pip upgraded"
+echo ""
+
+# Install dependencies
+echo "📚 Installing dependencies..."
+if [ -f "requirements.txt" ]; then
+    pip install -r requirements.txt
+    echo "✅ Dependencies installed"
+else
+    echo "❌ Error: requirements.txt not found"
+    exit 1
+fi
+echo ""
+
+# Create directories
+echo "📁 Creating required directories..."
+mkdir -p data/uploads data/reports data/cache logs
+touch data/uploads/.gitkeep
+touch data/reports/.gitkeep
+touch data/cache/.gitkeep
+touch logs/.gitkeep
+echo "✅ Directories created"
+echo ""
+
+# Create .env file if not exists
+echo "⚙️  Setting up environment..."
+if [ ! -f ".env" ]; then
+    if [ -f ".env.example" ]; then
+        cp .env.example .env
+        echo "✅ Created .env from .env.example"
+        echo "   ⚠️  Please review and update .env with your settings"
+    else
+        echo "⚠️  .env.example not found, skipping .env creation"
+    fi
+else
+    echo "⚠️  .env already exists"
+fi
+echo ""
+
+# Check system dependencies
+echo "🔍 Checking system dependencies..."
+missing_deps=()
+
+if ! command -v identify &> /dev/null; then
+    missing_deps+=("ImageMagick")
+fi
+
+if [ ${#missing_deps[@]} -gt 0 ]; then
+    echo "⚠️  Optional dependencies missing:"
+    for dep in "${missing_deps[@]}"; do
+        echo "   - $dep"
+    done
+    echo "   The app will work, but some features may be limited."
+else
+    echo "✅ All optional dependencies present"
+fi
+echo ""
+
+# Test import
+echo "🧪 Testing installation..."
+if python3 -c "import fastapi, cv2, numpy, scipy, PIL, reportlab" 2>/dev/null; then
+    echo "✅ All core packages import successfully"
+else
+    echo "❌ Some packages failed to import"
+    echo "   Try: pip install -r requirements.txt"
+    exit 1
+fi
+echo ""
+
+echo "================================================"
+echo "✨ Setup Complete!"
+echo "================================================"
+echo ""
+echo "Next steps:"
+echo ""
+echo "1. Review and update .env file (optional)"
+echo "2. Start the server:"
+echo "   $ source venv/bin/activate"
+echo "   $ python app.py"
+echo ""
+echo "3. Open browser:"
+echo "   http://localhost:8005"
+echo ""
+echo "4. Or build Docker image:"
+echo "   $ docker build -t ai-image-screener ."
+echo "   $ docker run -p 7860:7860 ai-image-screener"
+echo ""
+echo "📖 Documentation: docs/"
+echo "🐛 Issues: https://github.com/satyakimitra/ai-image-screener/issues"
+echo ""

ui/index.html

@@ -0,0 +1,2248 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>AI Image Screener</title>
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css">
+    <link rel="icon" type="image/x-icon" href="data:image/svg+xml,<svg xmlns=%22http://www.w3.org/2000/svg%22 viewBox=%220 0 100 100%22><text y=%22.9em%22 font-size=%2290%22>🔍</text></svg>">
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+
+        :root {
+            --primary: #2d3748;
+            --primary-light: #4a5568;
+            --primary-dark: #1a202c;
+            --secondary: #718096;
+            --accent: #38a169;
+            --accent-light: #68d391;
+            --accent-dark: #2f855a;
+            --warning: #d69e2e;
+            --danger: #e53e3e;
+            --background: #f7fafc;
+            --card-bg: #ffffff;
+            --border: #e2e8f0;
+            --text: #2d3748;
+            --text-light: #718096;
+            --shadow: 0 1px 3px 0 rgba(0, 0, 0, 0.1), 0 1px 2px 0 rgba(0, 0, 0, 0.06);
+            --shadow-lg: 0 10px 15px -3px rgba(0, 0, 0, 0.1), 0 4px 6px -2px rgba(0, 0, 0, 0.05);
+        }
+
+        body {
+            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, sans-serif;
+            background-color: var(--background);
+            color: var(--text);
+            line-height: 1.6;
+            min-height: 100vh;
+        }
+
+        .container {
+            max-width: 1200px;
+            margin: 0 auto;
+            padding: 10px;
+        }
+
+        /* Header */
+        header {
+            background: linear-gradient(135deg, var(--primary-dark) 0%, #2d3748 100%);
+            color: white;
+            padding: 1.5rem 0;
+            margin-bottom: 1rem;
+            border-radius: 0 0 1rem 1rem;
+            box-shadow: var(--shadow-lg);
+        }
+
+        .header-content {
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            flex-wrap: wrap;
+            gap: 1rem;
+        }
+
+        .logo {
+            display: flex;
+            align-items: center;
+            gap: 0.75rem;
+        }
+
+        .logo-icon {
+            width: 40px;
+            height: 40px;
+            background: linear-gradient(135deg, var(--accent) 0%, var(--accent-light) 100%);
+            border-radius: 8px;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            font-size: 1.25rem;
+        }
+
+        .logo-text h1 {
+            font-size: 1.5rem;
+            font-weight: 600;
+        }
+
+        .logo-text .tagline {
+            font-size: 0.875rem;
+            opacity: 0.8;
+            margin-top: 0.125rem;
+        }
+
+        /* Hero Section */
+        .hero {
+            background: linear-gradient(135deg, #2d3748 0%, #4a5568 100%);
+            border-radius: 1rem;
+            padding: 1.5rem 1.5rem;
+            text-align: center;
+            margin-bottom: 1rem;
+            color: white;
+            box-shadow: var(--shadow-lg);
+        }
+
+        .hero h2 {
+            font-size: 2.5rem;
+            margin-bottom: 1rem;
+            color: white;
+        }
+
+        .hero-subtitle {
+            font-size: 1.25rem;
+            color: rgba(255, 255, 255, 0.9);
+            margin-bottom: 2rem;
+            max-width: 800px;
+            margin-left: auto;
+            margin-right: auto;
+        }
+
+        .performance-badge {
+            display: inline-block;
+            padding: 0.75rem 1.5rem;
+            background-color: rgba(255, 255, 255, 0.1);
+            color: white;
+            border: 1px solid rgba(255, 255, 255, 0.2);
+            border-radius: 2rem;
+            font-size: 0.875rem;
+            margin-bottom: 1.5rem;
+            backdrop-filter: blur(10px);
+        }
+
+        .cta-button {
+            background: linear-gradient(135deg, var(--accent) 0%, var(--accent-dark) 100%);
+            color: white;
+            border: none;
+            padding: 1rem 2.5rem;
+            font-size: 1.125rem;
+            border-radius: 0.5rem;
+            cursor: pointer;
+            font-weight: 600;
+            transition: all 0.3s;
+            display: inline-flex;
+            align-items: center;
+            justify-content: center;
+            gap: 0.75rem;
+            box-shadow: 0 4px 6px rgba(56, 161, 105, 0.2);
+            min-width: 200px;
+            margin: 0 auto;
+        }
+
+        .cta-button:hover {
+            transform: translateY(-2px);
+            box-shadow: 0 6px 12px rgba(56, 161, 105, 0.3);
+        }
+
+        /* Tab Navigation */
+        .tabs {
+            display: flex;
+            gap: 0.5rem;
+            margin-bottom: 2rem;
+            border-bottom: 2px solid var(--border);
+            padding-bottom: 0;
+            background-color: white;
+            border-radius: 0.5rem;
+            padding: 0.5rem;
+            box-shadow: var(--shadow);
+        }
+
+        .tab-button {
+            padding: 1rem 2rem;
+            background: none;
+            border: none;
+            border-bottom: 3px solid transparent;
+            color: var(--text-light);
+            font-weight: 600;
+            cursor: pointer;
+            transition: all 0.3s;
+            position: relative;
+            flex: 1;
+            text-align: center;
+            border-radius: 0.25rem;
+        }
+
+        .tab-button.active {
+            color: var(--accent);
+            border-bottom-color: var(--accent);
+            background-color: rgba(56, 161, 105, 0.05);
+        }
+
+        .tab-button:hover:not(.active) {
+            color: var(--primary);
+            background-color: rgba(0, 0, 0, 0.02);
+        }
+
+        .tab-content {
+            display: none;
+            animation: fadeIn 0.5s ease;
+        }
+
+        .tab-content.active {
+            display: block;
+        }
+
+        @keyframes fadeIn {
+            from { opacity: 0; transform: translateY(10px); }
+            to { opacity: 1; transform: translateY(0); }
+        }
+
+        /* Features Grid */
+        .features-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
+            gap: 1.5rem;
+            margin-bottom: 2rem;
+        }
+
+        .feature-card {
+            background-color: white;
+            border-radius: 1rem;
+            padding: 1.5rem;
+            border: 1px solid var(--border);
+            transition: all 0.3s;
+        }
+
+        .feature-card:hover {
+            transform: translateY(-5px);
+            box-shadow: var(--shadow-lg);
+            border-color: var(--accent-light);
+        }
+
+        .feature-icon {
+            font-size: 2rem;
+            color: var(--accent);
+            margin-bottom: 1rem;
+        }
+
+        /* Metrics Grid - Updated for Detailed Cards */
+        .metrics-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(350px, 1fr));
+            gap: 1.5rem;
+            margin-bottom: 2rem;
+        }
+
+        @media (max-width: 768px) {
+            .metrics-grid {
+                grid-template-columns: 1fr;
+            }
+        }
+
+        .metric-card {
+            background-color: white;
+            border-radius: 1rem;
+            padding: 1.5rem;
+            border: 1px solid var(--border);
+            transition: all 0.3s;
+            display: flex;
+            flex-direction: column;
+            height: 100%;
+        }
+
+        .metric-card:hover {
+            transform: translateY(-5px);
+            box-shadow: var(--shadow-lg);
+        }
+
+        .metric-header {
+            display: flex;
+            align-items: center;
+            gap: 1rem;
+            margin-bottom: 1rem;
+            padding-bottom: 1rem;
+            border-bottom: 1px solid var(--border);
+        }
+
+        .metric-icon {
+            width: 3rem;
+            height: 3rem;
+            border-radius: 0.75rem;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            color: white;
+            font-size: 1.5rem;
+            flex-shrink: 0;
+        }
+
+        .metric-title {
+            font-size: 1.25rem;
+            font-weight: 600;
+            color: var(--primary);
+        }
+
+        .metric-weight {
+            display: inline-block;
+            padding: 0.25rem 0.75rem;
+            background-color: rgba(56, 161, 105, 0.1);
+            color: var(--accent);
+            border-radius: 2rem;
+            font-size: 0.875rem;
+            font-weight: 600;
+            margin-left: auto;
+        }
+
+        .metric-description {
+            color: var(--text-light);
+            margin-bottom: 1rem;
+            line-height: 1.6;
+        }
+
+        .metric-details {
+            margin-top: auto;
+            padding-top: 1rem;
+            border-top: 1px solid var(--border);
+        }
+
+        .detail-item {
+            display: flex;
+            justify-content: space-between;
+            margin-bottom: 0.5rem;
+            font-size: 0.875rem;
+        }
+
+        .detail-label {
+            color: var(--text-light);
+        }
+
+        .detail-value {
+            color: var(--primary);
+            font-weight: 500;
+        }
+
+        /* How-to-use Steps */
+        .steps-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
+            gap: 1.5rem;
+            margin-bottom: 2rem;
+        }
+
+        .step-card {
+            text-align: center;
+            padding: 2rem;
+            background-color: white;
+            border-radius: 1rem;
+            border: 1px solid var(--border);
+            transition: all 0.3s;
+        }
+
+        .step-card:hover {
+            transform: translateY(-5px);
+            border-color: var(--accent);
+        }
+
+        .step-number {
+            display: inline-flex;
+            align-items: center;
+            justify-content: center;
+            width: 3rem;
+            height: 3rem;
+            background: linear-gradient(135deg, var(--accent) 0%, var(--accent-light) 100%);
+            color: white;
+            border-radius: 50%;
+            font-size: 1.5rem;
+            font-weight: bold;
+            margin-bottom: 1rem;
+        }
+
+        /* Cards */
+        .card {
+            background-color: var(--card-bg);
+            border-radius: 1rem;
+            font-size: 1.00rem;
+            box-shadow: var(--shadow);
+            padding: 1.0rem;
+            margin-bottom: 0.5rem;
+            border: 1px solid var(--border);
+        }
+
+        .card-header {
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            margin-bottom: 0.5rem;
+            padding-bottom: 0.75rem;
+            border-bottom: 0.5px solid var(--border);
+        }
+
+        .card-title {
+            font-size: 1.25rem;
+            font-weight: 600;
+            display: flex;
+            align-items: center;
+            gap: 0.5rem;
+        }
+
+        /* Upload Section */
+        .upload-area {
+            border: 2px dashed var(--border);
+            border-radius: 1rem;
+            padding: 3rem 1.5rem;
+            text-align: center;
+            transition: all 0.3s ease;
+            cursor: pointer;
+            margin-bottom: 1rem;
+            background-color: #f8fafc;
+        }
+
+        .upload-area:hover, .upload-area.dragover {
+            border-color: var(--accent);
+            background-color: rgba(56, 161, 105, 0.05);
+        }
+
+        .upload-icon {
+            font-size: 3rem;
+            color: var(--accent);
+            margin-bottom: 1rem;
+        }
+
+        .upload-button {
+            background-color: var(--accent);
+            color: white;
+            border: none;
+            padding: 0.75rem 1.5rem;
+            border-radius: 0.5rem;
+            font-weight: 600;
+            cursor: pointer;
+            transition: all 0.3s;
+            display: inline-flex;
+            align-items: center;
+            justify-content: center;
+            gap: 0.5rem;
+        }
+
+        .upload-button:hover {
+            background-color: var(--accent-dark);
+            transform: translateY(-2px);
+        }
+
+        /* Thumbnail Grid */
+        .thumbnail-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fill, minmax(120px, 1fr));
+            gap: 1rem;
+            margin-top: 1rem;
+            max-height: 300px;
+            overflow-y: auto;
+            padding: 0.5rem;
+        }
+
+        .thumbnail-item {
+            position: relative;
+            border-radius: 0.5rem;
+            overflow: hidden;
+            border: 2px solid var(--border);
+            transition: all 0.3s;
+            height: 120px;
+        }
+
+        .thumbnail-item:hover {
+            border-color: var(--accent);
+            transform: translateY(-2px);
+        }
+
+        .thumbnail-img {
+            width: 100%;
+            height: 100%;
+            object-fit: cover;
+        }
+
+        .thumbnail-overlay {
+            position: absolute;
+            bottom: 0;
+            left: 0;
+            right: 0;
+            background: linear-gradient(transparent, rgba(0, 0, 0, 0.7));
+            padding: 0.5rem;
+            color: white;
+            font-size: 0.75rem;
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+        }
+
+        .remove-thumbnail {
+            background: rgba(229, 62, 62, 0.8);
+            border: none;
+            color: white;
+            width: 24px;
+            height: 24px;
+            border-radius: 50%;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            cursor: pointer;
+            transition: all 0.3s;
+        }
+
+        .remove-thumbnail:hover {
+            background: var(--danger);
+            transform: scale(1.1);
+        }
+
+        /* Start Analysis Button - Centered */
+        .start-analysis-btn {
+            background: linear-gradient(135deg, var(--accent) 0%, var(--accent-dark) 100%);
+            color: white;
+            border: none;
+            padding: 1rem 2rem;
+            font-size: 1.125rem;
+            border-radius: 0.5rem;
+            cursor: pointer;
+            font-weight: 600;
+            transition: all 0.3s;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            gap: 0.75rem;
+            width: 100%;
+            margin-top: 1.5rem;
+            box-shadow: 0 4px 6px rgba(56, 161, 105, 0.2);
+        }
+
+        .start-analysis-btn:hover:not(:disabled) {
+            transform: translateY(-2px);
+            box-shadow: 0 6px 12px rgba(56, 161, 105, 0.3);
+        }
+
+        .start-analysis-btn:disabled {
+            opacity: 0.5;
+            cursor: not-allowed;
+            transform: none !important;
+        }
+
+        .start-analysis-btn .btn-content {
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            gap: 0.75rem;
+        }
+
+        /* Progress Section */
+        .progress-container {
+            margin-top: 1rem;
+            padding: 1rem;
+            background-color: white;
+            border-radius: 0.5rem;
+            box-shadow: var(--shadow);
+            border: 1px solid var(--border);
+        }
+
+        .progress-header {
+            display: flex;
+            justify-content: space-between;
+            margin-bottom: 0.5rem;
+        }
+
+        .progress-bar {
+            height: 0.5rem;
+            background-color: var(--border);
+            border-radius: 1rem;
+            overflow: hidden;
+            margin-bottom: 0.5rem;
+        }
+
+        .progress-fill {
+            height: 100%;
+            background: linear-gradient(90deg, var(--accent), var(--accent-light));
+            border-radius: 1rem;
+            width: 0%;
+            transition: width 0.5s ease;
+        }
+
+        /* Results Section */
+        .results-summary {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
+            gap: 1rem;
+            margin-bottom: 1.5rem;
+        }
+
+        .summary-card {
+            text-align: center;
+            padding: 1.5rem;
+            border-radius: 1rem;
+            background-color: white;
+            border: 1px solid var(--border);
+            transition: transform 0.3s;
+        }
+
+        .summary-card:hover {
+            transform: translateY(-3px);
+        }
+
+        .summary-value {
+            font-size: 2rem;
+            font-weight: 700;
+            margin-bottom: 0.25rem;
+        }
+
+        .summary-label {
+            font-size: 0.875rem;
+            color: var(--text-light);
+        }
+
+        .results-table-container {
+            overflow-x: auto;
+            margin-top: 1.5rem;
+            border-radius: 0.5rem;
+            border: 1px solid var(--border);
+            background-color: white;
+        }
+
+        .results-table {
+            width: 100%;
+            border-collapse: collapse;
+        }
+
+        .results-table th {
+            background-color: #f8fafc;
+            color: var(--text);
+            padding: 1rem;
+            text-align: left;
+            font-weight: 600;
+            border-bottom: 1px solid var(--border);
+        }
+
+        .results-table td {
+            padding: 1rem;
+            border-bottom: 1px solid var(--border);
+            vertical-align: middle;
+        }
+
+        .results-table tr:hover {
+            background-color: #f8fafc;
+        }
+
+        .status-badge {
+            display: inline-block;
+            padding: 0.25rem 0.75rem;
+            border-radius: 2rem;
+            font-size: 0.75rem;
+            font-weight: 600;
+            white-space: nowrap;
+        }
+
+        .status-authentic {
+            background-color: rgba(56, 161, 105, 0.1);
+            color: var(--accent);
+            border: 1px solid rgba(56, 161, 105, 0.3);
+        }
+
+        .status-review {
+            background-color: rgba(214, 158, 46, 0.1);
+            color: var(--warning);
+            border: 1px solid rgba(214, 158, 46, 0.3);
+        }
+
+        .score-indicator {
+            display: flex;
+            align-items: center;
+            gap: 0.5rem;
+            min-width: 150px;
+        }
+
+        .score-bar {
+            flex: 1;
+            height: 0.5rem;
+            background-color: var(--border);
+            border-radius: 1rem;
+            overflow: hidden;
+        }
+
+        .score-fill {
+            height: 100%;
+            border-radius: 1rem;
+            transition: width 0.5s ease;
+        }
+
+        .score-low {
+            background: linear-gradient(90deg, var(--accent), var(--accent-light));
+        }
+
+        .score-medium {
+            background: linear-gradient(90deg, var(--warning), #ecc94b);
+        }
+
+        .score-high {
+            background: linear-gradient(90deg, var(--danger), #fc8181);
+        }
+
+        /* Detailed Analysis */
+        .detailed-analysis {
+            margin-top: 2rem;
+            padding: 1.5rem;
+            background-color: white;
+            border-radius: 1rem;
+            border: 1px solid var(--border);
+        }
+
+        .analysis-header {
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            margin-bottom: 1.5rem;
+            cursor: pointer;
+            padding: 0.5rem;
+            border-radius: 0.5rem;
+            transition: background-color 0.3s;
+        }
+
+        .analysis-header:hover {
+            background-color: #f8fafc;
+        }
+
+        .analysis-content {
+            display: none;
+            padding-top: 1rem;
+            border-top: 1px solid var(--border);
+            animation: fadeIn 0.5s ease;
+        }
+
+        .analysis-content.show {
+            display: block;
+        }
+
+        .signal-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));
+            gap: 1rem;
+            margin-bottom: 1.5rem;
+        }
+
+        .signal-card {
+            padding: 1rem;
+            border-radius: 0.5rem;
+            border: 1px solid var(--border);
+            background-color: #f8fafc;
+        }
+
+        .signal-header {
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            margin-bottom: 0.5rem;
+        }
+
+        .signal-badge {
+            padding: 0.25rem 0.5rem;
+            border-radius: 0.375rem;
+            font-size: 0.75rem;
+            font-weight: 500;
+            border: 1px solid;
+            white-space: nowrap;
+        }
+
+        .signal-passed {
+            background-color: rgba(56, 161, 105, 0.1);
+            color: var(--accent);
+            border-color: rgba(56, 161, 105, 0.3);
+        }
+
+        .signal-warning {
+            background-color: rgba(214, 158, 46, 0.1);
+            color: var(--warning);
+            border-color: rgba(214, 158, 46, 0.3);
+        }
+
+        .signal-flagged {
+            background-color: rgba(229, 62, 62, 0.1);
+            color: var(--danger);
+            border-color: rgba(229, 62, 62, 0.3);
+        }
+
+        /* Footer - Reduced spacing */
+        footer {
+            margin-top: 0.1rem;
+            padding-top: 0.1rem;
+            border-top: 1px solid var(--border);
+            color: var(--text-light);
+            font-size: 0.875rem;
+            text-align: center;
+        }
+
+        .footer-links {
+            display: flex;
+            justify-content: center;
+            gap: 2rem;
+            margin-bottom: 1rem;
+            flex-wrap: wrap;
+        }
+
+        .footer-link {
+            color: var(--accent);
+            text-decoration: none;
+            transition: color 0.3s;
+            font-size: 0.875rem;
+        }
+
+        .footer-link:hover {
+            color: var(--accent-dark);
+            text-decoration: underline;
+        }
+
+        /* Action buttons */
+        .action-button {
+            padding: 0.5rem 1rem;
+            border: none;
+            border-radius: 0.5rem;
+            font-weight: 500;
+            cursor: pointer;
+            transition: all 0.3s;
+            display: inline-flex;
+            align-items: center;
+            justify-content: center;
+            gap: 0.5rem;
+            font-size: 0.875rem;
+        }
+
+        .primary-action {
+            background-color: var(--accent);
+            color: white;
+        }
+
+        .primary-action:hover {
+            background-color: var(--accent-dark);
+        }
+
+        .secondary-action {
+            background-color: white;
+            color: var(--accent);
+            border: 1px solid var(--accent);
+        }
+
+        .secondary-action:hover {
+            background-color: rgba(56, 161, 105, 0.1);
+        }
+
+        /* Loading overlay */
+        .loading-overlay {
+            position: fixed;
+            top: 0;
+            left: 0;
+            right: 0;
+            bottom: 0;
+            background-color: rgba(0, 0, 0, 0.5);
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            z-index: 1000;
+            opacity: 0;
+            visibility: hidden;
+            transition: all 0.3s;
+        }
+
+        .loading-overlay.active {
+            opacity: 1;
+            visibility: visible;
+        }
+
+        .loading-spinner {
+            width: 60px;
+            height: 60px;
+            border: 4px solid rgba(255, 255, 255, 0.3);
+            border-radius: 50%;
+            border-top-color: white;
+            animation: spin 1s ease-in-out infinite;
+        }
+
+        @keyframes spin {
+            to { transform: rotate(360deg); }
+        }
+
+        /* Toast notification */
+        .toast {
+            position: fixed;
+            top: 20px;
+            right: 20px;
+            padding: 1rem 1.5rem;
+            background-color: white;
+            color: var(--text);
+            border-radius: 0.5rem;
+            box-shadow: var(--shadow-lg);
+            z-index: 1000;
+            transform: translateX(100%);
+            transition: transform 0.3s ease;
+            max-width: 300px;
+            border-left: 4px solid var(--accent);
+        }
+
+        .toast.show {
+            transform: translateX(0);
+        }
+
+        .toast.error {
+            border-left-color: var(--danger);
+        }
+
+        .toast.warning {
+            border-left-color: var(--warning);
+        }
+
+        /* Utility classes */
+        .hidden {
+            display: none !important;
+        }
+
+        .visible {
+            display: block !important;
+        }
+
+        .text-center {
+            text-align: center;
+        }
+
+        .mt-1 { margin-top: 0.5rem; }
+        .mt-2 { margin-top: 1rem; }
+        .mt-3 { margin-top: 1.5rem; }
+        .mb-1 { margin-bottom: 0.5rem; }
+        .mb-2 { margin-bottom: 1rem; }
+        .mb-3 { margin-bottom: 1.5rem; }
+
+        /* Responsive adjustments */
+        @media (max-width: 768px) {
+            .hero h2 {
+                font-size: 2rem;
+            }
+            
+            .hero-subtitle {
+                font-size: 1rem;
+            }
+            
+            .tabs {
+                flex-direction: column;
+            }
+            
+            .tab-button {
+                width: 100%;
+                text-align: center;
+            }
+            
+            .metrics-grid {
+                grid-template-columns: 1fr;
+            }
+            
+            .signal-grid {
+                grid-template-columns: 1fr;
+            }
+            
+            .footer-links {
+                flex-direction: column;
+                gap: 0.75rem;
+            }
+        }
+
+        /* Spinner for loading button */
+        .spinner {
+            display: inline-block;
+            width: 1rem;
+            height: 1rem;
+            border: 2px solid rgba(255, 255, 255, 0.3);
+            border-radius: 50%;
+            border-top-color: white;
+            animation: spin 1s ease-in-out infinite;
+            margin-right: 0.5rem;
+        }
+    </style>
+</head>
+<body>
+    <!-- Loading Overlay -->
+    <div class="loading-overlay" id="loadingOverlay">
+        <div class="loading-spinner"></div>
+    </div>
+
+    <!-- Toast Notification -->
+    <div class="toast hidden" id="toast"></div>
+
+    <!-- Header -->
+    <header>
+        <div class="container">
+            <div class="header-content">
+                <div class="logo">
+                    <div class="logo-icon">
+                        <i class="fas fa-filter"></i>
+                    </div>
+                    <div class="logo-text">
+                        <h1>AI Image Screener</h1>
+                        <div class="tagline">First-pass screening for bulk workflows</div>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </header>
+
+    <!-- Main Content -->
+    <div class="container">
+        <!-- Landing Screen -->
+        <div id="landingScreen">
+            <!-- Hero Section -->
+            <section class="hero">
+                <h2>AI Image Screener</h2>
+                <p class="hero-subtitle">
+                    A practical first-pass AI image screening system designed to identify images that require human review based on statistical and physical patterns.
+                </p>
+                <div class="performance-badge">
+                    <i class="fas fa-chart-line"></i> Screening accuracy: 40-90% detection rate across AI models
+                </div>
+                <br>
+                <button class="cta-button" id="tryNowBtn">
+                    <div class="btn-content">
+                        <i class="fas fa-play-circle"></i> Start Screening
+                    </div>
+                </button>
+            </section>
+
+            <!-- Tab Navigation -->
+            <div class="tabs">
+                <button class="tab-button active" data-tab="features">Features</button>
+                <button class="tab-button" data-tab="metrics">Detection Metrics</button>
+                <button class="tab-button" data-tab="howto">How to Use</button>
+            </div>
+
+            <!-- Features Tab -->
+            <div class="tab-content active" id="featuresTab">
+                <div class="features-grid">
+                    <div class="feature-card">
+                        <div class="feature-icon">
+                            <i class="fas fa-bolt"></i>
+                        </div>
+                        <h3>Fast Processing</h3>
+                        <p>Parallel processing for batch analysis with real-time progress tracking</p>
+                    </div>
+                    
+                    <div class="feature-card">
+                        <div class="feature-icon">
+                            <i class="fas fa-chart-bar"></i>
+                        </div>
+                        <h3>Multi-Signal Detection</h3>
+                        <p>Five independent statistical detectors with weighted ensemble aggregation</p>
+                    </div>
+                    
+                    <div class="feature-card">
+                        <div class="feature-icon">
+                            <i class="fas fa-file-export"></i>
+                        </div>
+                        <h3>Comprehensive Reports</h3>
+                        <p>Export results in CSV, JSON, and PDF formats for integration and documentation</p>
+                    </div>
+                    
+                    <div class="feature-card">
+                        <div class="feature-icon">
+                            <i class="fas fa-sliders-h"></i>
+                        </div>
+                        <h3>Adjustable Sensitivity</h3>
+                        <p>Conservative, balanced, and aggressive modes for different use cases</p>
+                    </div>
+                </div>
+
+                <!-- Caution Notice -->
+                <div class="card">
+                    <div class="card-header">
+                        <h3 class="card-title"><i class="fas fa-exclamation-triangle" style="color: var(--warning);"></i> Important Notice</h3>
+                    </div>
+                    <p style="color: var(--text-light);">
+                        <strong>This is not a perfect AI detector. It's a screening tool that helps reduce manual review workload by flagging suspicious images for human verification.</strong>
+                    </p>
+                </div>
+            </div>
+
+            <!-- Metrics Tab - Updated with Detailed Cards -->
+            <div class="tab-content" id="metricsTab">
+                <div class="metrics-grid">
+                    <div class="metric-card">
+                        <div class="metric-header">
+                            <div class="metric-icon" style="background: linear-gradient(135deg, #4a5568 0%, #718096 100%);">
+                                <i class="fas fa-wave-square"></i>
+                            </div>
+                            <div>
+                                <div class="metric-title">Gradient-Field PCA</div>
+                            </div>
+                            <span class="metric-weight">Weight: 30%</span>
+                        </div>
+                        <p class="metric-description">
+                            Detects lighting & gradient inconsistencies typical of diffusion models. Analyzes directional light patterns and shadow consistency that often appear unnatural in AI-generated images.
+                        </p>
+                        <div class="metric-details">
+                            <div class="detail-item">
+                                <span class="detail-label">Detection Method</span>
+                                <span class="detail-value">Principal Component Analysis</span>
+                            </div>
+                            <div class="detail-item">
+                                <span class="detail-label">Sensitivity</span>
+                                <span class="detail-value">High for diffusion models</span>
+                            </div>
+                            <div class="detail-item">
+                                <span class="detail-label">Performance</span>
+                                <span class="detail-value">85-95% detection rate</span>
+                            </div>
+                        </div>
+                    </div>
+                    
+                    <div class="metric-card">
+                        <div class="metric-header">
+                            <div class="metric-icon" style="background: linear-gradient(135deg, #718096 0%, #a0aec0 100%);">
+                                <i class="fas fa-chart-line"></i>
+                            </div>
+                            <div>
+                                <div class="metric-title">Frequency Analysis</div>
+                            </div>
+                            <span class="metric-weight">Weight: 25%</span>
+                        </div>
+                        <p class="metric-description">
+                            Identifies unnatural spectral energy distributions via FFT analysis. AI-generated images often show characteristic frequency patterns different from camera-captured photos.
+                        </p>
+                        <div class="metric-details">
+                            <div class="detail-item">
+                                <span class="detail-label">Detection Method</span>
+                                <span class="detail-value">Fast Fourier Transform</span>
+                            </div>
+                            <div class="detail-item">
+                                <span class="detail-label">Sensitivity</span>
+                                <span class="detail-value">Medium-High</span>
+                            </div>
+                            <div class="detail-item">
+                                <span class="detail-label">Performance</span>
+                                <span class="detail-value">75-85% detection rate</span>
+                            </div>
+                        </div>
+                    </div>
+                    
+                    <div class="metric-card">
+                        <div class="metric-header">
+                            <div class="metric-icon" style="background: linear-gradient(135deg, #38a169 0%, #68d391 100%);">
+                                <i class="fas fa-braille"></i>
+                            </div>
+                            <div>
+                                <div class="metric-title">Noise Pattern Analysis</div>
+                            </div>
+                            <span class="metric-weight">Weight: 20%</span>
+                        </div>
+                        <p class="metric-description">
+                            Detects missing or artificial sensor noise patterns. Real cameras produce characteristic noise while AI models often generate unnaturally uniform or missing noise patterns.
+                        </p>
+                        <div class="metric-details">
+                            <div class="detail-item">
+                                <span class="detail-label">Detection Method</span>
+                                <span class="detail-value">Noise Distribution Analysis</span>
+                            </div>
+                            <div class="detail-item">
+                                <span class="detail-label">Sensitivity</span>
+                                <span class="detail-value">Medium</span>
+                            </div>
+                            <div class="detail-item">
+                                <span class="detail-label">Performance</span>
+                                <span class="detail-value">70-80% detection rate</span>
+                            </div>
+                        </div>
+                    </div>
+                    
+                    <div class="metric-card">
+                        <div class="metric-header">
+                            <div class="metric-icon" style="background: linear-gradient(135deg, #d69e2e 0%, #ecc94b 100%);">
+                                <i class="fas fa-text-height"></i>
+                            </div>
+                            <div>
+                                <div class="metric-title">Texture Statistics</div>
+                            </div>
+                            <span class="metric-weight">Weight: 15%</span>
+                        </div>
+                        <p class="metric-description">
+                            Identifies overly smooth or uniform texture regions. AI-generated images often lack the natural texture variation found in real photographs, especially in complex surfaces.
+                        </p>
+                        <div class="metric-details">
+                            <div class="detail-item">
+                                <span class="detail-label">Detection Method</span>
+                                <span class="detail-value">GLCM Texture Analysis</span>
+                            </div>
+                            <div class="detail-item">
+                                <span class="detail-label">Sensitivity</span>
+                                <span class="detail-value">Medium-Low</span>
+                            </div>
+                            <div class="detail-item">
+                                <span class="detail-label">Performance</span>
+                                <span class="detail-value">60-70% detection rate</span>
+                            </div>
+                        </div>
+                    </div>
+                    
+                    <div class="metric-card">
+                        <div class="metric-header">
+                            <div class="metric-icon" style="background: linear-gradient(135deg, #e53e3e 0%, #fc8181 100%);">
+                                <i class="fas fa-palette"></i>
+                            </div>
+                            <div>
+                                <div class="metric-title">Color Distribution</div>
+                            </div>
+                            <span class="metric-weight">Weight: 10%</span>
+                        </div>
+                        <p class="metric-description">
+                            Flags unnatural saturation and color histogram patterns. AI models often produce colors that are either oversaturated or have distribution patterns that differ from real photographs.
+                        </p>
+                        <div class="metric-details">
+                            <div class="detail-item">
+                                <span class="detail-label">Detection Method</span>
+                                <span class="detail-value">Color Histogram Analysis</span>
+                            </div>
+                            <div class="detail-item">
+                                <span class="detail-label">Sensitivity</span>
+                                <span class="detail-value">Low-Medium</span>
+                            </div>
+                            <div class="detail-item">
+                                <span class="detail-label">Performance</span>
+                                <span class="detail-value">50-65% detection rate</span>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+
+            <!-- How-to-use Tab -->
+            <div class="tab-content" id="howtoTab">
+                <div class="steps-grid">
+                    <div class="step-card">
+                        <div class="step-number">1</div>
+                        <h3>Upload Images</h3>
+                        <p>Drag & drop or select images (JPG, PNG, WEBP)</p>
+                    </div>
+                    
+                    <div class="step-card">
+                        <div class="step-number">2</div>
+                        <h3>Start Analysis</h3>
+                        <p>Click "Start Analysis" to begin screening</p>
+                    </div>
+                    
+                    <div class="step-card">
+                        <div class="step-number">3</div>
+                        <h3>Review Results</h3>
+                        <p>Check flagged images and export reports</p>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <!-- Analysis Screen (Initially Hidden) -->
+        <div id="analysisScreen" class="hidden">
+            <!-- Upload Card -->
+            <div class="card">
+                <div class="card-header">
+                    <h2 class="card-title"><i class="fas fa-cloud-upload-alt"></i> Upload Images</h2>
+                    <button class="action-button secondary-action" id="backHomeBtn">
+                        <i class="fas fa-arrow-left"></i> Back
+                    </button>
+                </div>
+                
+                <div class="upload-area" id="uploadArea">
+                    <div class="upload-icon">
+                        <i class="fas fa-cloud-upload-alt"></i>
+                    </div>
+                    <h3 class="upload-text">Drag & drop images here</h3>
+                    <p class="upload-text">or</p>
+                    <div class="upload-button" id="fileInputBtn">
+                        <i class="fas fa-folder-open"></i> Browse Files
+                    </div>
+                    <input type="file" id="fileInput" multiple accept=".jpg,.jpeg,.png,.webp" style="display: none;">
+                    <p class="text-center mt-2" style="color: var(--text-light); font-size: 0.875rem;">
+                        Supports JPG, JPEG, PNG, WEBP up to 10MB each
+                    </p>
+                </div>
+                
+                <!-- Thumbnail Grid -->
+                <div class="thumbnail-grid" id="thumbnailGrid"></div>
+                
+                <!-- Start Analysis Button - Centered -->
+                <div class="mt-3" id="analyzeButtonContainer" style="display: none;">
+                    <button class="start-analysis-btn" id="analyzeBtn">
+                        <div class="btn-content">
+                            <i class="fas fa-play"></i> Start Analysis
+                        </div>
+                    </button>
+                </div>
+                
+                <div class="progress-container hidden" id="progressContainer">
+                    <div class="progress-header">
+                        <span>Processing</span>
+                        <span id="progressPercent">0%</span>
+                    </div>
+                    <div class="progress-bar">
+                        <div class="progress-fill" id="progressFill"></div>
+                    </div>
+                    <div class="progress-details">
+                        <span id="currentFile" class="current-file">Ready to process</span>
+                        <span id="progressStats">0 / 0</span>
+                    </div>
+                </div>
+            </div>
+            
+            <!-- Results Section -->
+            <div id="resultsSection" class="hidden">
+                <!-- Export Buttons -->
+                <div class="card">
+                    <div class="card-header">
+                        <h2 class="card-title"><i class="fas fa-chart-bar"></i> Analysis Results</h2>
+                        <div class="results-actions">
+                            <button class="action-button secondary-action" id="exportCsvBtn">
+                                <i class="fas fa-file-csv"></i> CSV
+                            </button>
+                            <button class="action-button secondary-action" id="exportPdfBtn">
+                                <i class="fas fa-file-pdf"></i> PDF
+                            </button>
+                            <button class="action-button secondary-action" id="exportJsonBtn">
+                                <i class="fas fa-file-code"></i> JSON
+                            </button>
+                            <button class="action-button secondary-action" id="newAnalysisBtn">
+                                <i class="fas fa-redo"></i> New
+                            </button>
+                        </div>
+                    </div>
+                    
+                    <!-- Results Summary -->
+                    <div class="results-summary" id="resultsSummary">
+                        <!-- Summary cards will be populated here -->
+                    </div>
+                    
+                    <!-- Results Table -->
+                    <div class="results-table-container">
+                        <table class="results-table" id="resultsTable">
+                            <thead>
+                                <tr>
+                                    <th>Image</th>
+                                    <th>Status</th>
+                                    <th>Score</th>
+                                    <th>Signals</th>
+                                    <th>Details</th>
+                                </tr>
+                            </thead>
+                            <tbody id="resultsTableBody">
+                                <!-- Results will be populated here -->
+                                <tr id="noResultsRow">
+                                    <td colspan="5" class="text-center" style="padding: 3rem; color: var(--text-light);">
+                                        <i class="fas fa-chart-bar" style="font-size: 3rem; margin-bottom: 1rem; opacity: 0.5;"></i>
+                                        <p>No analysis results yet. Upload images and click "Start Analysis" to begin.</p>
+                                    </td>
+                                </tr>
+                            </tbody>
+                        </table>
+                    </div>
+                </div>
+                
+                <!-- Detailed Analysis -->
+                <div class="detailed-analysis">
+                    <div class="analysis-header" id="toggleDetailedAnalysis">
+                        <h3><i class="fas fa-search"></i> Detailed Analysis</h3>
+                        <i class="fas fa-chevron-down" id="detailedAnalysisIcon"></i>
+                    </div>
+                    <div class="analysis-content" id="detailedAnalysisContent">
+                        <!-- Detailed analysis will be populated here -->
+                        <p id="noDetailedAnalysis" class="text-center" style="color: var(--text-light); padding: 2rem;">
+                            <i class="fas fa-eye" style="font-size: 2rem; margin-bottom: 1rem; opacity: 0.5;"></i><br>
+                            Select an image to view detailed analysis
+                        </p>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+    
+    <!-- Footer with reduced spacing -->
+    <footer>
+        <div class="container">
+            <div class="footer-links">
+                <a href="#" class="footer-link">Documentation</a>
+                <a href="#" class="footer-link">API Reference</a>
+                <a href="#" class="footer-link">Privacy</a>
+                <a href="#" class="footer-link">Support</a>
+            </div>
+            <p>AI Image Screener v1.0.0 © 2025</p>
+        </div>
+    </footer>
+
+    <script>
+        // API Configuration
+        const API_BASE_URL = window.location.origin;
+        const BATCH_ENDPOINT = '/analyze/batch';
+        const HEALTH_ENDPOINT = '/health';
+        const BATCH_PROGRESS_ENDPOINT = '/batch';
+        const CSV_REPORT_ENDPOINT = '/report/csv';
+        const PDF_REPORT_ENDPOINT = '/report/pdf';
+        
+        // Global state
+        let files = [];
+        let fileDataUrls = {};
+        let currentBatchId = null;
+        let batchResults = null;
+        let pollingInterval = null;
+        let selectedImageIndex = null;
+        
+        // DOM Elements
+        const landingScreen = document.getElementById('landingScreen');
+        const analysisScreen = document.getElementById('analysisScreen');
+        const resultsSection = document.getElementById('resultsSection');
+        const loadingOverlay = document.getElementById('loadingOverlay');
+        const toast = document.getElementById('toast');
+        const tryNowBtn = document.getElementById('tryNowBtn');
+        const backHomeBtn = document.getElementById('backHomeBtn');
+        const newAnalysisBtn = document.getElementById('newAnalysisBtn');
+        const uploadArea = document.getElementById('uploadArea');
+        const fileInput = document.getElementById('fileInput');
+        const fileInputBtn = document.getElementById('fileInputBtn');
+        const thumbnailGrid = document.getElementById('thumbnailGrid');
+        const analyzeBtn = document.getElementById('analyzeBtn');
+        const analyzeButtonContainer = document.getElementById('analyzeButtonContainer');
+        const progressContainer = document.getElementById('progressContainer');
+        const progressFill = document.getElementById('progressFill');
+        const progressPercent = document.getElementById('progressPercent');
+        const currentFile = document.getElementById('currentFile');
+        const progressStats = document.getElementById('progressStats');
+        const resultsSummary = document.getElementById('resultsSummary');
+        const resultsTableBody = document.getElementById('resultsTableBody');
+        const noResultsRow = document.getElementById('noResultsRow');
+        const exportCsvBtn = document.getElementById('exportCsvBtn');
+        const exportPdfBtn = document.getElementById('exportPdfBtn');
+        const exportJsonBtn = document.getElementById('exportJsonBtn');
+        const toggleDetailedAnalysis = document.getElementById('toggleDetailedAnalysis');
+        const detailedAnalysisIcon = document.getElementById('detailedAnalysisIcon');
+        const detailedAnalysisContent = document.getElementById('detailedAnalysisContent');
+        const noDetailedAnalysis = document.getElementById('noDetailedAnalysis');
+        const tabButtons = document.querySelectorAll('.tab-button');
+        const tabContents = document.querySelectorAll('.tab-content');
+        
+        // Initialize
+        document.addEventListener('DOMContentLoaded', () => {
+            setupEventListeners();
+            setupTabs();
+            checkApiHealth();
+        });
+        
+        // Toast notification
+        function showToast(message, type = 'success') {
+            toast.textContent = message;
+            toast.className = `toast ${type} show`;
+            
+            setTimeout(() => {
+                toast.classList.remove('show');
+            }, 3000);
+        }
+        
+        // Loading overlay
+        function showLoading(show) {
+            if (show) {
+                loadingOverlay.classList.add('active');
+            } else {
+                loadingOverlay.classList.remove('active');
+            }
+        }
+        
+        // Tab functionality
+        function setupTabs() {
+            tabButtons.forEach(button => {
+                button.addEventListener('click', () => {
+                    const tabId = button.dataset.tab + 'Tab';
+                    
+                    // Remove active class from all buttons and contents
+                    tabButtons.forEach(btn => btn.classList.remove('active'));
+                    tabContents.forEach(content => content.classList.remove('active'));
+                    
+                    // Add active class to clicked button and corresponding content
+                    button.classList.add('active');
+                    document.getElementById(tabId).classList.add('active');
+                });
+            });
+        }
+        
+        // Setup event listeners - FIXED FOR ONE-CLICK UPLOAD
+        function setupEventListeners() {
+            // Navigation
+            tryNowBtn.addEventListener('click', showAnalysisScreen);
+            backHomeBtn.addEventListener('click', showLandingScreen);
+            newAnalysisBtn.addEventListener('click', resetAnalysis);
+            
+            // File upload - ONLY ONE CLICK HANDLER
+            fileInputBtn.addEventListener('click', (e) => {
+                e.stopPropagation(); // Prevent bubbling
+                fileInput.click();
+            });
+            
+            // File input change handler
+            fileInput.addEventListener('change', handleFileSelect);
+            
+            // Remove the uploadArea click handler that was causing double triggers
+            // Keep only drag and drop handlers for uploadArea
+            uploadArea.addEventListener('dragover', handleDragOver);
+            uploadArea.addEventListener('dragleave', handleDragLeave);
+            uploadArea.addEventListener('drop', handleDrop);
+            
+            // Analysis
+            analyzeBtn.addEventListener('click', startAnalysis);
+            
+            // Export
+            exportCsvBtn.addEventListener('click', exportCsv);
+            exportPdfBtn.addEventListener('click', exportPdf);
+            exportJsonBtn.addEventListener('click', exportJson);
+            
+            // Detailed analysis toggle
+            toggleDetailedAnalysis.addEventListener('click', () => {
+                detailedAnalysisContent.classList.toggle('show');
+                detailedAnalysisIcon.classList.toggle('fa-chevron-down');
+                detailedAnalysisIcon.classList.toggle('fa-chevron-up');
+            });
+        }
+        
+        // Screen navigation
+        function showLandingScreen() {
+            landingScreen.classList.remove('hidden');
+            analysisScreen.classList.add('hidden');
+            window.scrollTo({ top: 0, behavior: 'smooth' });
+        }
+        
+        function showAnalysisScreen() {
+            landingScreen.classList.add('hidden');
+            analysisScreen.classList.remove('hidden');
+            window.scrollTo({ top: 0, behavior: 'smooth' });
+        }
+        
+        // File handling
+        function handleDragOver(e) {
+            e.preventDefault();
+            uploadArea.classList.add('dragover');
+        }
+        
+        function handleDragLeave(e) {
+            e.preventDefault();
+            uploadArea.classList.remove('dragover');
+        }
+        
+        function handleDrop(e) {
+            e.preventDefault();
+            uploadArea.classList.remove('dragover');
+            
+            const droppedFiles = Array.from(e.dataTransfer.files);
+            if (droppedFiles.length > 0) {
+                processFiles(droppedFiles);
+            }
+        }
+        
+        function handleFileSelect(e) {
+            const selectedFiles = Array.from(e.target.files);
+            if (selectedFiles.length > 0) {
+                processFiles(selectedFiles);
+            }
+            // Clear the input value to allow same file selection
+            e.target.value = '';
+        }
+        
+        async function processFiles(newFiles) {
+            const validFiles = [];
+            
+            for (const file of newFiles) {
+                const validTypes = ['image/jpeg', 'image/jpg', 'image/png', 'image/webp'];
+                const maxSize = 10 * 1024 * 1024;
+                
+                if (!validTypes.includes(file.type)) {
+                    showToast(`File ${file.name} is not a supported image type.`, 'error');
+                    continue;
+                }
+                
+                if (file.size > maxSize) {
+                    showToast(`File ${file.name} exceeds the 10MB size limit.`, 'error');
+                    continue;
+                }
+                
+                validFiles.push(file);
+            }
+            
+            if (validFiles.length > 0) {
+                showLoading(true);
+                
+                try {
+                    // Generate thumbnails
+                    for (const file of validFiles) {
+                        try {
+                            const dataUrl = await createThumbnail(file);
+                            fileDataUrls[file.name] = dataUrl;
+                        } catch (error) {
+                            console.error('Failed to create thumbnail:', error);
+                            fileDataUrls[file.name] = null;
+                        }
+                    }
+                    
+                    files.push(...validFiles);
+                    updateThumbnailGrid();
+                    showToast(`Added ${validFiles.length} file(s)`, 'success');
+                } catch (error) {
+                    console.error('Error processing files:', error);
+                    showToast('Error processing files. Please try again.', 'error');
+                } finally {
+                    showLoading(false);
+                }
+            }
+        }
+        
+        function createThumbnail(file) {
+            return new Promise((resolve, reject) => {
+                const reader = new FileReader();
+                reader.onload = (e) => {
+                    const img = new Image();
+                    img.onload = () => {
+                        const canvas = document.createElement('canvas');
+                        const ctx = canvas.getContext('2d');
+                        
+                        // Set canvas dimensions for thumbnail
+                        const maxSize = 120;
+                        let width = img.width;
+                        let height = img.height;
+                        
+                        if (width > height) {
+                            if (width > maxSize) {
+                                height *= maxSize / width;
+                                width = maxSize;
+                            }
+                        } else {
+                            if (height > maxSize) {
+                                width *= maxSize / height;
+                                height = maxSize;
+                            }
+                        }
+                        
+                        canvas.width = width;
+                        canvas.height = height;
+                        ctx.drawImage(img, 0, 0, width, height);
+                        
+                        resolve(canvas.toDataURL('image/jpeg', 0.7));
+                    };
+                    img.onerror = reject;
+                    img.src = e.target.result;
+                };
+                reader.onerror = reject;
+                reader.readAsDataURL(file);
+            });
+        }
+        
+        function updateThumbnailGrid() {
+            thumbnailGrid.innerHTML = '';
+            
+            if (files.length === 0) {
+                thumbnailGrid.style.display = 'none';
+                analyzeButtonContainer.style.display = 'none';
+                return;
+            }
+            
+            thumbnailGrid.style.display = 'grid';
+            analyzeButtonContainer.style.display = 'block';
+            
+            files.forEach((file, index) => {
+                const thumbnailItem = document.createElement('div');
+                thumbnailItem.className = 'thumbnail-item';
+                thumbnailItem.dataset.index = index;
+                
+                const dataUrl = fileDataUrls[file.name] || 'data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg" width="100" height="100" viewBox="0 0 100 100"><rect width="100" height="100" fill="%23f0f0f0"/><text x="50" y="50" font-family="Arial" font-size="14" text-anchor="middle" fill="%23999">No preview</text></svg>';
+                
+                thumbnailItem.innerHTML = `
+                    <img src="${dataUrl}" alt="${file.name}" class="thumbnail-img">
+                    <div class="thumbnail-overlay">
+                        <span style="overflow: hidden; text-overflow: ellipsis; white-space: nowrap; max-width: 80px;">
+                            ${file.name}
+                        </span>
+                        <button class="remove-thumbnail" data-index="${index}">
+                            <i class="fas fa-times"></i>
+                        </button>
+                    </div>
+                `;
+                
+                thumbnailGrid.appendChild(thumbnailItem);
+            });
+            
+            // Add event listeners to remove buttons
+            document.querySelectorAll('.remove-thumbnail').forEach(btn => {
+                btn.addEventListener('click', (e) => {
+                    e.stopPropagation();
+                    const index = parseInt(e.currentTarget.dataset.index);
+                    removeFile(index);
+                });
+            });
+        }
+        
+        function removeFile(index) {
+            const removedFile = files[index].name;
+            files.splice(index, 1);
+            delete fileDataUrls[removedFile];
+            updateThumbnailGrid();
+            showToast(`Removed ${removedFile}`, 'warning');
+        }
+        
+        // Analysis
+        async function startAnalysis() {
+            if (files.length === 0) return;
+            
+            showLoading(true);
+            analyzeBtn.disabled = true;
+            analyzeBtn.innerHTML = '<span class="spinner"></span> Processing...';
+            
+            progressFill.style.width = '0%';
+            progressPercent.textContent = '0%';
+            currentFile.textContent = 'Starting analysis...';
+            progressStats.textContent = `0 / ${files.length}`;
+            progressContainer.classList.remove('hidden');
+            
+            clearResults();
+            
+            const formData = new FormData();
+            files.forEach(file => {
+                formData.append('files', file);
+            });
+            
+            try {
+                console.log('Sending batch request for', files.length, 'images...');
+                
+                const response = await fetch(BATCH_ENDPOINT, {
+                    method: 'POST',
+                    body: formData
+                });
+                
+                console.log('Response status:', response.status);
+                
+                if (!response.ok) {
+                    const errorText = await response.text();
+                    throw new Error(`HTTP ${response.status}: ${errorText}`);
+                }
+                
+                const apiResponse = await response.json();
+                console.log('API response:', apiResponse);
+                
+                showLoading(false);
+                
+                if (!apiResponse.success) {
+                    throw new Error(apiResponse.message || 'API request failed');
+                }
+                
+                const data = apiResponse.data;
+                console.log('Data:', data);
+                
+                if (data && data.batch_id) {
+                    console.log('Polling mode: batch_id =', data.batch_id);
+                    currentBatchId = data.batch_id;
+                    showToast('Analysis started. Processing in background...', 'success');
+                    startPollingProgress();
+                    
+                } else if (data && data.result) {
+                    console.log('Immediate results mode');
+                    
+                    progressFill.style.width = '100%';
+                    progressPercent.textContent = '100%';
+                    currentFile.textContent = 'Processing complete!';
+                    progressStats.textContent = `${files.length} / ${files.length}`;
+                    
+                    batchResults = data.result;
+                    
+                    setTimeout(() => {
+                        displayResults();
+                        resetUI();
+                        
+                        resultsSection.classList.remove('hidden');
+                        document.getElementById('resultsSection').scrollIntoView({ 
+                            behavior: 'smooth',
+                            block: 'start'
+                        });
+                        
+                        showToast(`Analysis complete! Processed ${files.length} image(s)`, 'success');
+                    }, 500);
+                    
+                } else {
+                    console.error('Unexpected response format:', apiResponse);
+                    throw new Error('Invalid response format from server');
+                }
+                
+            } catch (error) {
+                console.error('Analysis failed:', error);
+                showLoading(false);
+                showToast('Analysis failed: ' + error.message, 'error');
+                resetUI();
+            }
+        }
+        
+        function startPollingProgress() {
+            if (pollingInterval) clearInterval(pollingInterval);
+            
+            pollingInterval = setInterval(async () => {
+                try {
+                    const response = await fetch(`${BATCH_PROGRESS_ENDPOINT}/${currentBatchId}/progress`);
+                    const data = await response.json();
+                    
+                    const sessionData = data.data || data;
+                    
+                    if (sessionData.status === 'completed') {
+                        clearInterval(pollingInterval);
+                        
+                        if (sessionData.result) {
+                            batchResults = sessionData.result;
+                        } else {
+                            batchResults = sessionData;
+                        }
+                        
+                        displayResults();
+                        resetUI();
+                        
+                        resultsSection.classList.remove('hidden');
+                        document.getElementById('resultsSection').scrollIntoView({ 
+                            behavior: 'smooth',
+                            block: 'start'
+                        });
+                        
+                        showToast('Batch analysis completed!', 'success');
+                        
+                    } else if (sessionData.status === 'processing') {
+                        const progress = sessionData.progress;
+                        if (progress) {
+                            const percent = Math.round((progress.current / progress.total) * 100);
+                            
+                            progressFill.style.width = `${percent}%`;
+                            progressPercent.textContent = `${percent}%`;
+                            currentFile.textContent = progress.filename || 'Processing...';
+                            progressStats.textContent = `${progress.current} / ${progress.total}`;
+                        }
+                    } else if (sessionData.status === 'failed' || sessionData.status === 'interrupted') {
+                        clearInterval(pollingInterval);
+                        showToast(`Analysis failed: ${sessionData.error || 'Unknown error'}`, 'error');
+                        resetUI();
+                    }
+                } catch (error) {
+                    console.error('Progress polling failed:', error);
+                }
+            }, 1000);
+        }
+        
+        function displayResults() {
+            if (!batchResults) {
+                console.error('No results to display:', batchResults);
+                return;
+            }
+            
+            console.log('Displaying batch results:', batchResults);
+            
+            const results = batchResults.results || [];
+            console.log('Results array:', results);
+            
+            updateSummary(batchResults);
+            
+            resultsTableBody.innerHTML = '';
+            
+            results.forEach((result, index) => {
+                const row = document.createElement('tr');
+                row.dataset.index = index;
+                
+                const resultData = result;
+                
+                const filename = resultData.filename || 'Unknown';
+                const overallScore = resultData.overall_score || 0;
+                const status = resultData.status || 'LIKELY_AUTHENTIC';
+                const confidence = resultData.confidence || 0;
+                const imageSize = resultData.image_size || [0, 0];
+                const signals = resultData.signals || [];
+                const processingTime = resultData.processing_time || 0;
+                
+                const scorePercent = Math.round(overallScore * 100);
+                let scoreClass = 'score-low';
+                let scoreWidth = '30%';
+                if (scorePercent >= 70) {
+                    scoreClass = 'score-high';
+                    scoreWidth = '90%';
+                } else if (scorePercent >= 50) {
+                    scoreClass = 'score-medium';
+                    scoreWidth = '60%';
+                }
+                
+                const flaggedCount = signals.filter(s => s.status === 'flagged').length;
+                const warningCount = signals.filter(s => s.status === 'warning').length;
+                
+                // Format status for display (remove underscores)
+                const displayStatus = status.replace(/_/g, ' ');
+                
+                // Get thumbnail
+                const thumbnailSrc = fileDataUrls[filename] || 'data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg" width="40" height="40" viewBox="0 0 40 40"><rect width="40" height="40" fill="%23f0f0f0"/></svg>';
+                
+                row.innerHTML = `
+                    <td style="min-width: 200px;">
+                        <div style="display: flex; align-items: center; gap: 0.75rem;">
+                            <img src="${thumbnailSrc}" alt="${filename}" style="width: 40px; height: 40px; object-fit: cover; border-radius: 0.25rem; border: 1px solid var(--border);">
+                            <div>
+                                <div style="font-weight: 500; font-size: 0.875rem;">${filename}</div>
+                                <div style="font-size: 0.75rem; color: var(--text-light);">
+                                    ${imageSize[0]} × ${imageSize[1]}
+                                </div>
+                            </div>
+                        </div>
+                    </td>
+                    <td>
+                        <span class="status-badge ${status === 'LIKELY_AUTHENTIC' ? 'status-authentic' : 'status-review'}" style="white-space: nowrap;">
+                            ${displayStatus}
+                        </span>
+                    </td>
+                    <td>
+                        <div class="score-indicator">
+                            <span style="min-width: 40px; font-size: 0.875rem;">${scorePercent}%</span>
+                            <div class="score-bar">
+                                <div class="score-fill ${scoreClass}" style="width: ${scoreWidth}"></div>
+                            </div>
+                        </div>
+                    </td>
+                    <td style="min-width: 150px;">
+                        <div style="display: flex; gap: 0.25rem; flex-wrap: wrap;">
+                            ${flaggedCount > 0 ? `<span class="signal-badge signal-flagged" style="font-size: 0.7rem;">${flaggedCount} flagged</span>` : ''}
+                            ${warningCount > 0 ? `<span class="signal-badge signal-warning" style="font-size: 0.7rem;">${warningCount} warning</span>` : ''}
+                            ${signals.length - flaggedCount - warningCount > 0 ? 
+                            `<span class="signal-badge signal-passed" style="font-size: 0.7rem;">${signals.length - flaggedCount - warningCount} passed</span>` : ''}
+                        </div>
+                    </td>
+                    <td>
+                        <button class="action-button secondary-action view-detail-btn" data-index="${index}" title="View Details" style="padding: 0.25rem 0.5rem;">
+                            <i class="fas fa-eye"></i>
+                        </button>
+                    </td>
+                `;
+                
+                resultsTableBody.appendChild(row);
+            });
+            
+            noResultsRow.classList.add('hidden');
+            
+            document.querySelectorAll('.view-detail-btn').forEach(btn => {
+                btn.addEventListener('click', (e) => {
+                    e.stopPropagation();
+                    const index = parseInt(e.currentTarget.dataset.index);
+                    showDetailedAnalysis(index);
+                });
+            });
+            
+            document.querySelectorAll('#resultsTableBody tr').forEach(row => {
+                row.addEventListener('click', (e) => {
+                    if (!e.target.closest('.view-detail-btn')) {
+                        const index = parseInt(row.dataset.index);
+                        showDetailedAnalysis(index);
+                    }
+                });
+            });
+        }
+        
+        function updateSummary(batchResult) {
+            const total = batchResult.total_images || 0;
+            const processed = batchResult.processed || batchResult.results?.length || 0;
+            const failed = batchResult.failed || 0;
+            
+            let likelyAuthentic = 0;
+            let reviewRequired = 0;
+            
+            if (batchResult.results) {
+                batchResult.results.forEach(result => {
+                    const resultData = result;
+                    const status = resultData.status || 'LIKELY_AUTHENTIC';
+                    if (status === 'LIKELY_AUTHENTIC') {
+                        likelyAuthentic++;
+                    } else if (status === 'REVIEW_REQUIRED') {
+                        reviewRequired++;
+                    }
+                });
+            }
+            
+            resultsSummary.innerHTML = `
+                <div class="summary-card">
+                    <div class="summary-value">${processed}</div>
+                    <div class="summary-label">Total Processed</div>
+                </div>
+                <div class="summary-card">
+                    <div class="summary-value">${likelyAuthentic}</div>
+                    <div class="summary-label">Likely Authentic</div>
+                </div>
+                <div class="summary-card">
+                    <div class="summary-value">${reviewRequired}</div>
+                    <div class="summary-label">Review Required</div>
+                </div>
+                <div class="summary-card">
+                    <div class="summary-value">${failed}</div>
+                    <div class="summary-label">Failed</div>
+                </div>
+            `;
+        }
+        
+        function showDetailedAnalysis(index) {
+            if (!batchResults || !batchResults.results || !batchResults.results[index]) return;
+            
+            selectedImageIndex = index;
+            const result = batchResults.results[index];
+            const resultData = result;
+            
+            const filename = resultData.filename || 'Unknown';
+            const overallScore = resultData.overall_score || 0;
+            const status = resultData.status || 'LIKELY_AUTHENTIC';
+            const confidence = resultData.confidence || 0;
+            const imageSize = resultData.image_size || [0, 0];
+            const processingTime = resultData.processing_time || 0;
+            const signals = resultData.signals || [];
+            
+            const scorePercent = Math.round(overallScore * 100);
+            const displayStatus = status.replace(/_/g, ' ');
+            
+            // Ensure detailed analysis is expanded
+            detailedAnalysisContent.classList.add('show');
+            detailedAnalysisIcon.classList.remove('fa-chevron-down');
+            detailedAnalysisIcon.classList.add('fa-chevron-up');
+            
+            document.getElementById('detailedAnalysisContent').scrollIntoView({ 
+                behavior: 'smooth',
+                block: 'start'
+            });
+            
+            // Build signals HTML
+            let signalsHtml = '';
+            if (signals && signals.length > 0) {
+                signals.forEach(signal => {
+                    let statusClass = 'signal-passed';
+                    if (signal.status === 'warning') statusClass = 'signal-warning';
+                    if (signal.status === 'flagged') statusClass = 'signal-flagged';
+                    
+                    const signalScore = Math.round((signal.score || 0) * 100);
+                    
+                    signalsHtml += `
+                        <div class="signal-card">
+                            <div class="signal-header">
+                                <strong>${signal.name || 'Unknown Metric'}</strong>
+                                <span class="signal-badge ${statusClass}">${signal.status}</span>
+                            </div>
+                            <p style="font-size: 0.875rem; margin-bottom: 0.5rem; color: var(--text-light);">
+                                ${signal.explanation || 'No explanation available.'}
+                            </p>
+                            <div style="display: flex; justify-content: space-between; align-items: center;">
+                                <div style="font-size: 0.75rem; color: var(--text-light);">
+                                    Score: ${signalScore}%
+                                </div>
+                            </div>
+                        </div>
+                    `;
+                });
+            } else {
+                signalsHtml = '<p class="text-center" style="color: var(--text-light);">No detection signals available.</p>';
+            }
+            
+            detailedAnalysisContent.innerHTML = `
+                <div style="margin-bottom: 1.5rem;">
+                    <div style="display: flex; align-items: center; gap: 1rem; margin-bottom: 1rem;">
+                        <img src="${fileDataUrls[filename] || 'data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg" width="60" height="60" viewBox="0 0 60 60"><rect width="60" height="60" fill="%23f0f0f0"/></svg>'}" 
+                             alt="${filename}" 
+                             style="width: 60px; height: 60px; object-fit: cover; border-radius: 0.5rem; border: 1px solid var(--border);">
+                        <div>
+                            <h4 style="margin-bottom: 0.25rem;">${filename}</h4>
+                            <div style="font-size: 0.875rem; color: var(--text-light);">
+                                ${imageSize[0]} × ${imageSize[1]} • ${processingTime.toFixed(2)}s
+                            </div>
+                        </div>
+                    </div>
+                    
+                    <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(150px, 1fr)); gap: 1rem; margin-bottom: 1.5rem;">
+                        <div style="text-align: center; padding: 1rem; background-color: #f8fafc; border-radius: 0.5rem;">
+                            <div style="font-size: 1.5rem; font-weight: 700; color: ${scorePercent >= 70 ? '#e53e3e' : scorePercent >= 50 ? '#d69e2e' : '#38a169'};">${scorePercent}%</div>
+                            <div style="font-size: 0.875rem; color: var(--text-light);">Score</div>
+                        </div>
+                        <div style="text-align: center; padding: 1rem; background-color: #f8fafc; border-radius: 0.5rem;">
+                            <div style="font-size: 1.5rem; font-weight: 700; color: ${displayStatus.includes('REVIEW') ? '#d69e2e' : '#38a169'};">${displayStatus}</div>
+                            <div style="font-size: 0.875rem; color: var(--text-light);">Verdict</div>
+                        </div>
+                        <div style="text-align: center; padding: 1rem; background-color: #f8fafc; border-radius: 0.5rem;">
+                            <div style="font-size: 1.5rem; font-weight: 700;">${confidence}%</div>
+                            <div style="font-size: 0.875rem; color: var(--text-light);">Confidence</div>
+                        </div>
+                    </div>
+                </div>
+                
+                <h4 style="margin-bottom: 1rem;">Detection Signals</h4>
+                <div class="signal-grid">
+                    ${signalsHtml}
+                </div>
+                
+                <div class="signal-card" style="margin-top: 1.5rem; background-color: ${displayStatus.includes('REVIEW') ? 'rgba(214, 158, 46, 0.1)' : 'rgba(56, 161, 105, 0.1)'}; border-color: ${displayStatus.includes('REVIEW') ? 'rgba(214, 158, 46, 0.3)' : 'rgba(56, 161, 105, 0.3)'};">
+                    <div class="signal-header">
+                        <strong>Recommendation</strong>
+                    </div>
+                    <p style="margin-bottom: 0.5rem;">
+                        ${displayStatus.includes('REVIEW') ? 'Manual verification recommended' : 'No immediate action required'}
+                    </p>
+                    <div style="font-size: 0.875rem; color: var(--text-light);">
+                        Confidence: ${confidence}% likelihood of ${displayStatus.includes('REVIEW') ? 'AI generation' : 'authenticity'}
+                    </div>
+                </div>
+            `;
+        }
+        
+        // Export functions
+        async function exportCsv() {
+            if (!currentBatchId) {
+                showToast('No analysis results to export.', 'warning');
+                return;
+            }
+            
+            showLoading(true);
+            try {
+                // Using GET request since backend now accepts both GET and POST
+                const response = await fetch(`${CSV_REPORT_ENDPOINT}/${currentBatchId}`);
+                
+                if (response.ok) {
+                    // Get the blob data
+                    const blob = await response.blob();
+                    
+                    // Create download link
+                    const downloadLink = document.createElement('a');
+                    downloadLink.href = URL.createObjectURL(blob);
+                    downloadLink.download = `ai_screener_report_${currentBatchId}.csv`;
+                    
+                    document.body.appendChild(downloadLink);
+                    downloadLink.click();
+                    document.body.removeChild(downloadLink);
+                    
+                    showToast('CSV report downloaded successfully.', 'success');
+                } else {
+                    showToast('Failed to generate CSV report.', 'error');
+                }
+            } catch (error) {
+                console.error('CSV export failed:', error);
+                showToast('CSV export failed. Please try again.', 'error');
+            } finally {
+                showLoading(false);
+            }
+        }
+
+        async function exportPdf() {
+            if (!currentBatchId) {
+                showToast('No analysis results to export.', 'warning');
+                return;
+            }
+            
+            showLoading(true);
+            try {
+                // Using GET request since backend now accepts both GET and POST
+                const response = await fetch(`${PDF_REPORT_ENDPOINT}/${currentBatchId}`);
+                
+                if (response.ok) {
+                    // Get the blob data
+                    const blob = await response.blob();
+                    
+                    // Create download link
+                    const downloadLink = document.createElement('a');
+                    downloadLink.href = URL.createObjectURL(blob);
+                    downloadLink.download = `ai_screener_report_${currentBatchId}.pdf`;
+                    
+                    document.body.appendChild(downloadLink);
+                    downloadLink.click();
+                    document.body.removeChild(downloadLink);
+                    
+                    showToast('PDF report downloaded successfully.', 'success');
+                } else {
+                    showToast('Failed to generate PDF report.', 'error');
+                }
+            } catch (error) {
+                console.error('PDF export failed:', error);
+                showToast('PDF export failed. Please try again.', 'error');
+            } finally {
+                showLoading(false);
+            }
+        }
+
+        async function exportJson() {
+            if (!batchResults) {
+                showToast('No analysis results to export.', 'warning');
+                return;
+            }
+            
+            showLoading(true);
+            try {
+                const dataStr = JSON.stringify(batchResults, null, 2);
+                const dataBlob = new Blob([dataStr], {type: 'application/json'});
+                
+                const downloadLink = document.createElement('a');
+                downloadLink.href = URL.createObjectURL(dataBlob);
+                downloadLink.download = `ai_image_screener_${new Date().toISOString().split('T')[0]}_${currentBatchId || 'report'}.json`;
+                
+                document.body.appendChild(downloadLink);
+                downloadLink.click();
+                document.body.removeChild(downloadLink);
+                
+                showToast('JSON report downloaded successfully.', 'success');
+            } catch (error) {
+                console.error('JSON export failed:', error);
+                showToast('JSON export failed. Please try again.', 'error');
+            } finally {
+                showLoading(false);
+            }
+        }
+        
+        // Reset functions
+        function resetUI() {
+            analyzeBtn.disabled = false;
+            analyzeBtn.innerHTML = '<div class="btn-content"><i class="fas fa-play"></i> Start Analysis</div>';
+            
+            setTimeout(() => {
+                progressContainer.classList.add('hidden');
+            }, 2000);
+        }
+        
+        function resetAnalysis() {
+            files = [];
+            fileDataUrls = {};
+            batchResults = null;
+            currentBatchId = null;
+            selectedImageIndex = null;
+            
+            updateThumbnailGrid();
+            clearResults();
+            resultsSection.classList.add('hidden');
+            detailedAnalysisContent.innerHTML = '<p id="noDetailedAnalysis" class="text-center" style="color: var(--text-light); padding: 2rem;"><i class="fas fa-eye" style="font-size: 2rem; margin-bottom: 1rem; opacity: 0.5;"></i><br>Select an image to view detailed analysis</p>';
+            
+            window.scrollTo({ top: 0, behavior: 'smooth' });
+            showToast('Analysis reset. Ready for new upload.', 'success');
+        }
+        
+        function clearResults() {
+            resultsSummary.innerHTML = '';
+            resultsTableBody.innerHTML = '';
+            noResultsRow.classList.remove('hidden');
+            
+            if (pollingInterval) {
+                clearInterval(pollingInterval);
+                pollingInterval = null;
+            }
+        }
+        
+        // API health check
+        async function checkApiHealth() {
+            try {
+                const response = await fetch(HEALTH_ENDPOINT);
+                const data = await response.json();
+                
+                if (data.status === 'ok') {
+                    console.log('API connected successfully');
+                }
+            } catch (error) {
+                console.error('API health check failed:', error);
+            }
+        }
+    </script>
+</body>
+</html>

utils/__init__.py

@@ -0,0 +1,19 @@
+from .logger import get_logger
+from .image_processor import ImageProcessor
+from .validators import ImageValidator
+from .helpers import (
+    generate_unique_id,
+    cleanup_old_files,
+    format_filesize,
+    calculate_hash
+)
+
+__all__ = [
+    'get_logger',
+    'ImageProcessor',
+    'ImageValidator',
+    'generate_unique_id',
+    'cleanup_old_files',
+    'format_filesize',
+    'calculate_hash'
+]

utils/helpers.py

@@ -0,0 +1,108 @@
+# Dependencies
+import re
+import uuid
+import hashlib
+from pathlib import Path
+from datetime import datetime
+from datetime import timedelta
+from utils.logger import get_logger
+
+
+# Setup Logging
+logger = get_logger(__name__)
+
+
+def generate_unique_id() -> str:
+    """
+    Generate unique ID for files/reports
+    """
+    unique_id = f"{datetime.now().strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex[:8]}"
+    
+    return unique_id
+
+
+def calculate_hash(file_path: Path) -> str:
+    """
+    Calculate SHA256 hash of file
+    """
+    sha256 = hashlib.sha256()
+
+    with open(file_path, 'rb') as f:
+        for chunk in iter(lambda: f.read(8192), b''):
+            sha256.update(chunk)
+    
+    hash = sha256.hexdigest()
+
+    return hash
+
+
+def format_filesize(size_bytes: int) -> str:
+    """
+    Format file size in human-readable format
+    """
+    for unit in ['B', 'KB', 'MB', 'GB']:
+        if (size_bytes < 1024.0):
+            return f"{size_bytes:.2f} {unit}"
+
+        size_bytes /= 1024.0
+
+    file_size = f"{size_bytes:.2f} TB"
+    
+    return file_size
+
+
+def cleanup_old_files(directory: Path, days: int = 7) -> int:
+    """
+    Clean up files older than specified days
+    
+    Arguments:
+    ----------
+        directory { Path } : Directory to clean
+        
+        days      { int }  : Files older than this will be deleted
+    
+    Returns:
+    --------
+            { int }        : Number of files deleted
+    """
+    if not directory.exists():
+        return 0
+    
+    cutoff  = datetime.now() - timedelta(days = days)
+    deleted = 0
+    
+    for file_path in directory.iterdir():
+        if file_path.is_file():
+            file_time = datetime.fromtimestamp(file_path.stat().st_mtime)
+            
+            if (file_time < cutoff):
+                try:
+                    file_path.unlink()
+                    deleted += 1
+                    logger.debug(f"Deleted old file: {file_path.name}")
+
+                except Exception as e:
+                    logger.error(f"Failed to delete {file_path.name}: {e}")
+    
+    if (deleted > 0):
+        logger.info(f"Cleaned up {deleted} files from {directory.name}")
+    
+    return deleted
+
+
+def safe_filename(filename: str) -> str:
+    """
+    Sanitize filename for safe storage
+    """
+    # Remove any path components
+    filename = Path(filename).name
+    
+    # Replace unsafe characters
+    filename = re.sub(r'[^\w\s.-]', '', filename)
+    
+    # Limit length
+    if (len(filename) > 255):
+        name, ext = filename.rsplit('.', 1) if '.' in filename else (filename, '')
+        filename  = name[:250] + ('.' + ext if ext else '')
+        
+    return filename

utils/image_processor.py

@@ -0,0 +1,163 @@
+# Dependencies
+import cv2
+import numpy as np
+from PIL import Image
+from pathlib import Path
+from typing import Tuple
+from typing import Optional
+from utils.logger import get_logger
+from config.constants import LUMINANCE_WEIGHTS
+
+
+# Setup Logging
+logger = get_logger(__name__)
+
+
+class ImageProcessor:
+    """
+    Image loading and preprocessing utilities
+    """
+    @staticmethod
+    def load_image(file_path: Path) -> np.ndarray:
+        """
+        Load image as numpy array in RGB format
+        
+        Arguments:
+        ----------
+            file_path { Path } : Path of the image file needs to be loaded
+
+        Returns:
+        --------
+            { np.ndarray }     : Image array in RGB format (H, W, 3)
+        """
+        try:
+            image = cv2.imread(str(file_path))
+            
+            if image is None:
+                raise ValueError(f"Failed to load image: {file_path}")
+            
+            # Convert BGR to RGB
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+            logger.debug(f"Loaded image: {file_path.name} shape={image.shape}")
+            return image
+
+        except Exception as e:
+            logger.error(f"Error loading image {file_path}: {e}")
+            raise
+
+    
+    @staticmethod
+    def rgb_to_luminance(image: np.ndarray) -> np.ndarray:
+        """
+        Convert RGB image to luminance using ITU-R BT.709 standard
+        
+        Arguments:
+        ----------
+            image { np.ndarray } : RGB image array (H, W, 3)
+        
+        Returns:
+        --------
+             { np.ndarray }      : Luminance array (H, W)
+        """
+        if ((image.ndim != 3) or (image.shape[2] != 3)):
+            raise ValueError(f"Expected RGB image (H, W, 3), got shape {image.shape}")
+        
+        r, g, b   = LUMINANCE_WEIGHTS
+        
+        luminance = r * image[:, :, 0] + g * image[:, :, 1] + b * image[:, :, 2]
+        
+        return luminance.astype(np.float32)
+
+    
+    @staticmethod
+    def compute_gradients(luminance: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Compute Sobel gradients
+        
+        Arguments:
+        ----------
+            luminance { np.ndarray } : Luminance array (H, W)
+        
+        Returns:
+        --------
+                   { tuple }         : Tuple of (gradient_x, gradient_y)
+        """
+        gx = cv2.Sobel(luminance, cv2.CV_64F, 1, 0, ksize = 3)
+        gy = cv2.Sobel(luminance, cv2.CV_64F, 0, 1, ksize = 3)
+
+        return gx, gy
+    
+
+    @staticmethod
+    def normalize_image(image: np.ndarray) -> np.ndarray:
+        """
+        Normalize image to [0, 1] range
+        """
+        normalized_image = image.astype(np.float32) / 255.0
+        
+        return normalized_image
+    
+
+    @staticmethod
+    def resize_if_needed(image: np.ndarray, max_dimension: int = 2048) -> np.ndarray:
+        """
+        Resize image if larger than max_dimension while maintaining aspect ratio
+        
+        Arguments:
+        ----------
+            image        { np.ndarray } : Input image
+
+            max_dimension   { int }     : Maximum dimension (width or height)
+        
+        Returns:
+        --------
+                 { np.ndarray }         : Resized image if needed, otherwise original
+        """
+        h, w = image.shape[:2]
+
+        if (max(h, w) <= max_dimension):
+            return image
+        
+        scale   = max_dimension / max(h, w)
+        new_w   = int(w * scale)
+        new_h   = int(h * scale)
+        
+        resized = cv2.resize(image, (new_w, new_h), interpolation = cv2.INTER_AREA)
+        
+        logger.debug(f"Resized image from {w}x{h} to {new_w}x{new_h}")
+
+        return resized
+    
+    @staticmethod
+    def extract_patches(image: np.ndarray, patch_size: int, stride: int, max_patches: Optional[int] = None) -> np.ndarray:
+        """
+        Extract patches from image
+        
+        Arguments:
+        ----------
+            image      { np.ndarray } : Input image (H, W) or (H, W, C)
+            
+            patch_size    { int }     : Size of patches
+            
+            stride        { int }     : Stride between patches
+            
+            max_patches   { int }     : Maximum number of patches to extract
+        
+        Returns:
+        --------
+                 { np.ndarray }       : Array of patches
+        """
+        h, w    = image.shape[:2]
+        patches = list()
+        
+        for y in range(0, h - patch_size + 1, stride):
+            for x in range(0, w - patch_size + 1, stride):
+                patch = image[y:y+patch_size, x:x+patch_size]
+
+                patches.append(patch)
+                
+                if (max_patches and (len(patches) >= max_patches)):
+                    return np.array(patches)
+        
+        return np.array(patches)

utils/logger.py

@@ -0,0 +1,85 @@
+# Dependencies
+import sys
+import logging
+from datetime import datetime
+from config.settings import settings
+
+
+class ColoredFormatter(logging.Formatter):
+    """
+    Colored log formatter for better readability
+    """
+    COLORS = {'DEBUG'    : '\033[36m',  # Cyan
+              'INFO'     : '\033[32m',  # Green
+              'WARNING'  : '\033[33m',  # Yellow
+              'ERROR'    : '\033[31m',  # Red
+              'CRITICAL' : '\033[35m',  # Magenta
+              'RESET'    : '\033[0m',
+             }
+    
+
+    def format(self, record):
+        if sys.stdout.isatty():
+            levelname = record.levelname
+
+            if (levelname in self.COLORS):
+                record.levelname = f"{self.COLORS[levelname]}{levelname}{self.COLORS['RESET']}"
+        
+        return super().format(record)
+
+
+def setup_logger(name: str = None) -> logging.Logger:
+    """
+    Setup logger with console and file handlers
+    
+    Arguments:
+    ----------
+        name   { str }     : Logger name (defaults to root logger)
+    
+    Returns:
+    --------
+        { logging.Logger } : Configured logger instance
+    """
+    logger = logging.getLogger(name or settings.APP_NAME)
+    
+    # Avoid duplicate handlers
+    if logger.handlers:
+        return logger
+    
+    level             = getattr(logging, settings.LOG_LEVEL, logging.INFO)
+    logger.setLevel(level)
+
+    logger.propagate  = False
+    
+    # Console handler with colors
+    console_handler   = logging.StreamHandler(sys.stdout)
+    console_handler.setLevel(logging.DEBUG if settings.DEBUG else logging.INFO)
+    
+    console_formatter = ColoredFormatter('%(asctime)s | %(levelname)-8s | %(name)s | %(message)s',
+                                         datefmt = '%Y-%m-%d %H:%M:%S'
+                                        )
+    console_handler.setFormatter(console_formatter)
+
+    logger.addHandler(console_handler)
+    
+    # File handler
+    log_file          = settings.LOGS_DIR / f"app_{datetime.now().strftime('%Y%m%d')}.log"
+    file_handler      = logging.FileHandler(log_file)
+    file_handler.setLevel(logging.DEBUG)
+
+    file_formatter    = logging.Formatter('%(asctime)s | %(levelname)-8s | %(name)s | %(funcName)s:%(lineno)d | %(message)s',
+                                          datefmt = '%Y-%m-%d %H:%M:%S'
+                                         )
+
+    file_handler.setFormatter(file_formatter)
+    
+    logger.addHandler(file_handler)
+    
+    return logger
+
+
+def get_logger(name: str = None) -> logging.Logger:
+    """
+    Get or create logger instance
+    """
+    return setup_logger(name)

utils/validators.py

@@ -0,0 +1,108 @@
+# Dependencies
+import magic
+from PIL import Image
+from pathlib import Path
+from typing import Tuple
+from utils.logger import get_logger
+from config.settings import settings
+from config.constants import MIN_IMAGE_DIMENSION
+from config.constants import MAX_IMAGE_DIMENSION
+
+
+# Setup Logging
+logger = get_logger(__name__)
+
+
+class ValidationError(Exception):
+    """
+    Custom validation error
+    """
+    pass
+
+
+class ImageValidator:
+    """
+    Validate uploaded images
+    """
+    @staticmethod
+    def validate_file_size(file_size: int) -> None:
+        """
+        Validate file size
+        """
+        if (file_size > settings.max_file_size_bytes):
+            raise ValidationError(f"File size {file_size} bytes exceeds maximum {settings.max_file_size_bytes} bytes")
+
+        if (file_size == 0):
+            raise ValidationError("File is empty")
+    
+
+    @staticmethod
+    def validate_file_extension(filename: str) -> None:
+        """
+        Validate file extension
+        """
+        extension = Path(filename).suffix.lower()
+        
+        if (extension not in settings.ALLOWED_EXTENSIONS):
+            raise ValidationError(f"File extension {extension} not allowed. Allowed: {', '.join(settings.ALLOWED_EXTENSIONS)}")
+    
+
+    @staticmethod
+    def validate_image_content(file_path: Path) -> Tuple[int, int]:
+        """
+        Validate image can be opened and get dimensions
+        """
+        try:
+            with Image.open(file_path) as image:
+                width, height = image.size
+                
+                # Validate dimensions
+                if ((width < MIN_IMAGE_DIMENSION) or (height < MIN_IMAGE_DIMENSION)):
+                    raise ValidationError(f"Image dimensions ({width}x{height}) too small. Minimum: {MIN_IMAGE_DIMENSION}px")
+                
+                if ((width > MAX_IMAGE_DIMENSION) or (height > MAX_IMAGE_DIMENSION)):
+                    raise ValidationError(f"Image dimensions ({width}x{height}) too large. Maximum: {MAX_IMAGE_DIMENSION}px")
+                
+                # Verify format
+                if (image.format.lower() not in ['jpeg', 'png', 'webp']):
+                    raise ValidationError(f"Unsupported image format: {image.format}")
+                
+                return width, height
+                
+        except ValidationError:
+            raise
+
+        except Exception as e:
+            raise ValidationError(f"Cannot open image: {str(e)}")
+    
+
+    @staticmethod
+    def validate_mime_type(file_path: Path) -> None:
+        """
+        Validate MIME type matches image
+        """
+        try:
+            mime = magic.from_file(str(file_path), mime = True)
+
+            if (not mime.startswith('image/')):
+                raise ValidationError(f"File is not an image. MIME type: {mime}")
+        
+        except Exception as e:
+            logger.warning(f"MIME type validation failed: {e}")
+            # Don't fail if python-magic is not available
+    
+
+    @classmethod
+    def validate_image(cls, file_path: Path, filename: str, file_size: int) -> Tuple[int, int]:
+        """
+        Comprehensive image validation
+        """
+        cls.validate_file_size(file_size)
+        cls.validate_file_extension(filename)
+
+        dimensions = cls.validate_image_content(file_path)
+        cls.validate_mime_type(file_path)  # Optional, commented out if python-magic not available
+        
+        logger.debug(f"Validated image: {filename} ({dimensions[0]}x{dimensions[1]})")
+
+        return dimensions