updated .gitignore

.gitignore

@@ -20,4 +20,4 @@ tests/
 # Models / large files
 *.pt
 *.bin
-*.onnx
+*.onnx

README_HUGGINGFACE.md

@@ -1,128 +0,0 @@
----
-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.

setup.sh

@@ -1,138 +0,0 @@
-#!/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 ""