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DockerFile.txt

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+# Use a lightweight official Python image
+FROM python:3.10-slim
+
+# Set working directory
+WORKDIR /app
+
+# Copy project files
+COPY . .
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose Hugging Face’s default port
+EXPOSE 7860
+
+# Launch FastAPI
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

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+---
+title: AI Mirror Backend
+emoji: 🪞
+colorFrom: indigo
+colorTo: pink
+sdk: docker
+app_file: app.py
+---
+
+## 💡 Project Overview
+
+In today's digital world, people express thousands of thoughts through messages, tweets, and posts — but rarely pause to see what those words say about their *emotions*.
+**AI Mirror** is an innovative **AI + GenAI-powered Data Analytics project** that transforms text into emotional insights, visualizes mood trends, and even generates AI-based art that represents how you *feel*.
+
+It's not just analytics — it's **emotion intelligence visualized through data.**
+
+---
+
+## 🧠 Core Idea
+
+AI Mirror reads and interprets your text, detects emotions using NLP models, and then creates:
+
+* **Mood Analysis Graphs**
+* **AI-generated summaries**
+* **Emotion-based artwork**
+* **Personalized mood reports**
+
+It acts as your **digital emotional reflection**, powered by data science and creativity.
+
+---
+
+## 📊 Datasets Used
+
+### 🧩 1. Emotion Dataset for NLP (Kaggle)
+* Contains ~20,000 text samples labeled with 6 emotions: *joy, sadness, anger, fear, love, surprise.*
+* Used for training and initial testing of the emotion classification model.
+
+### 🔮 2. GoEmotions (Google / Hugging Face)
+* Contains 58,000 Reddit comments labeled with 27 nuanced emotions.
+* Used to enhance realism and deepen emotional understanding.
+
+---
+
+## 📈 Key Features
+
+✨ **Emotion Analytics Dashboard** – Displays emotion intensity, trends, and sentiment graphs.  
+🧠 **AI Mood Summary** – ChatGPT/Gemini writes human-like emotional insights.  
+🎨 **Mood Landscape Generator** – AI creates stunning 4K nature landscapes with weather/seasons matching your emotions.  
+📘 **PDF Report Export** – Auto-generated "Weekly Mood Reflection Report."  
+💬 **Chatbot Mode** – "Ask your AI Mirror how you feel today."
+
+---
+
+## 🧰 Tech Stack
+
+| Category             | Tools & Frameworks                                     |
+| -------------------- | ------------------------------------------------------ |
+| Data Handling        | Pandas, NumPy                                          |
+| NLP & Modeling       | Hugging Face Transformers (BERT, DistilBERT), TextBlob |
+| Visualization        | Plotly, Seaborn, Matplotlib                            |
+| GenAI APIs           | ChatGPT (OpenAI), Google Gemini                        |
+| AI Image Generation  | DALL·E / Stable Diffusion                              |
+| Frontend             | React, TailwindCSS, shadcn/ui                          |
+| Backend              | FastAPI, Python                                        |
+| Deployment           | Netlify (Frontend) + Render (Backend)                  |
+
+---
+
+## 🚀 Getting Started
+
+### Prerequisites
+- Node.js 18+ and npm
+- Python 3.9+
+- API Keys: OpenAI, Google Gemini (optional)
+
+### Backend Setup
+
+```bash
+cd backend
+pip install -r requirements.txt
+python download_models.py  # Download NLP models
+uvicorn main:app --reload
+```
+
+### Frontend Setup
+
+```bash
+cd frontend
+npm install
+npm run dev
+```
+
+---
+
+## 📁 Project Structure
+
+```
+ai-mirror/
+├── backend/
+│   ├── main.py                 # FastAPI application
+│   ├── emotion_detector.py     # NLP emotion detection
+│   ├── ai_insights.py          # LLM integration
+│   ├── art_generator.py        # GenAI art creation
+│   ├── visualizations.py       # Data visualization
+│   ├── pdf_generator.py        # Report generation
+│   └── requirements.txt
+├── frontend/
+│   ├── src/
+│   │   ├── components/         # React components
+│   │   ├── pages/              # Main pages
+│   │   └── App.jsx
+│   ├── package.json
+│   └── vite.config.js
+└── README.md
+```
+
+---
+
+## 🌟 Expected Output
+
+* Emotion frequency chart
+* Sentiment timeline
+* Word clouds of dominant emotions
+* AI-generated mood artwork
+* GPT-powered emotional summary
+* Personalized downloadable report
+
+Example:
+
+> **Detected Emotions:** 60% calm, 25% joy, 15% tiredness  
+> **AI Summary:** "You seem emotionally balanced yet slightly fatigued — a calm achiever mood."  
+> **AI Artwork Title:** *'Serenity in Motion'*
+
+---
+
+## 🚀 Deployment
+
+🌐 **Frontend:** React interface deployed on **Netlify**  
+🧩 **Backend:** FastAPI deployed on **Render**  
+🎨 **Model & Art Integration:**
+* Hugging Face Transformers for emotion detection
+* OpenAI/DALL·E API for art creation
+* Gemini/ChatGPT for summaries
+
+---
+
+## 📘 Skills Showcased
+
+✅ NLP & Text Analytics  
+✅ Data Cleaning & Feature Engineering  
+✅ Predictive Modeling (BERT)  
+✅ AI Summarization (LLMs)  
+✅ Visualization & Data Storytelling  
+✅ GenAI Integration (Image + Text Generation)  
+✅ Full-stack AI Deployment
+
+---
+
+## 👨‍💻 Developer
+
+**Sayed Mohd Zayeem Khateeb**  
+📧 Email: [zayeem.s.khateeb@gmail.com](mailto:zayeem.s.khateeb@gmail.com)  
+💼 LinkedIn: [linkedin.com/in/zayeemkhateeb](https://www.linkedin.com/in/zayeemkhateeb)
+
+---
+
+## 💫 Tagline
+
+> **"AI Mirror doesn't just read your words — it reflects your emotions."**
+
+---
+
+## 📄 Usage
+
+This project is available for learning and portfolio purposes.

app.py

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+from fastapi import FastAPI, HTTPException, UploadFile, File
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import FileResponse, JSONResponse
+from pydantic import BaseModel
+from typing import List, Optional
+import os
+from dotenv import load_dotenv
+import logging
+
+from emotion_detector import EmotionDetector
+from ai_insights import AIInsightGenerator
+from art_generator import MoodArtGenerator
+from visualizations import VisualizationGenerator
+from pdf_generator import PDFReportGenerator
+
+# Load environment variables
+load_dotenv()
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Initialize FastAPI app
+app = FastAPI(
+    title="AI Mirror API",
+    description="The Data-Driven Mood & Personality Visualizer",
+    version="1.0.0"
+)
+
+# Configure CORS
+origins = os.getenv("ALLOWED_ORIGINS", "http://localhost:5173").split(",")
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Initialize components
+emotion_detector = EmotionDetector()
+ai_insights = AIInsightGenerator()
+art_generator = MoodArtGenerator()
+viz_generator = VisualizationGenerator()
+pdf_generator = PDFReportGenerator()
+
+
+# Pydantic models
+class TextInput(BaseModel):
+    text: str
+    user_name: Optional[str] = "User"
+
+
+class MultipleTexts(BaseModel):
+    texts: List[str]
+    user_name: Optional[str] = "User"
+
+
+class EmotionResponse(BaseModel):
+    emotions: dict
+    dominant_emotion: str
+    sentiment_score: float
+    sentiment_label: str
+
+
+@app.get("/")
+async def root():
+    """Root endpoint with API information"""
+    return {
+        "message": "Welcome to AI Mirror API",
+        "tagline": "Your words reflect more than you think",
+        "version": "1.0.0",
+        "developer": {
+            "name": "Sayed Mohd Zayeem Khateeb",
+            "email": "zayeem.s.khateeb@gmail.com",
+            "linkedin": "https://www.linkedin.com/in/zayeemkhateeb"
+        }
+    }
+
+
+@app.get("/health")
+async def health_check():
+    """Health check endpoint"""
+    return {"status": "healthy", "service": "AI Mirror API"}
+
+
+@app.post("/api/analyze-emotion", response_model=EmotionResponse)
+async def analyze_emotion(input_data: TextInput):
+    """
+    Analyze emotion from a single text input
+    """
+    try:
+        logger.info(f"Analyzing emotion for text: {input_data.text[:50]}...")
+        result = emotion_detector.detect_emotion(input_data.text)
+        return result
+    except Exception as e:
+        logger.error(f"Error analyzing emotion: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/api/analyze-batch")
+async def analyze_batch(input_data: MultipleTexts):
+    """
+    Analyze emotions from multiple text inputs
+    """
+    try:
+        logger.info(f"Analyzing batch of {len(input_data.texts)} texts")
+        results = []
+        for text in input_data.texts:
+            result = emotion_detector.detect_emotion(text)
+            results.append(result)
+        
+        # Aggregate results
+        aggregated = emotion_detector.aggregate_emotions(results)
+        return aggregated
+    except Exception as e:
+        logger.error(f"Error analyzing batch: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/api/generate-insights")
+async def generate_insights(input_data: TextInput):
+    """
+    Generate AI-powered insights from text
+    """
+    try:
+        logger.info("Generating AI insights...")
+        
+        # First detect emotions
+        emotion_result = emotion_detector.detect_emotion(input_data.text)
+        
+        # Generate insights using LLM
+        insights = await ai_insights.generate_insights(
+            text=input_data.text,
+            emotions=emotion_result['emotions'],
+            user_name=input_data.user_name
+        )
+        
+        return {
+            "emotions": emotion_result,
+            "insights": insights
+        }
+    except Exception as e:
+        logger.error(f"Error generating insights: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/api/generate-art")
+async def generate_art(input_data: TextInput):
+    """
+    Generate AI artwork based on detected emotions
+    """
+    try:
+        logger.info("Generating mood art...")
+        
+        # Detect emotions
+        emotion_result = emotion_detector.detect_emotion(input_data.text)
+        
+        # Generate art
+        art_result = await art_generator.generate_mood_art(
+            emotions=emotion_result['emotions'],
+            dominant_emotion=emotion_result['dominant_emotion']
+        )
+        
+        return {
+            "emotions": emotion_result,
+            "artwork": art_result
+        }
+    except Exception as e:
+        logger.error(f"Error generating art: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/api/visualize")
+async def create_visualizations(input_data: MultipleTexts):
+    """
+    Create visualization charts from multiple texts
+    """
+    try:
+        logger.info("Creating visualizations...")
+        
+        # Analyze all texts
+        results = []
+        for text in input_data.texts:
+            result = emotion_detector.detect_emotion(text)
+            results.append(result)
+        
+        # Generate visualizations
+        viz_data = viz_generator.create_visualizations(results)
+        
+        return viz_data
+    except Exception as e:
+        logger.error(f"Error creating visualizations: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/api/generate-report")
+async def generate_report(input_data: MultipleTexts):
+    """
+    Generate a comprehensive PDF report
+    """
+    try:
+        logger.info("Generating PDF report...")
+        
+        # Analyze all texts
+        results = []
+        for text in input_data.texts:
+            result = emotion_detector.detect_emotion(text)
+            results.append(result)
+        
+        # Aggregate emotions
+        aggregated = emotion_detector.aggregate_emotions(results)
+        
+        # Generate insights
+        combined_text = " ".join(input_data.texts)
+        insights = await ai_insights.generate_insights(
+            text=combined_text,
+            emotions=aggregated['emotions'],
+            user_name=input_data.user_name
+        )
+        
+        # Create visualizations
+        viz_data = viz_generator.create_visualizations(results)
+        
+        # Generate PDF
+        pdf_path = pdf_generator.generate_report(
+            user_name=input_data.user_name,
+            emotions=aggregated,
+            insights=insights,
+            visualizations=viz_data
+        )
+        
+        return FileResponse(
+            pdf_path,
+            media_type="application/pdf",
+            filename=f"ai_mirror_report_{input_data.user_name}.pdf"
+        )
+    except Exception as e:
+        logger.error(f"Error generating report: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/api/full-analysis")
+async def full_analysis(input_data: MultipleTexts):
+    """
+    Perform complete analysis including emotions, insights, art, and visualizations
+    """
+    try:
+        logger.info("Performing full analysis...")
+        
+        # Analyze all texts
+        results = []
+        for text in input_data.texts:
+            result = emotion_detector.detect_emotion(text)
+            results.append(result)
+        
+        # Aggregate emotions
+        aggregated = emotion_detector.aggregate_emotions(results)
+        
+        # Generate insights
+        combined_text = " ".join(input_data.texts)
+        insights = await ai_insights.generate_insights(
+            text=combined_text,
+            emotions=aggregated['emotions'],
+            user_name=input_data.user_name
+        )
+        
+        # Generate art
+        art_result = await art_generator.generate_mood_art(
+            emotions=aggregated['emotions'],
+            dominant_emotion=aggregated['dominant_emotion']
+        )
+        
+        # Create visualizations
+        viz_data = viz_generator.create_visualizations(results)
+        
+        return {
+            "emotions": aggregated,
+            "insights": insights,
+            "artwork": art_result,
+            "visualizations": viz_data,
+            "text_count": len(input_data.texts)
+        }
+    except Exception as e:
+        logger.error(f"Error in full analysis: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

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+fastapi==0.104.1
+uvicorn[standard]==0.24.0
+python-multipart==0.0.6
+transformers==4.35.2
+torch==2.2.2
+pandas==2.2.2
+numpy==1.26.2
+scikit-learn==1.3.2
+textblob==0.17.1
+nltk==3.8.1
+spacy==3.7.2
+plotly==5.18.0
+matplotlib==3.8.2
+seaborn==0.13.0
+pillow==10.1.0
+openai==1.3.7
+google-generativeai==0.3.1
+reportlab==4.0.7
+wordcloud==1.9.3
+python-dotenv==1.0.0
+pydantic==2.5.2
+httpx==0.25.2
+aiofiles==23.2.1
+
+dotenv
+gradio-client