| --- |
| title: Question Answering Classification |
| emoji: 🦀 |
| colorFrom: blue |
| colorTo: indigo |
| sdk: docker |
| pinned: false |
| --- |
| # 🧬 NEET Biology Q&A — AI Study Assistant |
|
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| An AI-powered Question Answering system for NEET Biology built with **BERT classification**, **RAG pipeline**, and **NCERT textbook context**. |
|
|
| --- |
|
|
| ## 🏗️ Architecture |
|
|
| ``` |
| User Question |
| │ |
| ▼ |
| ┌────────────────────────┐ |
| │ BERT Classifier │ → Predicts NCERT chapter (31 classes, 91.7% F1) |
| │ (bert-base-uncased) │ → Extracts 1-3 relevant topics/subtopics |
| └────────┬───────────────┘ |
| │ |
| ▼ |
| ┌────────────────────────┐ |
| │ Supabase pgvector │ → Retrieves top-k text chunks filtered by chapter |
| │ (BGE-small-en-v1.5) │ → Cosine similarity search on NCERT embeddings |
| └────────┬───────────────┘ |
| │ |
| ▼ |
| ┌────────────────────────┐ |
| │ Groq LLM (Llama 3) │ → Generates answer from retrieved context |
| │ RAG Pipeline │ → Justifies with step-by-step reasoning |
| │ │ → Verifies answer accuracy |
| └────────┬───────────────┘ |
| │ |
| ▼ |
| JSON Response → Frontend |
| ``` |
|
|
| --- |
|
|
| ## 📁 Project Structure |
|
|
| ``` |
| Question_AndAnswerGeneration/ |
| │ |
| ├── Backend/ # FastAPI REST API |
| │ ├── main.py # App entrypoint, CORS, model preloading |
| │ ├── schemas.py # Pydantic request/response models |
| │ └── routers/ |
| │ └── qa.py # POST /api/qa, POST /api/classify |
| │ |
| ├── Frontend/ # Vanilla HTML/CSS/JS UI |
| │ ├── index.html |
| │ ├── styles.css |
| │ └── app.js |
| │ |
| ├── Classification/ # BERT Chapter Classifier |
| │ ├── config.json # Training hyperparameters |
| │ ├── dataset.py # PyTorch Dataset class |
| │ ├── train.py # Fine-tuning script |
| │ ├── predict.py # Inference wrapper |
| │ ├── classify_and_extract.py # BERT + topic extraction orchestrator |
| │ ├── checkpoints/ |
| │ │ └── best_model/ # Trained BERT weights |
| │ ├── data/ |
| │ │ ├── generated_questions.json # 5,673 labeled questions |
| │ │ └── label_map.json # chapter_id → chapter_name |
| │ └── Evaluation_metrics/ |
| │ ├── eval_metrics.json |
| │ └── training_log.json |
| │ |
| ├── rag/ # RAG Pipeline |
| │ ├── retriever.py # Supabase vector search |
| │ ├── prompt_builder.py # LLM prompt templates |
| │ ├── generator.py # Groq API calls |
| │ ├── confidence.py # Confidence scoring |
| │ └── pipeline.py # Full orchestrator |
| │ |
| └── pdf_to_embedding/ # Data Ingestion |
| ├── clean_pdf_to_json.py # PDF → cleaned JSON chunks |
| ├── embed_and_upload.py # JSON → embeddings → Supabase |
| └── supabase_schema.sql # Database schema + RPC function |
| ``` |
|
|
| --- |
|
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| ## 🚀 Quick Start |
|
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| ### Prerequisites |
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| - Python 3.10+ |
| - Supabase account (with pgvector enabled) |
| - Groq API key |
|
|
| ### 1. Clone & Setup |
|
|
| ```bash |
| git clone https://github.com/PranilBankar/Question_AndAnswerGeneration.git |
| cd Question_AndAnswerGeneration |
| ``` |
|
|
| ### 2. Create Virtual Environment |
|
|
| ```bash |
| python -m venv .venv |
| |
| # Windows |
| .venv\Scripts\Activate.ps1 |
| |
| # macOS/Linux |
| source .venv/bin/activate |
| ``` |
|
|
| ### 3. Install Dependencies |
|
|
| ```bash |
| pip install torch transformers sentence-transformers scikit-learn |
| pip install fastapi uvicorn |
| pip install supabase python-dotenv |
| pip install groq numpy |
| ``` |
|
|
| ### 4. Environment Variables |
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| Create `pdf_to_embedding/.env`: |
|
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| ```env |
| SUPABASE_URL=https://your-project.supabase.co |
| SUPABASE_KEY=your-anon-key |
| GROQ_API_KEY=your-groq-api-key |
| EMBEDDING_MODEL=BAAI/bge-small-en-v1.5 |
| ``` |
|
|
| --- |
|
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| ## ▶️ Running the Project |
|
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| ### Start the Backend (Terminal 1) |
|
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| ```bash |
| cd Question_AndAnswerGeneration |
| .venv\Scripts\Activate.ps1 |
| uvicorn Backend.main:app --reload --port 8000 |
| ``` |
|
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| Wait for: `✅ All models loaded — server ready!` |
|
|
| ### Start the Frontend (Terminal 2) |
|
|
| ```bash |
| cd Question_AndAnswerGeneration |
| python -m http.server 5500 --directory Frontend |
| ``` |
|
|
| ### Open in Browser |
|
|
| ``` |
| Frontend: http://localhost:5500 |
| Swagger: http://localhost:8000/docs |
| Health: http://localhost:8000/health |
| ``` |
|
|
| --- |
|
|
| ## 🔌 API Endpoints |
|
|
| | Method | Endpoint | Description | |
| |--------|----------|-------------| |
| | `POST` | `/api/qa` | Full pipeline: classify → retrieve → answer → verify | |
| | `POST` | `/api/classify` | BERT classification + topic extraction only | |
| | `GET` | `/health` | Health check (server + model status) | |
| | `GET` | `/docs` | Swagger UI (auto-generated) | |
|
|
| ### Example: POST /api/qa |
|
|
| ```bash |
| curl -X POST http://localhost:8000/api/qa \ |
| -H "Content-Type: application/json" \ |
| -d '{"question": "What is the role of cofactors in enzyme activity?"}' |
| ``` |
|
|
| ### Example: POST /api/classify |
|
|
| ```bash |
| curl -X POST http://localhost:8000/api/classify \ |
| -H "Content-Type: application/json" \ |
| -d '{"question": "What is the role of cofactors in enzyme activity?"}' |
| ``` |
|
|
| --- |
|
|
| ## 🧠 Tech Stack |
|
|
| | Component | Technology | |
| |-----------|------------| |
| | Classifier | BERT (bert-base-uncased), PyTorch, HuggingFace Transformers | |
| | Embeddings | BGE-small-en-v1.5 (384-dim), Sentence Transformers | |
| | Vector DB | Supabase PostgreSQL + pgvector (HNSW index) | |
| | LLM | Groq API (Llama 3) | |
| | Backend | FastAPI + Uvicorn | |
| | Frontend | Vanilla HTML/CSS/JS | |
| | Data Source | NCERT Biology Class 11 & 12 (31 chapters) | |
|
|
| --- |
|
|
| ## 📊 Model Performance |
|
|
| | Metric | Score | |
| |--------|-------| |
| | Accuracy | 91.19% | |
| | F1 (macro) | 91.68% | |
| | Classes | 31 chapters | |
| | Training Data | 5,673 questions | |
| | Training Time | 8 min 30 sec (CPU) | |
|
|
| --- |
|
|
| ## 📝 License |
|
|
| This project is for educational purposes. |
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|
