README.md

metadata

title: Question Answering Classification
emoji: 🦀
colorFrom: blue
colorTo: indigo
sdk: docker
pinned: false

🧬 NEET Biology Q&A — AI Study Assistant

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

---

🚀 Quick Start

Prerequisites

• Python 3.10+
• Supabase account (with pgvector enabled)
• Groq API key

1. Clone & Setup

git clone https://github.com/PranilBankar/Question_AndAnswerGeneration.git
cd Question_AndAnswerGeneration

2. Create Virtual Environment

python -m venv .venv

# Windows
.venv\Scripts\Activate.ps1

# macOS/Linux
source .venv/bin/activate

3. Install Dependencies

pip install torch transformers sentence-transformers scikit-learn
pip install fastapi uvicorn
pip install supabase python-dotenv
pip install groq numpy

4. Environment Variables

Create pdf_to_embedding/.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

---

▶️ Running the Project

Start the Backend (Terminal 1)

cd Question_AndAnswerGeneration
.venv\Scripts\Activate.ps1
uvicorn Backend.main:app --reload --port 8000

Wait for: ✅ All models loaded — server ready!

Start the Frontend (Terminal 2)

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

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

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.