| """ |
| main.py — FastAPI application entry point for the NEET Biology Q&A system |
| |
| Run: |
| uvicorn Backend.main:app --reload --port 8000 |
| |
| Docs: |
| http://localhost:8000/docs (Swagger UI) |
| http://localhost:8000/redoc (ReDoc) |
| """ |
|
|
| import time |
| from contextlib import asynccontextmanager |
|
|
| from fastapi import FastAPI |
| from fastapi.middleware.cors import CORSMiddleware |
|
|
| from Backend.schemas import HealthResponse |
| from Backend.routers.qa import router as qa_router |
|
|
|
|
| |
| |
| |
| @asynccontextmanager |
| async def lifespan(app: FastAPI): |
| """ |
| Pre-load heavy models at startup so the first request is fast. |
| """ |
| print("\n🚀 Starting NEET Biology Q&A Backend...") |
| start = time.time() |
|
|
| |
| from Classification.classify_and_extract import get_predictor |
| get_predictor() |
|
|
| |
| from rag.retriever import get_embedding_model |
| get_embedding_model() |
|
|
| elapsed = time.time() - start |
| print(f"✅ All models loaded in {elapsed:.1f}s — server ready!\n") |
|
|
| yield |
|
|
| print("\n🛑 Shutting down NEET Biology Q&A Backend.") |
|
|
|
|
| |
| |
| |
| app = FastAPI( |
| title="NEET Biology Q&A API", |
| description=( |
| "AI-powered Question Answering system for NEET Biology. " |
| "Uses BERT classification for chapter detection and a " |
| "RAG pipeline with NCERT textbook context for accurate answers." |
| ), |
| version="1.0.0", |
| lifespan=lifespan, |
| ) |
|
|
|
|
| |
| |
| |
| app.add_middleware( |
| CORSMiddleware, |
| allow_origins=[ |
| "http://localhost:3000", |
| "http://localhost:5173", |
| "http://localhost:5500", |
| "http://127.0.0.1:5500", |
| "*", |
| ], |
| allow_credentials=True, |
| allow_methods=["*"], |
| allow_headers=["*"], |
| ) |
|
|
|
|
| |
| |
| |
| app.include_router(qa_router) |
|
|
|
|
| @app.get( |
| "/health", |
| response_model=HealthResponse, |
| tags=["System"], |
| summary="Health check", |
| ) |
| async def health_check(): |
| """Check if the server and all components are running.""" |
| from Classification.classify_and_extract import _predictor |
| from rag.retriever import _embedding_model, _supabase_client |
|
|
| return HealthResponse( |
| status="ok", |
| version="1.0.0", |
| components={ |
| "bert_classifier": "loaded" if _predictor is not None else "not loaded", |
| "embedding_model": "loaded" if _embedding_model is not None else "not loaded", |
| "supabase": "connected" if _supabase_client is not None else "not connected", |
| }, |
| ) |
|
|
|
|
| @app.get("/", tags=["System"]) |
| async def root(): |
| """API root — welcome message.""" |
| return { |
| "message": "NEET Biology Q&A API", |
| "version": "1.0.0", |
| "docs": "/docs", |
| "endpoints": { |
| "POST /api/qa": "Full Q&A pipeline (classify + answer + verify)", |
| "POST /api/classify": "Chapter classification + topic extraction only", |
| "GET /health": "Health check", |
| }, |
| } |
|
|
