main.py

from fastapi import FastAPI, UploadFile, File, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from services import predictor
from contextlib import asynccontextmanager


@asynccontextmanager
async def lifespan(app: FastAPI):

    # Load models on startup

    print("\n--- SYSTEM BOOT ---")
    print("Pre-loading deep learning models from the Hub into RAM...\n")

    predictor._get_wave_model()
    predictor._get_spiral_model()    


    print("Models successfully cached! Server is now ready to accept network traffic.\n")

    yield
    # Everything before 'yield' happens BEFORE the server accepts traffic.

    print("\n--- SYSTEM SHUTDOWN ---")
    print("Releasing memory and shutting down...")




app = FastAPI(
    title="Motor Impairment Score API",
    description="API for predicting Parkinson's motor impairment from drawings.",
    lifespan=lifespan
)

# Configure CORS
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Allow all origins
    allow_credentials=True,
    allow_methods=["*"],  # Allow all HTTP methods (GET, POST, etc.)
    allow_headers=["*"],  # Allow all headers
)

@app.get("/")
async def root():
    return {
        "status": "ok",
        "message": "Welcome to the Motor Impairment Score API"
    }



@app.post("/predict/wave")
async def predict_wave_endpoint(file: UploadFile = File(...)):
    
    if not file.content_type.startswith("image/"):
        raise HTTPException(status_code=400, detail="Invalid file type. Please upload an image.")
    
    try:
        image_bytes = await file.read()
        result = predictor.predict_wave(image_bytes)
        return result
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))



@app.post("/predict/spiral")
async def predict_spiral_endpoint(file: UploadFile = File(...)):
    
    if not file.content_type.startswith("image/"):
        raise HTTPException(status_code=400, detail="Invalid file type. Please upload an image.")
    
    try:
        image_bytes = await file.read()
        result = predictor.predict_spiral(image_bytes)
        return result
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))