api.py

# from pathlib import Path
# import os
# import shutil
# from fastapi import FastAPI, UploadFile, File, HTTPException
# from fastapi.middleware.cors import CORSMiddleware

# app = FastAPI(title="Food Nutrition ML API")

# app.add_middleware(
#     CORSMiddleware,
#     allow_origins=["*"],
#     allow_credentials=True,
#     allow_methods=["*"],
#     allow_headers=["*"],
# )

# # Use system ephemeral tmp directory on Cloud Run
# TEMP_DIR = Path("/tmp/temp_uploads")
# TEMP_DIR.mkdir(parents=True, exist_ok=True)


# @app.get("/")
# def health_check():
#     """Health check endpoint for Cloud Run/monitoring."""
#     return {"status": "healthy", "service": "Food Nutrition ML API"}


# @app.post("/analyze-food")
# async def analyze_food(file: UploadFile = File(...)):
#     if not file.content_type or not file.content_type.startswith("image/"):
#         raise HTTPException(status_code=400, detail="File must be an image.")

#     temp_path = TEMP_DIR / file.filename
#     try:
#         with open(temp_path, "wb") as buffer:
#             shutil.copyfileobj(file.file, buffer)

#         # Defer heavy ML pipeline import until request execution
#         # This keeps container startup under 1 second for Cloud Run health checks
#         from inference.pipeline import process_image

#         result = process_image(temp_path)
#         return result

#     except Exception as e:
#         raise HTTPException(status_code=500, detail=str(e))

#     finally:
#         if temp_path.exists():
#             os.remove(temp_path)

#new 
import io
from pathlib import Path
from fastapi import FastAPI, UploadFile, File, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from PIL import Image

app = FastAPI(title="Food Nutrition ML API")

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


@app.get("/")
def health_check():
    """Health check endpoint for Cloud Run/monitoring."""
    return {"status": "healthy", "service": "Food Nutrition ML API"}


@app.post("/analyze-food")
async def analyze_food(file: UploadFile = File(...)):
    if not file.content_type or not file.content_type.startswith("image/"):
        raise HTTPException(status_code=400, detail="File must be an image.")

    try:
        # Read file bytes directly in memory
        contents = await file.read()
        pil_img = Image.open(io.BytesIO(contents)).convert("RGB")

        # Lazy import of pipeline to preserve fast container cold-starts
        from inference.pipeline import process_image_in_memory

        result = process_image_in_memory(pil_img, filename=file.filename)
        return result

    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))