# backend/app/main.py (for Hugging Face Spaces) from fastapi import FastAPI, File, UploadFile, HTTPException from fastapi.middleware.cors import CORSMiddleware from fastapi.responses import JSONResponse, HTMLResponse, FileResponse from fastapi.staticfiles import StaticFiles import os import shutil from pathlib import Path import time from .inference import InferenceEngine # Initialize FastAPI app app = FastAPI( title="MRI Brain Tumor Detection API", description="Deep Learning API for brain tumor classification from MRI scans", version="1.0.0" ) # CORS Configuration app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) # Configure paths BASE_DIR = Path(__file__).resolve().parent.parent UPLOAD_DIR = BASE_DIR / "uploads" MODEL_PATH = BASE_DIR / "models" / "model_Full.pth" STATIC_DIR = BASE_DIR / "static" # Ensure directories exist UPLOAD_DIR.mkdir(exist_ok=True) # Initialize inference engine inference_engine = None @app.on_event("startup") async def startup_event(): """Initialize model on startup""" global inference_engine print(f"📁 Static directory: {STATIC_DIR}") print(f"📁 Static exists: {STATIC_DIR.exists()}") if STATIC_DIR.exists(): print(f"📁 Static contents: {list(STATIC_DIR.iterdir())}") if not MODEL_PATH.exists(): print(f"⚠️ Model file not found at {MODEL_PATH}") print("Please place your model_Full.pth in the backend/models/ directory") else: try: inference_engine = InferenceEngine(str(MODEL_PATH)) print("✅ Inference engine initialized successfully") except Exception as e: print(f"❌ Failed to initialize inference engine: {e}") # Mount static files for assets (CSS, JS, etc.) if STATIC_DIR.exists(): app.mount("/assets", StaticFiles(directory=STATIC_DIR / "assets"), name="assets") @app.get("/", response_class=HTMLResponse) async def root(): """Serve the frontend HTML""" html_file = STATIC_DIR / "index.html" if html_file.exists(): return FileResponse(html_file) # Fallback API info if no frontend return HTMLResponse(content=""" MRI Brain Tumor Detection API

🧠 MRI Brain Tumor Detection API

Status: ✅ Online
Model: """ + ("✅ Loaded" if inference_engine else "❌ Not Loaded") + """

📚 API Documentation

📖 Interactive API Docs 📋 ReDoc Documentation

🔌 Endpoints

POST /api/predict - Upload MRI image for prediction
GET /health - Health check endpoint

🚀 Usage

Send a POST request to /api/predict with an MRI image file:

curl -X POST "https://arghadip2002-mri-app.hf.space/api/predict" \\
  -F "mriImage=@your_mri_image.jpg"
""") @app.post("/api/predict") async def predict(mriImage: UploadFile = File(...)): """ Predict brain tumor type from MRI image Args: mriImage: Uploaded MRI scan image file Returns: JSON with prediction results """ # Check if model is loaded if inference_engine is None: raise HTTPException( status_code=503, detail="Model not loaded. Please check server logs." ) # Validate file type allowed_extensions = {'.jpg', '.jpeg', '.png'} file_ext = Path(mriImage.filename).suffix.lower() if file_ext not in allowed_extensions: raise HTTPException( status_code=400, detail=f"Invalid file type. Allowed: {', '.join(allowed_extensions)}" ) # Save uploaded file temporarily timestamp = int(time.time() * 1000) temp_filename = f"{timestamp}_{mriImage.filename}" temp_filepath = UPLOAD_DIR / temp_filename try: # Save file with temp_filepath.open("wb") as buffer: shutil.copyfileobj(mriImage.file, buffer) # Run inference result = inference_engine.predict(str(temp_filepath)) # Clean up temporary file if temp_filepath.exists(): temp_filepath.unlink() if not result.get("success"): raise HTTPException( status_code=500, detail=f"Inference failed: {result.get('error', 'Unknown error')}" ) return JSONResponse(content={ "predicted_class": result["predicted_class"], "confidence": result["confidence"], "all_probabilities": result["all_probabilities"] }) except HTTPException: # Re-raise HTTP exceptions if temp_filepath.exists(): temp_filepath.unlink() raise except Exception as e: # Clean up on error if temp_filepath.exists(): temp_filepath.unlink() raise HTTPException( status_code=500, detail=f"Server error: {str(e)}" ) @app.get("/health") async def health_check(): """Detailed health check""" return { "status": "healthy", "model_loaded": inference_engine is not None, "model_path": str(MODEL_PATH), "model_exists": MODEL_PATH.exists(), "static_dir_exists": STATIC_DIR.exists() } if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=7860)