app.py

from fastapi import FastAPI, Request, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from onnxruntime import InferenceSession
import numpy as np
import os
import uvicorn

# Initialize FastAPI with docs disabled for Spaces
app = FastAPI(docs_url=None, redoc_url=None)

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

# Load ONNX model
try:
    session = InferenceSession("model.onnx")
    print("Model loaded successfully")
except Exception as e:
    print(f"Model loading failed: {str(e)}")
    raise

@app.get("/")
async def health_check():
    return {"status": "ready", "model": "onnx"}

@app.post("/api/predict")
async def predict(request: Request):
    try:
        # Get JSON input
        data = await request.json()
        
        # Convert to numpy arrays with correct shape
        input_ids = np.array(data["input_ids"], dtype=np.int64).reshape(1, -1)
        attention_mask = np.array(data["attention_mask"], dtype=np.int64).reshape(1, -1)
        
        # Run inference
        outputs = session.run(
            None,
            {
                "input_ids": input_ids,
                "attention_mask": attention_mask
            }
        )
        
        return {"embedding": outputs[0].tolist()}
    
    except Exception as e:
        raise HTTPException(status_code=400, detail=str(e))

# Required for Hugging Face Spaces
if __name__ == "__main__":
    uvicorn.run(
        "app:app",
        host="0.0.0.0",
        port=7860,
        reload=False
    )