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from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from onnxruntime import InferenceSession
import numpy as np
import os
app = FastAPI(title="ONNX Model API")
# CORS configuration
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_methods=["*"],
allow_headers=["*"],
)
# Load ONNX model
model_path = os.path.join(os.getcwd(), "model.onnx")
session = InferenceSession(model_path)
@app.get("/")
def health_check():
return {"status": "healthy", "message": "ONNX model is ready"}
@app.post("/predict")
async def predict(inputs: dict):
"""Expects {'input_ids': [], 'attention_mask': []}"""
try:
input_ids = np.array(inputs["input_ids"], dtype=np.int64).reshape(1, -1)
attention_mask = np.array(inputs["attention_mask"], dtype=np.int64).reshape(1, -1)
outputs = session.run(
None,
{
"input_ids": input_ids,
"attention_mask": attention_mask
}
)
return {"embedding": outputs[0].tolist()}
except Exception as e:
return {"error": str(e)}
# Required for Hugging Face Spaces
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860) |