app.py

from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
import numpy as np
from onnxruntime import InferenceSession
from transformers import AutoTokenizer
import os

app = FastAPI()

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

# Load model
session = InferenceSession("model.onnx")
tokenizer = AutoTokenizer.from_pretrained("Xenova/multi-qa-mpnet-base-dot-v1")

@app.post("/predict")
async def predict(query: str):
    inputs = tokenizer(query, return_tensors="np")
    inputs = {k: v.astype(np.int64) for k, v in inputs.items()}
    outputs = session.run(None, inputs)
    embedding = outputs[0][0].tolist()
    
    return {"embedding": embedding}