from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from micrograd.train import model  # Replace with your ML model library
from typing import List

# Create FastAPI app
app = FastAPI()

# Load your ML model (replace this with your model loading logic)
# model = engine.load_model("path/to/your/model")

# Define a request model
class Item(BaseModel):
    data: List[float]  # Change 'list' to match the input format of your model

# Endpoint for ML inference
@app.post("/predict")
async def predict(item: Item):
    try:
        # Perform prediction (modify this part according to your model's API)
        nn = model()
        prediction = nn(item.data)
        return {"prediction": prediction.data}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

# Run the API with Uvicorn
if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8008)