31 lines
934 B
Python
31 lines
934 B
Python
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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from micrograd.train import model # Replace with your ML model library
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from typing import List
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# Create FastAPI app
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app = FastAPI()
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# Load your ML model (replace this with your model loading logic)
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# model = engine.load_model("path/to/your/model")
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# Define a request model
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class Item(BaseModel):
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data: List[float] # Change 'list' to match the input format of your model
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# Endpoint for ML inference
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@app.post("/predict")
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async def predict(item: Item):
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try:
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# Perform prediction (modify this part according to your model's API)
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nn = model()
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prediction = nn(item.data)
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return {"prediction": prediction.data}
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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# Run the API with Uvicorn
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8008)
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