from src import load_model @app.post("/classify") async def classify(request: dict): text = request["text"] model = load_model("classifier_v1") prediction = model.predict([text]) return {"label": prediction}

The provided code has several issues:

1. Loading the model inside the handler — load_model("classifier_v1") is called on every request, which is very inefficient and slows down the operation.

2. Asynchronous and blocking operations — if model.predict blocks, it can slow down the asynchronous server.

3. Lack of input validation — it is advisable to check for the presence and type of the "text" field.

The correct approach is to load the model once at application startup and reuse it:

from src import load_model
from fastapi import FastAPI, HTTPException

app = FastAPI()

model = load_model("classifier_v1")  # load at startup

@app.post("/classify")
async def classify(request: dict):
    text = request.get("text")
    if not text or not isinstance(text, str):
        raise HTTPException(status_code=400, detail="Invalid or missing 'text' field")
    prediction = model.predict([text])
    return {"label": prediction[0]}

If model.predict is a blocking operation, it can be moved to a thread pool using run_in_executor to maintain asynchronicity.