ahyahya1616/fast_api_classification_air

1# main.py2from fastapi import FastAPI3from pydantic import BaseModel4import joblib5import numpy as np6 7# Charger le modèle8model = joblib.load("model_air_quality.joblib")9 10app = FastAPI(title="API Prédiction Qualité de l'air")11 12# Schéma de la requête13class SensorData(BaseModel):14    co2: float15    pm25: float16    temp: float17    hum: float18 19 20@app.get("/")21def read_root():22    return {"status": "API is running", "endpoint": "/predict"}23 24@app.post("/predict")25def predict(data: SensorData):26    features = np.array([[data.co2, data.pm25, data.temp, data.hum]])27    prediction = model.predict(features)[0]28    # On peut renvoyer la probabilité aussi si on veut29    proba = model.predict_proba(features)[0].tolist()30    return {31        "prediction": int(prediction),32        "probability": proba33    }