app.py

import pandas as pd
import joblib
import uvicorn
from fastapi import FastAPI, Body, Request
from fastapi.responses import JSONResponse, RedirectResponse
from fastapi.exceptions import RequestValidationError
from pydantic import BaseModel, Field
from typing import Literal


# Charger le modèle
model = joblib.load("get_around_v1.pkl")

# Classe Pydantic pour les entrées
class CarData(BaseModel):
    mileage: float = Field(..., ge=0, description="Kilométrage doit être >= 0")
    engine_power: float = Field(..., ge=45, le=423, description="Puissance moteur entre 45 et 423")
    car_brand: Literal[
        "Citroën", "Peugeot", "PGO", "Renault", "Audi", "BMW", "Ford", "Mercedes",
        "Opel", "Porsche", "Volkswagen", "KIA Motors", "Alfa Romeo", "Ferrari",
        "Fiat", "Lamborghini", "Maserati", "Lexus", "Honda", "Mazda", "Mini",
        "Mitsubishi", "Nissan", "SEAT", "Subaru", "Suzuki", "Toyota", "Yamaha"
    ]
    fuel: Literal["diesel", "petrol", "hybrid_petrol", "electro"]
    paint_color: Literal[
        "black", "grey", "white", "red", "silver", "blue",
        "orange", "beige", "brown", "green"
    ]
    car_type: Literal[
        "sedan", "convertible", "coupe", "estate", "hatchback", "subcompact", "suv", "van"
    ]
    private_parking_available: bool
    has_gps: bool
    has_air_conditioning: bool
    automatic_car: bool
    has_getaround_connect: bool
    has_speed_regulator: bool
    winter_tires: bool

# Initialisation FastAPI
app = FastAPI(
    title="🚙🚕 API de Prédiction de Prix GetAround 🚗🚑 ",
    docs_url="/docs",
    description="""

Bienvenue sur l'API de prédiction de prix de location de véhicules GetAround !  

Grâce à notre modèle prédictif entraîné sur les données du partenaire GetAround, vous pouvez estimer rapidement le prix journalier d'un véhicule en fournissant ses caractéristiques.

📌 **Règles pour certaines colonnes :**  

- **car_brand** : choisissez parmi les marques listées :  
  "Citroën", "Peugeot", "PGO", "Renault", "Audi", "BMW", "Ford", "Mercedes",
  "Opel", "Porsche", "Volkswagen", "KIA Motors", "Alfa Romeo", "Ferrari",
  "Fiat", "Lamborghini", "Maserati", "Lexus", "Honda", "Mazda", "Mini",
  "Mitsubishi", "Nissan", "SEAT", "Subaru", "Suzuki", "Toyota", "Yamaha".  

- **fuel** : "diesel", "petrol", "hybrid_petrol", "electro".  

- **paint_color** : "black", "grey", "white", "red", "silver", "blue",
  "orange", "beige", "brown", "green".  

- **car_type** : "convertible", "coupe", "estate", "hatchback", "sedan",
  "subcompact", "suv", "van".  

✅ Pour les autres options (private_parking_available, has_gps, etc.), utilisez **true** pour Oui et **false** pour Non.

💡 Exemple d'utilisation :  

```json
{
  "car_brand": "Renault",
  "mileage": 50000,
  "engine_power": 120,
  "fuel": "diesel",
  "paint_color": "white",
  "car_type": "estate",
  "private_parking_available": false,
  "has_gps": true,
  "has_air_conditioning": false,
  "automatic_car": false,
  "has_getaround_connect": false,
  "has_speed_regulator": false,
  "winter_tires": true
}

""",
    version="1.0"
)


@app.get("/")
def root():
    return RedirectResponse(url="/docs")

# Gestionnaire d'erreurs personnalisé
@app.exception_handler(RequestValidationError)
async def validation_exception_handler(request: Request, exc: RequestValidationError):
    errors = []
    for e in exc.errors():
        field = e.get("loc")[-1]
        expected = e.get("ctx", {}).get("expected")
        msg = e.get("msg")
        if expected:
            errors.append({
                "field": field,
                "message": f"Valeur incorrecte pour '{field}'. Valeurs possibles : {expected}"
            })
        else:
            errors.append({
                "field": field,
                "message": f"Erreur sur '{field}': {msg}"
            })
    return JSONResponse(
        status_code=422,
        content={
            "error": "Certaines valeurs ne sont pas valides",
            "details": errors
        }
    )

# Endpoint de prédiction
@app.post(
    "/predict",
    summary="Prédire le prix journalier d'une voiture",
    description="Fournir toutes les caractéristiques de la voiture pour obtenir le prix prédictif."
)
def predict(
    data: CarData = Body(
        ...,
        examples={
            "valid_example": {
                "summary": "Exemple valide",
                "value": {
                    "mileage": 120000,
                    "engine_power": 100,
                    "car_brand": "Citroën",
                    "fuel": "diesel",
                    "paint_color": "white",
                    "car_type": "sedan",
                    "private_parking_available": True,
                    "has_gps": True,
                    "has_air_conditioning": True,
                    "automatic_car": True,
                    "has_getaround_connect": True,
                    "has_speed_regulator": True,
                    "winter_tires": True
                }
            }
        }
    )
):
    df = pd.DataFrame([data.model_dump()])
    df.rename(columns={'car_brand': 'model_key'}, inplace=True)
    prediction = model.predict(df)
    return {"prediction": round(float(prediction[0]), 2)}


# Lancer localement (utile pour tests)
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)