| from fastapi import FastAPI, HTTPException |
| from pydantic import BaseModel |
| from typing import Literal |
| import pandas as pd |
| import boto3 |
| import joblib |
| import os |
| import io |
|
|
| |
| app = FastAPI( |
| title="🚗 Getaround Price Prediction API", |
| description=""" |
| ### 🎯 Description |
| Cette API prédit le **prix de location journalier** d’un véhicule. |
| |
| 👉 Pour certaines colonnes (`model_key`, `fuel`, `car_type`, `paint_color`), **choisir une valeur parmi les critères listés**. |
| ModelKey = |
| "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" |
| |
| Mileage = Renseigner des valeurs entre 0 et 400000 |
| |
| EnginePower = Renseigner des valeurs entre 10 et 300 |
| |
| Fuel = "diesel", "petrol", "hybrid_petrol", "electro" |
| |
| PaintColor = "black", "grey", "white", "red", "silver", "blue", "orange","beige", "brown", "green" |
| |
| CarType = "convertible", "coupe", "estate", "hatchback", "sedan", "subcompact", "suv", "van" |
| |
| 👉 Pour les autres options (`private_parking_available`, `has_gps`, etc.), utiliser **true pour Oui** et **false pour Non**. |
| |
| Exemple : |
| ```json |
| { |
| "model_key": "Audi", |
| "mileage": 100000, |
| "engine_power": 120, |
| "fuel": "diesel", |
| "paint_color": "black", |
| "car_type": "estate", |
| "private_parking_available": true, |
| "has_gps": true, |
| "has_air_conditioning": false, |
| "automatic_car": false, |
| "has_getaround_connect": true, |
| "has_speed_regulator": false, |
| "winter_tires": true |
| } |
| """, |
| version="1.0" |
| ) |
|
|
| |
| class InputData(BaseModel): |
| model_key: str |
| mileage: int |
| engine_power: int |
| fuel: str |
| paint_color: str |
| car_type: str |
| 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 |
|
|
| |
| S3_BUCKET = os.getenv("S3_BUCKET") |
| MODEL_KEY = os.getenv("MODEL_KEY", "mlflow/models/xgboost_model.joblib") |
| s3 = boto3.client("s3") |
|
|
| |
| model = None |
|
|
| @app.on_event("startup") |
| def load_model(): |
| global model |
| try: |
| print(f"Téléchargement du modèle depuis s3://{S3_BUCKET}/{MODEL_KEY}") |
| response = s3.get_object(Bucket=S3_BUCKET, Key=MODEL_KEY) |
| model_bytes = io.BytesIO(response["Body"].read()) |
| model = joblib.load(model_bytes) |
| print("✅ Modèle chargé avec succès") |
| except Exception as e: |
| print(f"❌ Erreur chargement modèle : {e}") |
| raise RuntimeError(f"Impossible de charger le modèle : {e}") |
|
|
| |
| @app.get("/") |
| def home(): |
| return {"message": "Bienvenue sur l'API Getaround 🚗 - Utilisez /predict pour faire une prédiction"} |
|
|
| @app.post("/predict") |
| def predict(data: InputData): |
| try: |
| |
| df = pd.DataFrame([data.dict()]) |
| print("📥 Données reçues :", df.to_dict()) |
|
|
| |
| prediction = model.predict(df) |
| price = float(prediction[0]) |
|
|
| return {"predicted_price_per_day": [round(float(prediction), 2)]} |
|
|
| except Exception as e: |
| print(f"❌ Erreur prédiction : {e}") |
| raise HTTPException(status_code=500, detail=str(e)) |
| |
| |
|
|
