Update app.py

app.py

@@ -1,161 +1,161 @@
-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=1, le=423, description="Puissance moteur entre 1 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[
-        "convertible", "coupe", "estate", "hatchback", "sedan", "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": 15000,
-                    "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__":
+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=1, le=423, description="Puissance moteur entre 1 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[
+        "convertible", "coupe", "estate", "hatchback", "sedan", "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": 80000,
+                    "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)