second

app.py

@@ -4,6 +4,7 @@ import joblib
 import boto3
 import os
 import io
+import pandas as pd
 import uvicorn
 import numpy as np
 from dotenv import load_dotenv
@@ -38,12 +39,30 @@ model = load_model_from_s3(S3_BUCKET, MODEL_KEY)
 class InputData(BaseModel):
     input: list
 
+
 @app.post("/predict")
 def predict(data: InputData):
-    X = np.array(data.input)
+    # Colonnes attendues par le modèle
+    columns = [
+        "mileage", "engine_power", "fuel", "paint_color", "car_type",
+        "private_parking_available", "has_gps", "has_air_conditioning",
+        "automatic_car", "has_getaround_connect", "has_speed_regulator", "winter_tires"
+    ]
+    
+    # Transformer l'input en DataFrame
+    X = pd.DataFrame(data.input, columns=columns)
+    
+    # Prédictions
     preds = model.predict(X)
     return {"prediction": preds.tolist()}
 
+
+# @app.post("/predict")
+# def predict(data: InputData):
+#     X = np.array(data.input)
+#     preds = model.predict(X)
+#     return {"prediction": preds.tolist()}
+
 @app.get("/")
 def home():
     return {"message": "Bienvenue sur l'API GetAround Pricing! Utilisez /predict pour faire une prédiction."}