Update app.py

app.py

@@ -111,28 +111,28 @@ async def predict(predictionFeatures: RentalFeatures):
     Prediction of car daily rental cost for a given properties of a car !
     """
     # Read data
-    # pf = [
-    #     predictionFeatures.model_key or "Peugeot",   
-    #     predictionFeatures.mileage or 0,
-    #     predictionFeatures.engine_power or 0,
-    #     predictionFeatures.fuel or "petrol",
-    #     predictionFeatures.car_type or "sedan",
-    #     predictionFeatures.private_parking_available or 1,
-    #     predictionFeatures.has_gps or 0,
-    #     predictionFeatures.has_air_conditioning or 0,
-    #     predictionFeatures.automatic_car or 0,
-    #     predictionFeatures.has_getaround_connect or 0,
-    #     predictionFeatures.has_speed_regulator or 0,
-    #     predictionFeatures.winter_tires or 0,
-    # ]
-
-    car_caracteristic = pd.DataFrame([predictionFeatures.model_dump()])
+    pf = [
+         predictionFeatures.model_key or "Peugeot",   
+         predictionFeatures.mileage or 0,
+         predictionFeatures.engine_power or 0,
+         predictionFeatures.fuel or "petrol",
+         predictionFeatures.car_type or "sedan",
+         predictionFeatures.private_parking_available or 1,
+         predictionFeatures.has_gps or 0,
+         predictionFeatures.has_air_conditioning or 0,
+         predictionFeatures.automatic_car or 0,
+         predictionFeatures.has_getaround_connect or 0,
+         predictionFeatures.has_speed_regulator or 0,
+         predictionFeatures.winter_tires or 0,
+     ]
+
+    car_caracteristic = pd.DataFrame([pf])
     logger.info('Started')
 
     logger.info(car_caracteristic)
     #car_caracteristic = pd.DataFrame({"Car": pf})
     # Log model from mlflow
-    logged_model = "models:/getaround-price-prediction-model/versions/6"  # REPLACE WITH YOUR OWN RUN ID
+    logged_model = "models:/getaround-price-prediction-model@champion"  # REPLACE WITH YOUR OWN RUN ID
     #logged_model = "runs:/d3856ef1a2ee42db989496949f47ac74/getaround-price-prediction-model"
     # Load model as a PyFuncModel.
     loaded_model = mlflow.pyfunc.load_model(logged_model)