new update

.gitignore

@@ -0,0 +1 @@
+.gradio/

app.py

@@ -4,21 +4,53 @@ import numpy as np
 
 # Charger le modèle en spécifiant le chemin absolu
 model_path = "./linear_regression_model.joblib"
-lr = joblib.load(model_path)
+try:
+    lr = joblib.load(model_path)
+except FileNotFoundError:
+    raise FileNotFoundError(f"Le fichier modèle '{model_path}' est introuvable. Vérifiez le chemin.")
 
 # Fonction de prédiction
-def predict_price(*features):
-    features = np.array(features).reshape(1, -1)
-    prediction = lr.predict(features)[0]
-    return round(prediction, 2)
+def predict_price(kms_driven, present_price, fuel_type, seller_type, transmission, age):
+    # Encodage des variables catégoriques
+    fuel_type_mapping = {"Petrol": 0, "Diesel": 1, "CNG": 2}
+    seller_type_mapping = {"Dealer": 0, "Individual": 1}
+    transmission_mapping = {"Manual": 0, "Automatic": 1}
 
-# Interface Gradio
-input_labels = ["Kms_Driven", "Present_Price",	"Fuel_Type",	"Seller_Type",	"Transmission",	"Age",	"Selling_Price"]  
-inputs = [gr.Number(label=label) for label in input_labels]
+    try:
+        # Conversion des types et gestion des encodages
+        fuel_type = fuel_type_mapping.get(fuel_type, -1)
+        seller_type = seller_type_mapping.get(seller_type, -1)
+        transmission = transmission_mapping.get(transmission, -1)
+
+        if fuel_type == -1 or seller_type == -1 or transmission == -1:
+            return "Erreur : Valeurs non reconnues pour les types de carburant, vendeur ou transmission."
+
+        # Création de l'entrée pour le modèle
+        features = np.array([kms_driven, present_price, fuel_type, seller_type, transmission, age]).reshape(1, -1)
 
+        # Prédiction
+        prediction = lr.predict(features)[0]
+        return round(prediction, 2)
+    except Exception as e:
+        return f"Erreur lors de la prédiction : {str(e)}"
+
+# Interface Gradio
+input_labels = [
+    gr.Number(label="Kms_Driven"),
+    gr.Number(label="Present_Price"),
+    gr.Dropdown(choices=["Petrol", "Diesel", "CNG"], label="Fuel_Type"),
+    gr.Dropdown(choices=["Dealer", "Individual"], label="Seller_Type"),
+    gr.Dropdown(choices=["Manual", "Automatic"], label="Transmission"),
+    gr.Number(label="Age"),
+]
 output = gr.Number(label="Predicted Price")
 
-interface = gr.Interface(fn=predict_price, inputs=inputs, outputs=output, title="Car Price Prediction")
+interface = gr.Interface(
+    fn=predict_price,
+    inputs=input_labels,
+    outputs=output,
+    title="Car Price Prediction"
+)
 
 # Lancer l'application
 if __name__ == "__main__":