Create app.py

app.py

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+import numpy as np 
+import pandas as pd
+# Fonction de prédiction
+import gradio as gr
+import joblib
+# importer les encodeurs
+encoder0 = joblib.load('Fuel_Type.joblib')
+encoder1 = joblib.load('Seller_Type.joblib')
+encoder2 = joblib.load('Transmission.joblib')
+# importer le modèle
+xgb = joblib.load('xgb.joblib')
+# importer le normaliseur
+scaler = joblib.load('scaler.joblib')
+def Pred_func(Kms_Driven,Present_Price, Fuel_Type, Seller_Type, Transmission, Age):
+
+  # Encoder les valeurs des Fuel_Type, Seller_Type et Transmission
+  Fuel_Type = encoder0.transform([Fuel_Type])[0]
+  Seller_Type = encoder1.transform([Seller_Type])[0]
+  Transmission = encoder2.transform([Transmission])[0]
+  # vecteur des valeurs numériques
+  x_new = np.array([Kms_Driven,Present_Price, Fuel_Type, Seller_Type, Transmission, Age])
+  x_new = x_new.reshape(1,-1) # convert en un 2D array
+  # Normaliser les données
+  x_new = scaler.transform(x_new)
+  # Prédire
+  y_pred = xgb.predict(x_new)
+  # Arrondir
+  y_pred = round(y_pred[0],2)
+  return str(y_pred) + 'k$'
+
+# Fonction de prédiction multiple
+def Pred_func_csv(file):
+  # Lire le fichier csv
+  df = pd.read_csv(file)
+  predictions = []
+  # Boucle sur les lignes du dataframe
+  for row in df.iloc[:, :].values:
+    # nouvelle ligne avec les valeurs des Fuel_Type, Seller_Type et Transmission encodées
+    new_row = np.array([row[0], row[1], encoder0.transform([row[2]])[0], encoder1.transform([row[3]])[0], encoder2.transform([row[4]])[0], row[5]])
+    new_row = new_row.reshape(1,-1) # convertir en un 2D array
+    # Normaliser les données
+    new_row = scaler.transform(new_row)
+    # Prédire
+    y_pred = xgb.predict(new_row)
+    # Arrondir
+    y_pred = round(y_pred[0],2)
+    # ajouter la prediction sur List_predictions
+    predictions.append(y_pred)
+
+  df['Selling_Price'] = predictions
+  df.to_csv('predictions.csv', index = False)
+  return 'predictions.csv'
+
+# définir les blocks
+demo = gr.Blocks(theme = gr.themes.Monochrome())
+
+# Créer les inputs
+inputs = [gr.Number(label='Kms Driven'),
+          gr.Number(label='Present Price'),
+          gr.Radio(choices=['Petrol', 'Diesel', 'CNG'], label='Fuel Type'),
+          gr.Radio(choices=['Dealer', 'Individual'], label='Seller Type'),
+          gr.Radio(choices=['Manual', 'Automatic'], label='Transmission'),
+          gr.Number(label='Age')]
+# Créer les outputs
+outputs = gr.Textbox(label='Selling Price')
+# Créer l'interface 1
+interface1 = gr.Interface(fn = Pred_func,
+                         inputs = inputs,
+                         outputs = outputs,
+                         title="Predict the selling price of a car with a single input",
+                         description = """This machine learning model allows us to predict the selling price of a car
+                         from the kms driven, present price, fuel type, seller type, transmission and age of the car.
+                         """)
+# Créer l'interface 2
+interface2 = gr.Interface(fn = Pred_func_csv,
+                         inputs = gr.File(label='Upload a csv file'),
+                          outputs = gr.File(label='Download a csv file'),
+                         title="Predict the selling price of a car with a multiple inputs",
+                         description = """This machine learning model allows us to predict the selling price of a car
+                         from the kms driven, present price, fuel type, seller type, transmission and age of the car.
+                         """)
+
+# faire un tabbing des interfaces
+with demo:
+  gr.TabbedInterface([interface1, interface2], ['Simple Prediction', 'Prédiction multiple'])
+
+# lancer l'interface
+demo.launch()
+
+
+
+
+