Update app.py

app.py

@@ -22,6 +22,26 @@ def predict_func(hours_studied,   previous_scores,        Extracurricular_Activi
   y_pred = model.predict(x_new)
   #y_pred = round(y_pred[0][0],2)
   return round(y_pred[0][0],2)
+
+# Télécharger l'encoder
+encoder = joblib.load('Extracurricular.joblib')
+# Télécharger le sacler
+scaler = joblib.load('scaler.joblib')
+# Le modèle
+model = load_model('/content/DNN_model.h5')
+
+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:
+    y_pred = predict_func(row[0], row[1],row[2], row[3],row[4])
+    # ajouter la prediction sur List_predictions
+    predictions.append(y_pred)
+  df['Performance_Index'] = predictions
+  df.to_csv('predictions.csv', index = False)
+  return 'predictions.csv'
     
 demo=gr.Blocks(theme = 'NoCrypt/miku')
 # Créer les inputs
@@ -36,13 +56,16 @@ outputs = gr.Textbox(label='Performance_Index')
 interface1 = gr.Interface(fn = predict_func,
                          inputs = inputs,
                          outputs = outputs,
-                         title="Prédire la performence d'un élève"
-                          )
-
-
+                         title="Prédire la performance d'un individu",
+                         )
+# 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="Prédiction multiple de la performance d'un individu"
+                         )
 # faire un tabbing des interfaces
 with demo:
-  gr.TabbedInterface([interface1], ['Simple Prediction'])
-
+  gr.TabbedInterface([interface1, interface2], ['Simple Prediction', 'Prédiction multiple'])
 # lancer l'interface
 demo.launch()