Create app.py

app.py

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+import joblib
+import numpy as np
+from keras.models import load_model
+import gradio as gr
+import pandas as pd
+# Télécharger l'encoder
+encoder = joblib.load('Extracurricular.joblib')
+# Télécharger le sacler
+scaler = joblib.load('scaler.joblib')
+# Le modèle
+model = load_model('/content/DNN_model.keras')
+
+def predict_func (hours_studied,   previous_scores,        extra_activities,       sleep_hours,    sample_question_pp):
+  # encoder la valeur de Extracurriclar Activities using map
+  extra_activities_series = pd.Series([extra_activities])
+  extra_activities_encoded = extra_activities_series.map(encoder).iloc[0]
+
+  # vecteur des valeurs numeriques
+  x_new=np.array([hours_studied,        previous_scores,        extra_activities_encoded,       sleep_hours,    sample_question_pp]).reshape(1, -1)
+  # Apply scaling
+  x_new=scaler.transform(x_new)
+  # Prediction
+  y_pred = model.predict(x_new)
+  return f"L'élève a une performance de {y_pred[0][0]:.2f}%"
+
+demo=gr.Blocks(theme = 'NoCrypt/miku')
+# Créer les inputs
+inputs = [gr.Number(label='Hours_Studied'),
+          gr.Number(label='Previous_Scores'),
+          gr.Radio(choices=['Yes', 'No'], label='Extracurricular_Activities'),
+          gr.Number(label='Sleep_Hours'),
+          gr.Number(label='Sample_Question_Papers_Practiced')]
+# Créer les outputs
+outputs = gr.Textbox(label='Performance_Index')
+# Créer l'interface 1
+interface1 = gr.Interface(fn = predict_func,
+                         inputs = inputs,
+                         outputs = outputs,
+                         title="Prédire la performence d'un élève",
+                          theme = gr.themes.Ocean())
+
+
+# faire un tabbing des interfaces
+with demo:
+  gr.TabbedInterface([interface1], ['Simple Prediction'])
+
+# lancer l'interface
+demo.launch()