Create app.py

app.py

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+# Fonction de prédiction
+import gradio as gr
+import joblib
+import pandas as pd
+import numpy as np
+# importer les encodeurs
+encoder = joblib.load('encoder_Extracurricular.joblib')
+#for i in range(len(cat_data.columns)):
+ # encoders.append(joblib.load(f'{cat_data.columns[i]}_encoder.joblib'))
+
+# importer le modèle
+from keras.models import load_model
+model = load_model('DNN_model.h5')
+# importer le scaler
+scaler = joblib.load('scaler.joblib')
+# Fonction de prédiction simple
+def prediction_func(Hours_Studied,Previous_Scores,Extracurricular_Activities,Sleep_Hours,Sample_Question_Papers_Practiced):
+  #encoder
+  Extracurricular_Activities = encoder.transform([Extracurricular_Activities])[0]
+  x_new = np.array([Hours_Studied,Previous_Scores,Extracurricular_Activities,Sleep_Hours,Sample_Question_Papers_Practiced]).reshape(1, -1)
+  # normaliser les valeurs
+  x_new = scaler.transform(x_new)
+  # prédire la valeur
+  y_pred = np.round(model.predict(x_new))[0][0]
+  # retourner
+  return y_pred
+
+    # load les valeurs uniques
+#uniques = []
+#for i in range(len(cat_data.columns)):
+ # uniques.append(joblib.load(f'{cat_data.columns[i]}_unique.joblib'))
+# créer les inputs Hours_Studied,Previous_Scores,Extracurricular_Activities,Sleep_Hours,Sample_Question_Papers_Practiced,Performance_Index
+inputs = [
+          gr.Number(label="hours Studies"),
+          gr.Number(label="Previous score"),
+          gr.Dropdown(choices=['No', 'Yes'], label="Extracurricular_Activities"),
+          gr.Number( label="Sleep_Hours"),
+          gr.Number(label="Sample_Question_Papers_Practiced")
+ 
+]
+# créer les outputs
+outputs = gr.Textbox(label = 'Performance')
+# Interface
+Interface =gr.Interface(fn = prediction_func,
+             inputs = inputs,
+             outputs = outputs,
+             title = 'Student performance'
+             ,theme='NoCrypt/miku')
+Interface.launch()