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app.py

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+import pandas as pd
+import numpy as np
+import joblib
+import gradio as gr
+
+#importer l'encoder
+encoder = joblib.load('encoder.joblib')
+#importer le scaler
+scaler = joblib.load('scaler.joblib')
+#importer le model
+ls = joblib.load('ls.joblib')
+
+def pred_func(HS,  PS, EA,  SH,  SQPP):
+    EA = encoder.transform([EA])[0]
+    x_new = np.array([HS,  PS, EA,  SH,  SQPP])
+    x_new = x_new.reshape(1, -1)
+    x_new = scaler.transform(x_new)
+    y_pred = ls.predict(x_new)
+    y_pred = round(y_pred[0], 2)
+    return f"la performance de cet etudiant est: {str(y_pred)}"
+
+def pred_func_csv(file):
+    df = pd.read_csv(file)
+    
+    prediction = []
+    
+    for row in df.iloc[:, :].values:
+        new_row = np.array([row[0], row[1], encoder.transform([row[2]])[0], row[3], row[4]])
+        new_row = new_row.reshape(1, -1)
+        new_row = scaler.transform(new_row)
+        y_pred = ls.predict(new_row)
+        y_pred = round(y_pred[0], 2)
+        prediction.append(y_pred)
+        
+    df['Performance Index']  = prediction
+    df.to_csv('predictions.csv', index= False)
+    return 'predictions.csv'
+    
+demo = gr.Blocks(theme= gr.themes.Origin())
+
+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')
+]
+outputs = gr.Textbox(label='Performance Index')
+
+interface1 = gr.Interface(fn= pred_func,
+                          inputs= inputs,
+                          outputs= outputs,
+                          title = "Predire les performance de l'etudiant en saisant les données",
+                          description= """Cette modele permet de predire les performation d'un etudiant a partir de quelques un de ces informations"""
+                          )
+interface2 = gr.Interface(
+    fn = pred_func_csv,
+    inputs = gr.File(label= 'Telecharger le document csv'),
+    outputs= gr.File(label= 'Telecharger le documents csv'),
+    title= "Predictions multiple en inserant un fichier csv",
+    description= """Cette modele permet de predire les performation d'un etudiant a partir de quelques un de ces informations"""
+)
+with demo:
+    gr.TabbedInterface([interface1, interface2], ['Predictions simple', 'Predictions multiple'])
+    
+demo.launch()
+    

encoder.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4d523e95304cbd88e9f82f9dd6dd0ffd59a0006e2fbe059db4aac0cb9a50f257
+size 484

ls.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ca33d60704ee251c743bf2d2ecb82b54e6226b54e9c5314c385320d66f95f632
+size 672

requirements.txt

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+pandas
+numpy
+joblib
+gradio

scaler.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0b2795d2d0f5d97d0ed842f9073390b925767a4fed4ef1b85a631f100897abd5
+size 575