|
|
import pandas as pd
|
|
|
import numpy as np
|
|
|
import joblib
|
|
|
import gradio as gr
|
|
|
|
|
|
|
|
|
encoder = joblib.load('encoder.joblib')
|
|
|
|
|
|
scaler = joblib.load('scaler.joblib')
|
|
|
|
|
|
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()
|
|
|
|
|
|
|
