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()