import gradio as gr
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.feature_selection import mutual_info_classif
from sklearn.feature_selection import chi2
from sklearn.linear_model import LinearRegression
import numpy as np


def update(name):
    df = pd.read_csv('emp_experience_data.csv')
    pd.options.display.max_columns = 25
    data_encoded = df.copy(deep=True)
    categorical_column = ['Attrition', 'Gender', 'BusinessTravel', 'Education', 'EmployeeExperience', 'EmployeeFeedbackSentiments', 'Designation',
                            'SalarySatisfaction', 'HealthBenefitsSatisfaction', 'UHGDiscountProgramUsage', 'HealthConscious', 'CareerPathSatisfaction', 'Region']
    label_encoding = LabelEncoder()
    for col in categorical_column:
        data_encoded[col] = label_encoding.fit_transform(data_encoded[col])

    data_selected = data_encoded[['EmployeeExperience', 'HealthBenefitsSatisfaction', 'SalarySatisfaction', 'Designation', 'HealthConscious',
                            'EmployeeFeedbackSentiments', 'Education', 'Gender', 'HoursOfTrainingAttendedLastYear', 'InternalJobMovement', 'Attrition']]

    validation_data = data_selected[100:198]
    validation_input_data = validation_data.drop(['Attrition'], axis=1)
    validation_target_data = validation_data[['Attrition']]
    reg = LinearRegression().fit(validation_input_data, validation_target_data)
    prediction_value = reg.predict(np.array([[2,2,1,3,1,2,0,1,40,1]]))
    return f"Prediction : , {prediction_value}!"

with gr.Blocks() as demo:
    gr.Markdown("Start typing below and then click **Run** to see the output.")
    with gr.Row():
        inp = gr.Textbox(placeholder="Enter Employee Experience Data")
        out = gr.Textbox()
    btn = gr.Button("Run")
    btn.click(fn=update, inputs=inp, outputs=out)

demo.launch()