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

def data_description(action_type):
    df = pd.read_csv('emp_experience_data.csv')
    pd.options.display.max_columns = 25
    pd.options.display.max_rows = 10
    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])
    input_data = data_encoded.drop(['Attrition'], axis=1)
    target_data = data_encoded[['Attrition']]
    col_values = list(input_data.columns.values)

    if action_type == "Input Data":
        return input_data.head()
    if action_type == "Target Data":
        return target_data.head()
    if action_type == "Feature Selection By Mutual Information":
        feature_scores = mutual_info_classif(input_data, target_data)
        data = [["Feature", "Mutual Information (0: independent, 1: dependent)"]]
        for score, fname in sorted(zip(feature_scores, col_values), reverse=True)[:10]:
            data.append([fname, score])
        return data
    if action_type == "Feature Selection By Chi Square":
        feature_scores = chi2(input_data, target_data)[0]
        data = [["Feature", "Chi-Square (Frequency Distribution)"]]
        for score, fname in sorted(zip(feature_scores, col_values), reverse=True)[:10]:
            data.append([fname, score])
        return data

inputs = [
        gr.Dropdown(["Input Data", "Target Data", "Feature Selection By Mutual Information", "Feature Selection By Chi Square"], label="Develop Data Models")
    ]

outputs = [gr.DataFrame()]

demo = gr.Interface(
    fn = data_description,
    inputs = inputs,
    outputs = outputs,
    title="Employee-Experience: Model Development",
    allow_flagging=False
)

if __name__ == "__main__":
    demo.launch()