import gradio as gr
import pickle 
# import time
import pandas as pd
import numpy as np
from utils import create_new_columns, create_processed_dataframe


pipeline_pkl = "full_pipeline.pkl"
log_reg = "logistic_reg_class_model.pkl"

# hist_df = "history.csv"

# def check_csv(csv_file, data):
#     if os.path.isfile(csv_file):
#         data.to_csv(csv_file, mode='a', header=False, index=False, encoding='utf-8')
#     else:
#         history = data.copy()
#         history.to_csv(csv_file, index=False)







def tenure_values():
    cols = ['0-2', '3-5', '6-8', '9-11', '12-14', '15-17', '18-20', '21-23', '24-26', '27-29', '30-32', '33-35', '36-38', '39-41', '42-44', '45-47', '48-50', '51-53', '54-56', '57-59', '60-62', '63-65', '66-68', '69-71', '72-74']
    return cols

def predict_churn(gender, SeniorCitizen, Partner, Dependents, Tenure, PhoneService, MultipleLines, InternetService, 
                  OnlineSecurity, OnlineBackup, DeviceProtection,TechSupport,StreamingTV, StreamingMovies, 
                  Contract, PaperlessBilling, PaymentMethod, MonthlyCharges, TotalCharges):
    
    data = [gender, SeniorCitizen, Partner, Dependents, Tenure, PhoneService, MultipleLines, InternetService, 
                   OnlineSecurity, OnlineBackup, DeviceProtection,TechSupport,StreamingTV, StreamingMovies, 
                   Contract, PaperlessBilling, PaymentMethod, MonthlyCharges, TotalCharges]
    
    x = np.array([data])
    dataframe = pd.DataFrame(x, columns=train_features)
    dataframe = dataframe.astype({'MonthlyCharges': 'float', 'TotalCharges': 'float', 'tenure': 'float'})
    dataframe_ = create_new_columns(dataframe)
    try:
        processed_data = pipeline.transform(dataframe_)
    except Exception as e:
        raise gr.Error('Kindly make sure to check/select all')
    else:
        # check_csv(hist_df, dataframe)
        # history = pd.read_csv(hist_df)

        processed_dataframe = create_processed_dataframe(processed_data, dataframe)
        predictions = model.predict_proba(processed_dataframe)
    return round(predictions[0][0], 3), round(predictions[0][1], 3)



theme = gr.themes.Default().set(body_background_fill="#0E1117",
                                 background_fill_secondary="#FFFFFF",
                                 background_fill_primary="#262730",
                                 body_text_color="#FF4B4B",
                                 checkbox_background_color='#FFFFFF', 
                                 button_secondary_background_fill="#FF4B4B")


def load_pickle(filename):
    with open(filename, 'rb') as file:
        data = pickle.load(file)
        return data

pipeline = load_pickle(pipeline_pkl)
model = load_pickle(log_reg)

train_features = ['gender', 'SeniorCitizen', 'Partner', 'Dependents','tenure', 'PhoneService', 'MultipleLines', 'InternetService', 
                   'OnlineSecurity', 'OnlineBackup', 'DeviceProtection','TechSupport','StreamingTV', 'StreamingMovies', 
                   'Contract', 'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges', 'TotalCharges']


# theme = gr.themes.Base()
with  gr.Blocks(theme=theme) as demo:
    gr.HTML("""
    <h1 style="color:white; text-align:center">Customer Churn Classification App</h1>
    <h2 style="color:white;">Welcome Cherished User 👋 </h2>
    <h4 style="color:white;">Start predicting customer churn.</h4>
    
    """)
    with gr.Row():
        gender = gr.Dropdown(label='Gender', choices=['Female', 'Male'])
        Contract  = gr.Dropdown(label='Contract', choices=['Month-to-month', 'One year', 'Two year'])
        InternetService = gr.Dropdown(label='Internet Service', choices=['DSL', 'Fiber optic', 'No'])

    with gr.Accordion('Yes or no'):

        with gr.Row():
            OnlineSecurity = gr.Radio(label="Online Security", choices=["Yes", "No", "No internet service"])
            OnlineBackup = gr.Radio(label="Online Backup", choices=["Yes", "No", "No internet service"])
            DeviceProtection = gr.Radio(label="Device Protection", choices=["Yes", "No", "No internet service"])
            TechSupport = gr.Radio(label="Tech Support", choices=["Yes", "No", "No internet service"])
            StreamingTV = gr.Radio(label="TV Streaming", choices=["Yes", "No", "No internet service"])
            StreamingMovies = gr.Radio(label="Movie Streaming", choices=["Yes", "No", "No internet service"]) 
        with gr.Row():
            SeniorCitizen = gr.Radio(label="Senior Citizen", choices=["Yes", "No"])
            Partner = gr.Radio(label="Partner", choices=["Yes", "No"])
            Dependents = gr.Radio(label="Dependents", choices=["Yes", "No"])
            PaperlessBilling = gr.Radio(label="Paperless Billing", choices=["Yes", "No"])
            PhoneService = gr.Radio(label="Phone Service", choices=["Yes", "No"])
            MultipleLines = gr.Radio(label="Multiple Lines", choices=["No phone service", "Yes", "No"]) 
    
    with gr.Row():
        MonthlyCharges = gr.Number(label="Monthly Charges")
        TotalCharges = gr.Number(label="Total Charges")
        Tenure = gr.Number(label='Months of Tenure')
        PaymentMethod = gr.Dropdown(label="Payment Method", choices=["Electronic check", "Mailed check", "Bank transfer (automatic)", "Credit card (automatic)"])

    submit_button = gr.Button('Prediction')
    # print(type([[122, 456]]))
    
    with gr.Row():
        with gr.Accordion('Churn Prediction'):
            output1 = gr.Slider(maximum=1,
                                minimum=0,
                                value=0.0,
                                label='Yes')
            output2 = gr.Slider(maximum=1,
                                minimum=0,
                                value=0.0,
                                label='No')
        # with gr.Accordion('Input History'):
        #     output3 = gr.Dataframe()

    submit_button.click(fn=predict_churn, inputs=[gender, SeniorCitizen, Partner, Dependents, Tenure, PhoneService, MultipleLines,     
                                                  InternetService, OnlineSecurity, OnlineBackup, DeviceProtection,TechSupport,StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod, MonthlyCharges, TotalCharges], outputs=[output1, output2])


demo.launch(debug=True)