import gradio as gr

# from arguments import init_args
from gr_app.GradioApp import GradioApp
from gr_app import args

app = GradioApp()

demo = gr.Blocks(**args.block)

with demo:
    warning = gr.Warning()
    gr.Markdown('# Sentient.io - Demand Forecasting')
    gr.Markdown('Demo for demand forecasting pipeline')

    gr.Markdown('---')

    gr.Markdown('# Step 1 - Load Data')
    with gr.Row():
        gr.Markdown('''
                    Use button "Load Demo Data" for a quick demo with pre-loaded data. For uploading your own data, please follow the below requirements.
                    
                    ### Data Requirements:
                    - Time series data have to be in CSV format
                    - Data must contains datetime, y and sku columns. 
                    - Multiple SKUs can put in to same CSV
                    - Time interval in data must be consistent
                    - Missing value have to be filled up
                    ''')

        with gr.Column():
            btn_load_data = gr.Button('Load Demo Data')

            gr.Markdown('------ or ------',
                        elem_classes="demo_app_text_center")

            file_upload_data = gr.File(**args.file_upload_data)

    df_ts_data = gr.DataFrame(**args.df_ts_data)

    gr.Markdown('---')

    gr.Markdown('# Step 2 - Model Selection')

    with gr.Row():
        gr.Markdown('''
                    Train and evaluate model, identify data characteristics and select the best performing model. This step only need to run when the market regime shifted or when need to to re-select the model.
                    
                    - Click "Use Demo Data" Button if the demo data set has been loaded in Step 1
                    - Else, directly proceed to model selection
                    - Only upload dataset if the model select process had been previously done, and you have save a copy of the CSV response. 
                    ''')

        with gr.Column():
            btn_load_model_data = gr.Button('Use Demo Data')
            btn_model_selection = gr.Button('Model Selection', variant='primary')
            gr.Markdown('Upload previous model selection result (if have):')
            file_upload_model_data = gr.File(**args.file_upload_model_data)

    df_model_data = gr.DataFrame()
    file_model_data = gr.File()

    gr.Markdown('# Step 3 - Forecasting')

    with gr.Row():
        gr.Markdown(
            'This step only can be done when model selection process is completed.')

        with gr.Column():
            gr.Markdown('''
                        ### Forecast Horizon
                        Max horizon will be 20% of provided data range. The unit will be same as the time series data time interval.
                        ''')
            slider_forecast_horizon = gr.Slider(**args.slider_forecast_horizon)

            btn_forecast = gr.Button("Forecast", variant='primary')

    df_forecast = gr.DataFrame(**args.df_forecast)
    file_forecast = gr.File()

    # ============= #
    # = Functions = #
    # ============= #

    btn_load_data.click(
        app.btn_load_data__click,
        [],
        [df_ts_data, df_model_data, file_model_data, slider_forecast_horizon])

    file_upload_data.upload(
        app.file_upload_data__upload,
        [file_upload_data],
        [df_ts_data, df_model_data, file_model_data, slider_forecast_horizon])

    file_upload_model_data.upload(
        app.file_upload_model_data__upload,
        [file_upload_model_data],
        [df_model_data, file_model_data]
    )

    btn_load_model_data.click(
        app.btn_load_model_data__click,
        [], [df_model_data, file_model_data]
    )

    btn_model_selection.click(
        app.btn_model_selection__click,
        [], [df_model_data, file_model_data])

    btn_forecast.click(
        app.btn_forecast__click,
        [], [df_forecast, file_forecast]
    )

    slider_forecast_horizon.change(
        app.slider_forecast_horizon__update,
        [slider_forecast_horizon],
        [])


demo.launch()