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| 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() | |