import gradio as gr from utils.constants import CSV_HEADER, NUMERIC_FEATURE_NAMES, NUMBER_INPUT_COLS from utils.preprocess import create_max_values_map, create_dropdown_default_values_map, create_sample_test_data, CATEGORICAL_FEATURES_WITH_VOCABULARY from utils.predict import batch_predict, user_input_predict inputs_list = [] max_values_map = create_max_values_map() dropdown_default_values_map = create_dropdown_default_values_map() sample_input_df_val = create_sample_test_data() demo = gr.Blocks() with demo: gr.Markdown("# **Binary Classification using Gated Residual and Variable Selection Networks** \n") gr.Markdown("This space demonstrates the use of Gated Residual Networks (GRN) and Variable Selection Networks (VSN), proposed by Bryan Lim et al. in Temporal Fusion Transformers (TFT) for Interpretable Multi-horizon Time Series Forecasting for structured data classification") gr.Markdown("Play around and see yourself 🤗 ") with gr.Tabs(): with gr.TabItem("Predict using Example inputs"): gr.Markdown("**Input DataFrame** \n") input_df = gr.Dataframe(headers=CSV_HEADER,value=sample_input_df_val,) gr.Markdown("**Output DataFrame** \n") output_df = gr.Dataframe() gr.Markdown("**Make Predictions**") with gr.Row(): compute_button = gr.Button("Predict") with gr.TabItem("Tweak inputs Yourself & Predict"): with gr.Tabs(): with gr.TabItem("Numerical Inputs"): gr.Markdown("Set values for numerical inputs here.") for num_variable in NUMERIC_FEATURE_NAMES: with gr.Column(): if num_variable in NUMBER_INPUT_COLS: numeric_input = gr.Number(label=num_variable) else: curr_max_val = max_values_map["max_"+num_variable] numeric_input = gr.Slider(0,curr_max_val, label=num_variable,step=1) inputs_list.append(numeric_input) with gr.TabItem("Categorical Inputs"): gr.Markdown("Choose values for categorical inputs here.") for cat_variable in CATEGORICAL_FEATURES_WITH_VOCABULARY.keys(): with gr.Column(): categorical_input = gr.Dropdown(CATEGORICAL_FEATURES_WITH_VOCABULARY[cat_variable], label=cat_variable, value=str(dropdown_default_values_map["max_"+cat_variable])) inputs_list.append(categorical_input) predict_button = gr.Button("Predict") final_output = gr.Label() predict_button.click(user_input_predict, inputs=inputs_list, outputs=final_output) compute_button.click(batch_predict, inputs=input_df, outputs=output_df) gr.Markdown('\n Author: Shivalika Singh
Based on this Keras example by Khalid Salama
Demo Powered by this GRN-VSN model') demo.launch()