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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import json | |
| from streamlit_echarts import st_echarts | |
| from app.show_examples import * | |
| from app.content import * | |
| import pandas as pd | |
| from model_information import get_dataframe | |
| info_df = get_dataframe() | |
| def draw_table(dataset_displayname, metrics): | |
| with open('organize_model_results.json', 'r') as f: | |
| organize_model_results = json.load(f) | |
| dataset_nickname = displayname2datasetname[dataset_displayname] | |
| model_results = organize_model_results[dataset_nickname][metrics] | |
| model_name_mapping = {key.strip(): val for key, val in zip(info_df['Original Name'], info_df['Proper Display Name'])} | |
| model_results = {model_name_mapping.get(key, key): val for key, val in model_results.items()} | |
| # = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | |
| ''' | |
| Show Table | |
| ''' | |
| with st.container(): | |
| st.markdown('##### TABLE') | |
| model_link_mapping = {key.strip(): val for key, val in zip(info_df['Proper Display Name'], info_df['Link'])} | |
| chart_data_table = pd.DataFrame(list(model_results.items()), columns=["model_show", dataset_displayname]) | |
| chart_data_table["model_link"] = chart_data_table["model_show"].map(model_link_mapping) | |
| def highlight_first_element(x): | |
| # Create a DataFrame with the same shape as the input | |
| df_style = pd.DataFrame('', index=x.index, columns=x.columns) | |
| df_style.iloc[0, 1] = 'background-color: #b0c1d7' | |
| return df_style | |
| if dataset_displayname in [ | |
| 'LibriSpeech-Clean', | |
| 'LibriSpeech-Other', | |
| 'CommonVoice-15-EN', | |
| 'Peoples-Speech', | |
| 'GigaSpeech-1', | |
| 'Earnings-21', | |
| 'Earnings-22', | |
| 'TED-LIUM-3', | |
| 'TED-LIUM-3-LongForm', | |
| 'AISHELL-ASR-ZH', | |
| 'MNSC-PART1-ASR', | |
| 'MNSC-PART2-ASR', | |
| 'MNSC-PART3-ASR', | |
| 'MNSC-PART4-ASR', | |
| 'MNSC-PART5-ASR', | |
| 'MNSC-PART6-ASR', | |
| 'CNA', | |
| 'IDPC', | |
| 'Parliament', | |
| 'UKUS-News', | |
| 'Mediacorp', | |
| 'IDPC-Short', | |
| 'Parliament-Short', | |
| 'UKUS-News-Short', | |
| 'Mediacorp-Short', | |
| 'YTB-ASR-Batch1', | |
| 'YTB-ASR-Batch2', | |
| 'SEAME-Dev-Man', | |
| 'SEAME-Dev-Sge', | |
| ]: | |
| chart_data_table = chart_data_table.sort_values( | |
| by = chart_data_table.columns[1], | |
| ascending = True | |
| ).reset_index(drop=True) | |
| else: | |
| chart_data_table = chart_data_table.sort_values( | |
| by = chart_data_table.columns[1], | |
| ascending = False | |
| ).reset_index(drop=True) | |
| styled_df = chart_data_table.style.format( | |
| {chart_data_table.columns[1]: "{:.3f}"} | |
| ).apply( | |
| highlight_first_element, axis=None | |
| ) | |
| st.dataframe( | |
| styled_df, | |
| column_config={ | |
| 'model_show' : 'Model', | |
| chart_data_table.columns[1]: {'alignment': 'left'}, | |
| "model_link" : st.column_config.LinkColumn("Model Link"), | |
| }, | |
| hide_index=True, | |
| use_container_width=True | |
| ) | |
| # = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | |
| ''' | |
| Show Chart | |
| ''' | |
| # Initialize a session state variable for toggling the chart visibility | |
| if "show_chart" not in st.session_state: | |
| st.session_state.show_chart = False | |
| # Create a button to toggle visibility | |
| if st.button("Show Chart"): | |
| st.session_state.show_chart = not st.session_state.show_chart | |
| if st.session_state.show_chart: | |
| with st.container(): | |
| st.markdown('##### CHART') | |
| # Get Values | |
| data_values = chart_data_table.iloc[:, 1] | |
| # Calculate Q1 and Q3 | |
| q1 = data_values.quantile(0.25) | |
| q3 = data_values.quantile(0.75) | |
| # Calculate IQR | |
| iqr = q3 - q1 | |
| # Define lower and upper bounds (1.5*IQR is a common threshold) | |
| lower_bound = q1 - 1.5 * iqr | |
| upper_bound = q3 + 1.5 * iqr | |
| # Filter data within the bounds | |
| filtered_data = data_values[(data_values >= lower_bound) & (data_values <= upper_bound)] | |
| # Calculate min and max values after outlier handling | |
| min_value = round(filtered_data.min() - 0.1 * filtered_data.min(), 3) | |
| max_value = round(filtered_data.max() + 0.1 * filtered_data.max(), 3) | |
| options = { | |
| # "title": {"text": f"{dataset_name}"}, | |
| "tooltip": { | |
| "trigger": "axis", | |
| "axisPointer": {"type": "cross", "label": {"backgroundColor": "#6a7985"}}, | |
| "triggerOn": 'mousemove', | |
| }, | |
| "legend": {"data": ['Overall Accuracy']}, | |
| "toolbox": {"feature": {"saveAsImage": {}}}, | |
| "grid": {"left": "3%", "right": "4%", "bottom": "3%", "containLabel": True}, | |
| "xAxis": [ | |
| { | |
| "type": "category", | |
| "boundaryGap": True, | |
| "triggerEvent": True, | |
| "data": chart_data_table['model_show'].tolist(), | |
| } | |
| ], | |
| "yAxis": [{"type": "value", | |
| "min": min_value, | |
| "max": max_value, | |
| "boundaryGap": True | |
| # "splitNumber": 10 | |
| }], | |
| "series": [{ | |
| "name": f"{dataset_nickname}", | |
| "type": "bar", | |
| "data": chart_data_table[f'{dataset_displayname}'].tolist(), | |
| }], | |
| } | |
| events = { | |
| "click": "function(params) { return params.value }" | |
| } | |
| value = st_echarts(options=options, events=events, height="500px") | |
| # = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = | |
| ''' | |
| Show Examples | |
| ''' | |
| # Initialize a session state variable for toggling the chart visibility | |
| if "show_examples" not in st.session_state: | |
| st.session_state.show_examples = False | |
| # Create a button to toggle visibility | |
| if st.button("Show Examples"): | |
| st.session_state.show_examples = not st.session_state.show_examples | |
| if st.session_state.show_examples: | |
| st.markdown('To be implemented') | |
| # # if dataset_name in ['Earnings21-Test', 'Earnings22-Test', 'Tedlium3-Test', 'Tedlium3-Long-form-Test']: | |
| # if dataset_name in []: | |
| # pass | |
| # else: | |
| # show_examples(category_name, dataset_name, chart_data['Model'].tolist(), display_model_names) | |