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import streamlit as st |
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import pandas as pd |
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import numpy as np |
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from streamlit_echarts import st_echarts |
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from streamlit.components.v1 import html |
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from app.show_examples import * |
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import pandas as pd |
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path = "/home/Collaborative_Projects/SpeechEval-Related/leadboard_st_audio/additional_info/Leaderboard-Rename.xlsx" |
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info_df = pd.read_excel(path) |
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def draw(folder_name, category_name, dataset_name, metrics): |
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folder = f"./results/{metrics}/" |
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display_names = { |
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'SU': 'Speech Understanding', |
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'ASU': 'Audio Scene Understanding', |
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'VU': 'Voice Understanding' |
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} |
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data_path = f'{folder}/{category_name.lower()}.csv' |
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chart_data = pd.read_csv(data_path).round(3) |
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new_dataset_name = dataset_name.replace('-', '_').lower() |
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chart_data = chart_data[['Model', new_dataset_name]] |
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st.markdown(""" |
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<style> |
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.stMultiSelect [data-baseweb=select] span{ |
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max-width: 800px; |
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font-size: 0.9rem; |
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} |
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</style> |
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""", unsafe_allow_html=True) |
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display_model_names = {key.strip() :val.strip() for key, val in zip(info_df['AudioBench'], info_df['Proper Display Name'])} |
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chart_data['Model'] = chart_data['Model'].map(display_model_names) |
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models = st.multiselect("Please choose the model", |
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chart_data['Model'].tolist(), |
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default = chart_data['Model'].tolist()) |
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chart_data = chart_data[chart_data['Model'].isin(models)] |
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chart_data = chart_data.sort_values(by=[new_dataset_name], ascending=True).dropna(axis=0) |
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if len(chart_data) == 0: |
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return |
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min_value = round(chart_data.iloc[:, 1::].min().min() - 0.1*chart_data.iloc[:, 1::].min().min(), 1) |
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max_value = round(chart_data.iloc[:, 1::].max().max() + 0.1*chart_data.iloc[:, 1::].max().max(), 1) |
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options = { |
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"title": {"text": f"{display_names[folder_name.upper()]}"}, |
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"tooltip": { |
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"trigger": "axis", |
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"axisPointer": {"type": "cross", "label": {"backgroundColor": "#6a7985"}}, |
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"triggerOn": 'mousemove', |
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}, |
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"legend": {"data": ['Overall Accuracy']}, |
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"toolbox": {"feature": {"saveAsImage": {}}}, |
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"grid": {"left": "3%", "right": "4%", "bottom": "3%", "containLabel": True}, |
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"xAxis": [ |
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{ |
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"type": "category", |
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"boundaryGap": True, |
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"triggerEvent": True, |
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"data": chart_data['Model'].tolist(), |
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} |
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], |
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"yAxis": [{"type": "value", |
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"min": min_value, |
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"max": max_value, |
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"boundaryGap": True |
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}], |
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"series": [{ |
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"name": f"{dataset_name}", |
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"type": "bar", |
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"data": chart_data[f'{new_dataset_name}'].tolist(), |
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}], |
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} |
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events = { |
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"click": "function(params) { return params.value }" |
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} |
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value = st_echarts(options=options, events=events, height="500px") |
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''' |
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Show table |
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''' |
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with st.container(): |
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st.markdown('##### TABLE') |
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custom_css = """ |
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""" |
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st.markdown(custom_css, unsafe_allow_html=True) |
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model_link = {key.strip(): val for key, val in zip(info_df['Proper Display Name'], info_df['Link'])} |
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s = '' |
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for model in models: |
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try: |
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s += f"""<tr> |
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<td align="center"><input type="checkbox" name="select"></td> |
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<td><a href={model_link[model]}>{model}</a></td> |
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<td>{chart_data[chart_data['Model'] == model][new_dataset_name].tolist()[0]}</td> |
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</tr>""" |
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except: |
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continue |
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select_all_function = """<script> |
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function toggle(source) { |
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var checkboxes = document.querySelectorAll('input[type="checkbox"]'); |
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for (var i = 0; i < checkboxes.length; i++) { |
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if (checkboxes[i] != source) |
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checkboxes[i].checked = source.checked; |
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} |
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} |
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</script>""" |
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st.markdown(f""" |
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<div class="select_all">{select_all_function}</div> |
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""", unsafe_allow_html=True) |
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info_body_details = f""" |
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<table style="width:100%"> |
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<thead> |
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<tr style="text-align: center;"> |
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<th style="width:10%"><input type="checkbox" onclick="toggle(this);"></th> |
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<th style="width:45%">MODEL</th> |
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<th style="width:45%">{dataset_name}</th> |
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</tr> |
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{s} |
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</thead> |
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</table> |
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""" |
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st.markdown(f""" |
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<div class="my-data-table">{info_body_details}</div> |
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""", unsafe_allow_html=True) |
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''' |
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show samples |
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''' |
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if dataset_name in ['Earnings21-Test', 'Earnings22-Test', 'Tedlium3-Long-form-Test']: |
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pass |
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else: |
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show_examples(category_name, dataset_name, chart_data['Model'].tolist()) |
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