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import streamlit as st |
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import datasets |
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import numpy as np |
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import html |
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def show_examples(category_name, dataset_name, model_lists, display_model_names): |
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st.divider() |
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sample_folder = f"./examples/{category_name}/{dataset_name}" |
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dataset = datasets.load_from_disk(sample_folder) |
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for index in range(len(dataset)): |
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with st.container(): |
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st.markdown(f'##### Example-{index+1}') |
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col1, col2 = st.columns([0.3, 0.7], vertical_alignment="center") |
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st.audio(f'{sample_folder}/sample_{index}.wav', format="audio/wav") |
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if dataset_name in ['CN-College-Listen-MCQ-Test', 'DREAM-TTS-MCQ-Test']: |
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choices = dataset[index]['other_attributes']['choices'] |
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if isinstance(choices, str): |
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choices_text = choices |
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elif isinstance(choices, list): |
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choices_text = ' '.join(i for i in choices) |
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question_text = f"""{dataset[index]['instruction']['text']} {choices_text}""" |
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else: |
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question_text = f"""{dataset[index]['instruction']['text']}""" |
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question_text = html.escape(question_text) |
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with st.container(): |
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custom_css = """ |
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<style> |
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.my-container-table, p.my-container-text { |
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background-color: #fcf8dc; |
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padding: 10px; |
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border-radius: 5px; |
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font-size: 13px; |
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# height: 50px; |
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word-wrap: break-word |
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} |
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</style> |
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""" |
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st.markdown(custom_css, unsafe_allow_html=True) |
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model_lists.sort() |
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s = f"""<tr> |
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<td><b>REFERENCE</td> |
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<td><b>{html.escape(question_text.replace('(A)', '<br>(A)').replace('(B)', '<br>(B)').replace('(C)', '<br>(C)'))} |
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</td> |
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<td><b>{html.escape(dataset[index]['answer']['text'])} |
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</td> |
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</tr> |
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""" |
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if dataset_name in ['CN-College-Listen-MCQ-Test', 'DREAM-TTS-MCQ-Test']: |
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for model in model_lists: |
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try: |
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model_prediction = dataset[index][model]['model_prediction'] |
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model_prediction = model_prediction.replace('<','').replace('>','').replace('\n','(newline)').replace('*','') |
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s += f"""<tr> |
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<td>{display_model_names[model]}</td> |
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<td> |
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{dataset[index][model]['text'].replace('Choices:', '<br>Choices:').replace('(A)', '<br>(A)').replace('(B)', '<br>(B)').replace('(C)', '<br>(C)') |
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} |
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</td> |
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<td>{html.escape(model_prediction)}</td> |
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</tr>""" |
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except: |
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print(f"{model} is not in {dataset_name}") |
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continue |
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else: |
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for model in model_lists: |
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print(dataset[index][model]['model_prediction']) |
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try: |
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model_prediction = dataset[index][model]['model_prediction'] |
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model_prediction = model_prediction.replace('<','').replace('>','').replace('\n','(newline)').replace('*','') |
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s += f"""<tr> |
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<td>{display_model_names[model]}</td> |
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<td>{html.escape(dataset[index][model]['text'])}</td> |
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<td>{html.escape(model_prediction)}</td> |
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</tr>""" |
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except: |
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print(f"{model} is not in {dataset_name}") |
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continue |
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body_details = f"""<table style="table-layout: fixed; width:100%"> |
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<thead> |
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<tr style="text-align: center;"> |
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<th style="width:20%">MODEL</th> |
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<th style="width:30%">QUESTION</th> |
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<th style="width:50%">MODEL PREDICTION</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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st.markdown(f"""<div class="my-container-table"> |
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{body_details} |
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</div>""", unsafe_allow_html=True) |
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st.text("") |
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st.divider() |
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