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