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import streamlit as st | |
import datasets | |
import numpy as np | |
def show_examples(category_name, dataset_name, model_lists): | |
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") | |
with col1: | |
st.audio(f'{sample_folder}/sample_{index}.wav', format="audio/wav") | |
with col2: | |
with st.container(): | |
custom_css = """ | |
<style> | |
.my-container-question { | |
background-color: #F5EEF8; | |
padding: 10px; | |
border-radius: 10px; | |
height: auto; | |
} | |
</style> | |
""" | |
st.markdown(custom_css, unsafe_allow_html=True) | |
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"""<div class="my-container-question"> | |
<p>QUESTION: {dataset[index]['instruction']['text']}</p> | |
<p>CHOICES: {choices_text}</p> | |
</div> | |
""" | |
else: | |
question_text = f"""<div class="my-container-question"> | |
<p>QUESTION: {dataset[index]['instruction']['text']}</p> | |
</div>""" | |
st.markdown(question_text, unsafe_allow_html=True) | |
with st.container(): | |
custom_css = """ | |
<style> | |
.my-container-answer { | |
background-color: #F9EBEA; | |
padding: 10px; | |
border-radius: 10px; | |
height: auto; | |
} | |
</style> | |
""" | |
st.markdown(custom_css, unsafe_allow_html=True) | |
st.markdown(f"""<div class="my-container-answer"> | |
<p>CORRECT ANSWER: {dataset[index]['answer']['text']}</p> | |
</div>""", unsafe_allow_html=True) | |
# st.divider() | |
with st.container(): | |
custom_css = """ | |
<style> | |
.my-container-table { | |
background-color: #F2F3F4; | |
padding: 10px; | |
border-radius: 5px; | |
# height: 50px; | |
} | |
</style> | |
""" | |
st.markdown(custom_css, unsafe_allow_html=True) | |
model_lists.sort() | |
s = '' | |
if dataset_name in ['CN-College-Listen-MCQ-Test', 'DREAM-TTS-MCQ-Test']: | |
for model in model_lists: | |
try: | |
s += f"""<tr> | |
<td>{model}</td> | |
<td><p>{dataset[index][model]['text']}</p> <p>{choices_text}</p></td> | |
<td>{dataset[index][model]['model_prediction']}</td> | |
</tr>""" | |
except: | |
print(f"{model} is not in {dataset_name}") | |
continue | |
else: | |
for model in model_lists: | |
try: | |
s += f"""<tr> | |
<td>{model}</td> | |
<td>{dataset[index][model]['text']}</td> | |
<td>{dataset[index][model]['model_prediction']}</td> | |
</tr>""" | |
except: | |
print(f"{model} is not in {dataset_name}") | |
continue | |
body_details = f"""<table style="width:100%"> | |
<thead> | |
<tr style="text-align: center;"> | |
<th style="width:20%">MODEL</th> | |
<th style="width:40%">QUESTION</th> | |
<th style="width:40%">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() | |