app/pages.py

import streamlit as st
from app.draw_diagram import *
from app.content import *

def dataset_contents(dataset, metrics):
    
    custom_css = """
                <style>
                .my-dataset-info {
                # background-color: #F9EBEA;
                # padding: 10px;
                color: #626567;
                font-style: italic;
                font-size: 8px;
                height: auto;
                }
                </style>
                """
    st.markdown(custom_css, unsafe_allow_html=True)
    st.markdown(f"""<div class="my-dataset-info">
                    <p><b>Dataset Information</b>: {dataset}</p>
                    </div>""", unsafe_allow_html=True)
    st.markdown(f"""<div class="my-dataset-info">
                    <p><b>Metric Information</b>: {metrics}</p>
                    </div>""", unsafe_allow_html=True)


def dashboard():

    with st.container():
        st.title("AudioBench")
   
        st.markdown("""
            [gh]: https://github.com/AudioLLMs/AudioBench
            [![GitHub watchers](https://img.shields.io/github/watchers/AudioLLMs/AudioBench?style=social)][gh]
            [![GitHub Repo stars](https://img.shields.io/github/stars/AudioLLMs/AudioBench?style=social)][gh]
            """)

    audio_url = "https://arxiv.org/abs/2406.16020"

    st.divider()
    st.markdown("#### [AudioBench](%s)" % audio_url)
    st.markdown("##### :dizzy: A comprehensive evaluation benchmark designed for general instruction-following audiolanguage models")
    st.markdown('''

                
                ''')

    with st.container():
        left_co, center_co, right_co = st.columns([0.5,1, 0.5])
        with center_co:
            st.image("./style/audio_overview.png", 
                     caption="Overview of the datasets in AudioBench.", 
                     use_column_width = True)
        
        st.markdown('''

                
                ''')
        
        st.markdown("###### :dart: Our Benchmark includes: ")
        cols = st.columns(10)
        cols[1].metric(label="Tasks", value="8") #delta="Tasks", delta_color="off"
        cols[2].metric(label="Datasets", value="26")
        cols[3].metric(label="Test Models", value="5")

        # st.markdown("###### :dart: Supported Models and Datasets: ")
        
        # sup = pd.DataFrame(
        #         {"Dataset": "LibriSpeech-Clean", 
        #          "Category": st.selectbox('category', ['Speech Understanding']),
        #          "Task": st.selectbox('task', ['Automatic Speech Recognition']),
        #          "Metrics": st.selectbox('metrics', ['WER']),
        #          "Status":True}
        # )
        
        # st.data_editor(sup, num_rows="dynamic")


    st.divider()
    with st.container():
        st.markdown("##### Citations")

        st.markdown('''
                    :round_pushpin: AudioBench Paper \n
                        @article{wang2024audiobench,
                            title={AudioBench: A Universal Benchmark for Audio Large Language Models},
                            author={Wang, Bin and Zou, Xunlong and Lin, Geyu and Sun, Shuo and Liu, Zhuohan and Zhang, Wenyu and Liu, Zhengyuan and Aw, AiTi and Chen, Nancy F},
                            journal={arXiv preprint arXiv:2406.16020},
                            year={2024}
                            }
                    ''')

def asr():
    st.title("Automatic Speech Recognition")
    
    filters_levelone = ['LibriSpeech-Test-Clean', 
                        'LibriSpeech-Test-Other', 
                        'Common-Voice-15-En-Test', 
                        'Peoples-Speech-Test', 
                        'GigaSpeech-Test', 
                        'Earnings21-Test', 
                        'Earnings22-Test', 
                        'Tedlium3-Test', 
                        'Tedlium3-Long-form-Test', 
                        'IMDA-Part1-ASR-Test', 
                        'IMDA-Part2-ASR-Test']
    
    left, center, _, middle,right = st.columns([0.2, 0.2, 0.2, 0.2 ,0.2])
    
    with left:
        filter_1 = st.selectbox('Select Dataset', filters_levelone)
    
    # with middle:
    #     if filter_1 == filters_levelone[0]:
    #         sort_leveltwo = ['LibriSpeech-Test-Clean', 'LibriSpeech-Test-Other', 'Common-Voice-15-En-Test', 'Peoples-Speech-Test',
    #                         'GigaSpeech-Test', 'Tedlium3-Test','Tedlium3-Long-form-Test', 'Earning-21-Test', 'Earning-22-Test']
    #     elif filter_1 == filters_levelone[1]:
    #         sort_leveltwo = ['CN-College-Listen-Test', 'SLUE-P2-SQA5-Test', 'DREAM-TTS-Test', 'Public-SG-SpeechQA-Test']
        
    #     elif filter_1 == filters_levelone[2]:
    #         sort_leveltwo = ['OpenHermes-Audio-Test', 'ALPACA-Audio-Test']
        
    #     sort = st.selectbox("Sort Dataset", sort_leveltwo)
    
    # with right:
    #     sorted = st.selectbox('by', ['Ascending', 'Descending'])

    if filter_1:
        dataset_contents(asr_datsets[filter_1], metrics['wer'])
        draw('su', 'ASR', filter_1, 'wer', cus_sort=True)
    # else:
    #     draw('su', 'ASR', 'LibriSpeech-Test-Clean', 'wer')


    ## examples
    

def sqa():
    st.title("Speech Question Answering")
    
    binary = ['CN-College-Listen-MCQ-Test', 'DREAM-TTS-MCQ-Test']

    rest = ['SLUE-P2-SQA5-Test', 
            'Public-SG-Speech-QA-Test', 
            'Spoken-Squad-Test']

    filters_levelone = binary + rest
    
    left, center, _, middle,right = st.columns([0.2, 0.2, 0.2, 0.2 ,0.2])
    
    with left:
        filter_1 = st.selectbox('Select Dataset', filters_levelone)

    if filter_1:
        if filter_1 in binary:
            dataset_contents(sqa_datasets[filter_1], metrics['llama3_70b_judge_binary'])
            draw('su', 'SQA', filter_1, 'llama3_70b_judge_binary')
        
        else:
            dataset_contents(sqa_datasets[filter_1], metrics['llama3_70b_judge'])
            draw('su', 'SQA', filter_1, 'llama3_70b_judge')
    # else:
    #     draw('su', 'SQA', 'CN-College-Listen-Test', 'llama3_70b_judge_binary')

def si():
    st.title("Speech Question Answering")
    
    filters_levelone = ['OpenHermes-Audio-Test', 
                        'ALPACA-Audio-Test']
    
    left, center, _, middle,right = st.columns([0.2, 0.2, 0.2, 0.2 ,0.2])
    
    with left:
        filter_1 = st.selectbox('Select Dataset', filters_levelone)

    if filter_1:
        dataset_contents(si_datasets[filter_1], metrics['llama3_70b_judge'])
        draw('su', 'SI', filter_1, 'llama3_70b_judge')
    # else:
    #     draw('su', 'SI', 'OpenHermes-Audio-Test', 'llama3_70b_judge')

def ac():
    st.title("Audio Captioning")

    filters_levelone = ['WavCaps-Test', 
                        'AudioCaps-Test']
    filters_leveltwo = ['Llama3-70b-judge', 'Meteor']
    
    left, center, _, middle,right = st.columns([0.2, 0.2, 0.2, 0.2 ,0.2])
    
    with left:
        filter_1 = st.selectbox('Select Dataset', filters_levelone)
    with middle:
        metric = st.selectbox('Select Metric', filters_leveltwo)
    
    # with middle:
    #     if filter_1 == filters_levelone[0]:
    #         sort_leveltwo = ['Clotho-AQA-Test', 'WavCaps-QA-Test', 'AudioCaps-QA-Test']
    #     elif filter_1 == filters_levelone[1]:
    #         sort_leveltwo = ['WavCaps-Test', 'AudioCaps-Test']
        
    #     sort = st.selectbox("Sort Dataset", sort_leveltwo)
    
    # with right:
    #     sorted = st.selectbox('by', ['Ascending', 'Descending'])

    if filter_1 or metric:
        dataset_contents(ac_datasets[filter_1], metrics[metric.lower().replace('-', '_')])
        draw('asu', 'AC',filter_1, metric.lower().replace('-', '_'))
    # else:
    #     draw('asu', 'AC', 'WavCaps-Test', 'llama3_70b_judge')

def asqa():
    st.title("Audio Scene Question Answering")

    filters_levelone = ['Clotho-AQA-Test', 
                        'WavCaps-QA-Test', 
                        'AudioCaps-QA-Test']
    
    left, center, _, middle,right = st.columns([0.2, 0.2, 0.2, 0.2 ,0.2])
    
    with left:
        filter_1 = st.selectbox('Select Dataset', filters_levelone)
    
    if filter_1:
        dataset_contents(asqa_datasets[filter_1], metrics['llama3_70b_judge'])
        draw('asu', 'AQA',filter_1, 'llama3_70b_judge')
    # else:
    #     draw('asu', 'AQA', 'Clotho-AQA-Test', 'llama3_70b_judge')

def er():
    st.title("Emotion Recognition")

    filters_levelone = ['IEMOCAP-Emotion-Test', 
                        'MELD-Sentiment-Test', 
                        'MELD-Emotion-Test']
    # sort_leveltwo = []
    
    left, center, _, middle,right = st.columns([0.2, 0.2, 0.2, 0.2 ,0.2])
    
    with left:
        filter_1 = st.selectbox('Select Dataset', filters_levelone)
    
    # with middle:
    #     if filter_1 == filters_levelone[0]:
    #         sort_leveltwo = ['IEMOCAP-Emotion-Test', 'MELD-Sentiment-Test', 'MELD-Emotion-Test']
        
    #     elif filter_1 == filters_levelone[1]:
    #         sort_leveltwo = ['VoxCeleb1-Accent-Test']
        
    #     elif filter_1 == filters_levelone[2]:
    #         sort_leveltwo = ['VoxCeleb1-Gender-Test', 'IEMOCAP-Gender-Test']
        
    #     sort = st.selectbox("Sort Dataset", sort_leveltwo)
    
    # with right:
    #     sorted = st.selectbox('by', ['Ascending', 'Descending'])

    if filter_1:
        dataset_contents(er_datasets[filter_1], metrics['llama3_70b_judge_binary'])
        draw('vu', 'ER', filter_1, 'llama3_70b_judge_binary')
    # else:
    #     draw('vu', 'ER', 'IEMOCAP-Emotion-Test', 'llama3_70b_judge_binary')

def ar():
    st.title("Accent Recognition")

    filters_levelone = ['VoxCeleb-Accent-Test']
    
    left, center, _, middle,right = st.columns([0.2, 0.2, 0.2, 0.2 ,0.2])
    
    with left:
        filter_1 = st.selectbox('Select Dataset', filters_levelone)


    if filter_1:
        dataset_contents(ar_datsets[filter_1], metrics['llama3_70b_judge'])
        draw('vu', 'AR', filter_1, 'llama3_70b_judge')


def gr():
    st.title("Gender Recognition")

    filters_levelone = ['VoxCeleb-Gender-Test', 
                        'IEMOCAP-Gender-Test']
    
    left, center, _, middle,right = st.columns([0.2, 0.2, 0.2, 0.2 ,0.2])
    
    with left:
        filter_1 = st.selectbox('Select Dataset', filters_levelone)
    
    if filter_1:
        dataset_contents(gr_datasets[filter_1], metrics['llama3_70b_judge_binary'])
        draw('vu', 'GR', filter_1, 'llama3_70b_judge_binary')
    # else:
    #     draw('vu', 'GR', 'VoxCeleb1-Gender-Test', 'llama3_70b_judge_binary')

def spt():
    st.title("Speech Translation")

    filters_levelone = ['Covost2-EN-ID-test', 
                        'Covost2-EN-ZH-test',
                        'Covost2-EN-TA-test', 
                        'Covost2-ID-EN-test', 
                        'Covost2-ZH-EN-test', 
                        'Covost2-TA-EN-test']
    
    left, center, _, middle,right = st.columns([0.2, 0.2, 0.2, 0.2 ,0.2])
    
    with left:
        filter_1 = st.selectbox('Select Dataset', filters_levelone)
    
    if filter_1:
        dataset_contents(spt_datasets[filter_1], metrics['bleu'])
        draw('su', 'ST', filter_1, 'bleu')
    # else:
    #     draw('su', 'ST', 'Covost2-EN-ID-test', 'bleu')

def cnasr():
    st.title("Chinese Automatic Speech Recognition")

    filters_levelone = ['Aishell-ASR-ZH-Test']
    
    left, center, _, middle,right = st.columns([0.2, 0.2, 0.2, 0.2 ,0.2])
    
    with left:
        filter_1 = st.selectbox('Select Dataset', filters_levelone)
    
    if filter_1:
        dataset_contents(cnasr_datasets[filter_1], metrics['wer'])
        draw('su', 'CNASR', filter_1, 'wer')
    # else:
    #     draw('su', 'CNASR', 'Aishell-ASR-ZH-Test', 'wer')