app/pages.py

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

def dataset_contents(dataset, metrics):
    
    custom_css = """
                <style>
                .my-dataset-info {
                # background-color: #F9EBEA;
                # padding: 10px;
                color: #050505;
                font-style: normal;
                font-size: 8px;
                height: auto;
                }
                </style>
                """
    st.markdown(custom_css, unsafe_allow_html=True)
    st.markdown(f"""<div class="my-dataset-info">
                    <p><b>About this dataset</b>: {dataset}</p>
                    </div>""", unsafe_allow_html=True)
    st.markdown(f"""<div class="my-dataset-info">
                    <p><b>About this metric</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.markdown("#### News")
    st.markdown("Dec, 2024: Update layout and support comparison between models with similar model sizes.")

    st.divider()
    
    st.markdown("#### What is [AudioBench](%s)?" % audio_url)
    st.markdown("##### :dizzy: A comprehensive evaluation benchmark designed for general instruction-following audiolanguage models.")
    st.markdown("##### :dizzy: A evaluation benchmark that we consistently put effort in updating and maintaining.")
    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_container_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=">30")
        cols[3].metric(label="Evaluated Models", value=">5")


    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("Task: Automatic Speech Recognition")
    
    sum = ['Overall']
    dataset_lists = [
                    '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', 
                    ]

    filters_levelone = sum + dataset_lists
    
    left, center, _, middle, right = st.columns([0.2, 0.2, 0.2, 0.2 ,0.2])
    
    with left:
        filter_1 = st.selectbox('Dataset', filters_levelone)
    
    if filter_1:
        if filter_1 in sum:
            sum_table_mulit_metrix('ASR', ['wer'])
        else:
            dataset_contents(asr_datsets[filter_1], metrics['wer'])
            draw('su', 'ASR', filter_1, 'wer', cus_sort=True)


def cnasr():
    st.title("Task: Automatic Speech Recognition - Mandarin")

    sum = ['Overall']
    dataset_lists = [
                    'Aishell-ASR-ZH-Test', 
                    ]

    filters_levelone = sum + dataset_lists
    
    left, center, _, middle, right = st.columns([0.2, 0.2, 0.2, 0.2 ,0.2])
    
    with left:
        filter_1 = st.selectbox('Dataset', filters_levelone)
    
    if filter_1:
        if filter_1 in sum:
            sum_table_mulit_metrix('CNASR', ['wer'])
        else:
            dataset_contents(cnasr_datasets[filter_1], metrics['wer'])
            draw('su', 'CNASR', filter_1, 'wer')

    

def sqa():
    st.title("Task: Speech Question Answering")
    
    sum = ['Overall']

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

    if filter_1:
        if filter_1 in sum:
            sum_table_mulit_metrix('SQA', ['llama3_70b_judge_binary', 'llama3_70b_judge'])

        elif 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')

def si():
    st.title("Task: Speech Instruction")
    
    sum = ['Overall']

    dataset_lists = ['OpenHermes-Audio-Test', 
                     'ALPACA-Audio-Test']
    
    filters_levelone = sum + dataset_lists
    
    left, center, _, middle, right = st.columns([0.2, 0.2, 0.2, 0.2 ,0.2])
    
    with left:
        filter_1 = st.selectbox('Dataset', filters_levelone)

    if filter_1:
        if filter_1 in sum:
            sum_table_mulit_metrix('SI', ['llama3_70b_judge'])
        else:
            dataset_contents(si_datasets[filter_1], metrics['llama3_70b_judge'])
            draw('su', 'SI', filter_1, 'llama3_70b_judge')

def ac():
    st.title("Task: 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('Dataset', filters_levelone)
    with middle:
        metric = st.selectbox('Metric', filters_leveltwo)

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


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

    sum = ['Overall']

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


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

    sum = ['Overall']

    dataset_lists = ['IEMOCAP-Emotion-Test', 
                        'MELD-Sentiment-Test', 
                        'MELD-Emotion-Test']

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

    if filter_1:
        if filter_1 in sum:
            sum_table_mulit_metrix('ER', ['llama3_70b_judge_binary'])
        else:
            dataset_contents(er_datasets[filter_1], metrics['llama3_70b_judge_binary'])
            draw('vu', 'ER', filter_1, 'llama3_70b_judge_binary')


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

    sum = ['Overall']
    dataset_lists = ['VoxCeleb-Accent-Test']


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


    if filter_1:
        if filter_1 in sum:
            sum_table_mulit_metrix('AR', ['llama3_70b_judge'])
        # sum_table('aR', 'llama3_70b_judge')
        else:
            dataset_contents(ar_datsets[filter_1], metrics['llama3_70b_judge'])
            draw('vu', 'AR', filter_1, 'llama3_70b_judge')


def gr():
    st.title("Task: Gender Recognition")
    
    sum = ['Overall']

    dataset_lists =  ['VoxCeleb-Gender-Test', 
                        'IEMOCAP-Gender-Test']

    filters_levelone = sum + dataset_lists
    
    left, center, _, middle, right = st.columns([0.2, 0.2, 0.2, 0.2 ,0.2])
    
    with left:
        filter_1 = st.selectbox('Dataset', filters_levelone)
    
    if filter_1:
        if filter_1 in sum:
            sum_table_mulit_metrix('GR', ['llama3_70b_judge_binary'])
        else:
            dataset_contents(gr_datasets[filter_1], metrics['llama3_70b_judge_binary'])
            draw('vu', 'GR', filter_1, 'llama3_70b_judge_binary')


def spt():
    st.title("Task: Speech Translation")
    
    sum = ['Overall']
    dataset_lists = [
                        'Covost2-EN-ID-test', 
                        'Covost2-EN-ZH-test',
                        'Covost2-EN-TA-test', 
                        'Covost2-ID-EN-test', 
                        'Covost2-ZH-EN-test', 
                        'Covost2-TA-EN-test']

    filters_levelone = sum + dataset_lists
    
    left, center, _, middle, right = st.columns([0.2, 0.2, 0.2, 0.2 ,0.2])
    
    with left:
        filter_1 = st.selectbox('Dataset', filters_levelone)
    
    if filter_1:
        if filter_1 in sum:
            sum_table_mulit_metrix('ST', ['bleu'])
        else:
            dataset_contents(spt_datasets[filter_1], metrics['bleu'])
            draw('su', 'ST', filter_1, 'bleu')