app/pages.py

import streamlit as st
from app.draw_diagram import *

def dashboard():

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


    st.markdown("""
            ### Changelog
                
            - **Dec 2024**:
                - Added model: **aisingapore/llama3.1-70b-cpt-sea-lionv3-instruct**.
                - Updated results for **Cross-MMLU**, **Cross-LogiQA**, **Cross-XQuad**, **MMLU**, **IndoMMLU**, and **SG-Eval-v2** with new prompts (simple prompts to encourage reasoning).
                - Added new models: **SEA-LION v3**, **Gemma-2**, and **Sailor 2**.

            - **Nov 2024**:
                - Updated layout and added support for comparison between models with similar sizes.
            """)

    st.divider()

    st.markdown("#### What is [SeaEval](https://seaeval.github.io/)?")

    with st.container():
        left_co, cent_co,last_co = st.columns(3)
        with cent_co:
            st.image("./style/seaeval_overall.png", 
                    #  caption="SeaEval data range", 
                    width=500)
        st.markdown('''

                    ''')
        st.markdown("##### A benchmark for multilingual, multicultral foundation model evaluation consisting of >30 dataset and we are keep expanding over time.")
        st.markdown(''':star: How models understand and reason with natural language?    
                    :balloon: Languages: English, Chinese, Malay, Spainish, Indonedian, Vietnamese, Filipino.
                    ''')
        
        st.markdown(''':star: How models comprehend cultural practices, nuances and values?    
                    :balloon: 4 new datasets on Cultural Understanding.
                    ''')

        st.markdown(''':star: How models perform across languages in terms of consistency?    
                    :balloon: 2 new datasets with curated metrics for Cross-Linugal Consistency.
                    ''')
    with st.container():
        left_co, cent_co,last_co = st.columns(3)
        with cent_co:
            st.image("./style/consistency.png", 
                    #  caption="SeaEval data range", 
                    width=500)
        st.markdown("##### Evaluation with enhanced cross-lingual capabilities.")
        st.markdown(''':star: How models perform according to different (paraphrased) instructions?    
                    :balloon: Each dataset is equipped with 5 different prompts to avoid randomness introduced by instructions,
                    which is non-negligible..
                    ''')
        
        st.markdown(''':star: Multilingual accuracy and performance consistency across languages.    
                    :balloon: If you can answer the question in your native language, can you answer the same question 
                    correctly in your second/third language?
                    ''')

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

        st.markdown('''
                    ```
                        @article{SeaEval,
                        title={SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning},
                        author={Wang, Bin and Liu, Zhengyuan and Huang, Xin and Jiao, Fangkai and Ding, Yang and Aw, Ai Ti and Chen, Nancy F.},
                        journal={NAACL},
                        year={2024}
                        }
                    ```
                    ''')
        

def cross_lingual_consistency():
    st.title("Task: Cross-Lingual Consistency")
    
    filters_levelone = ['Zero Shot', 'Few Shot']
    filters_leveltwo = [
                        'Cross-MMLU', 
                        'Cross-XQUAD', 
                        'Cross-LogiQA', 
                        ]
    
    category_one_dict = {
                        'Zero Shot': 'zero_shot',
                        'Few Shot' : 'few_shot'
                         }
    
    category_two_dict = {
                        'Cross-MMLU'            : 'cross_mmlu_no_prompt',
                        'Cross-XQUAD'           : 'cross_xquad_no_prompt',
                        'Cross-LogiQA'          : 'cross_logiqa_no_prompt',
                         }

    left, center, middle, _, right = st.columns([0.2, 0.2, 0.2, 0.2 ,0.2])
    with left:
        category_one = st.selectbox('Zero or Few Shot', filters_levelone)
    with center:
        category_two = st.selectbox('Dataset', filters_leveltwo)
    with middle:
        model_size_range = st.selectbox('Model Size', ['All', '<10B', '10B-30B', '>30B'])

    with right:
        sort = st.selectbox('Sort (For Chart)', ['Accuracy','Cross-Lingual Consistency', 'AC3',
                                     'English', 'Chinese', 'Spanish', 'Vietnamese'])
       
    sortby = 'Ascending'

    if category_one or category_two or sort or sortby:
        category_one = category_one_dict[category_one]
        category_two = category_two_dict[category_two]
        
        draw('cross_lingual', category_one, category_two, sort, sortby, model_size_range)


def cultural_reasoning():
    st.title("Task: Cultural Reasoning")

    filters_levelone = ['Zero Shot', 'Few Shot']
    filters_leveltwo = [
                        'SG-EVAL-v2-MCQ',
                        'SG-EVAL-v2-Open-Ended',
                        'CN-EVAL', 
                        'PH-EVAL', 
                        'US-EVAL'
                        ]
    
    category_one_dict = {'Zero Shot': 'zero_shot', 
                         'Few Shot': 'few_shot'
                         }
    
    category_two_dict = {
                         'SG-EVAL-v2-MCQ'          : 'sg_eval_v2_mcq_no_prompt',
                         'SG-EVAL-v2-Open-Ended'   : 'sg_eval_v2_open',
                         'US-EVAL'                 : 'us_eval',
                         'CN-EVAL'                 : 'cn_eval',
                         'PH-EVAL'                 : 'ph_eval'
                         }

    left, center, middle, _, right = st.columns([0.2, 0.2, 0.2, 0.2 ,0.2])
    with left:
        category_one = st.selectbox('Zero or Few Shot', filters_levelone)
    with center:
        category_two = st.selectbox('Dataset', filters_leveltwo)
    with middle:
        model_size_range = st.selectbox('Model Size', ['All', '<10B', '10B-30B', '>30B'])

    sortby = 'Ascending'
    
    if category_one or category_two or sortby:
        category_one = category_one_dict[category_one]
        category_two = category_two_dict[category_two]
        draw('cultural_reasoning', category_one, category_two, 'Accuracy', sortby, model_size_range)


def general_reasoning():
    st.title("Task: General Reasoning")

    filters_levelone = ['Zero Shot', 'Few Shot']

    filters_leveltwo = [
                        'IndoMMLU',
                        'MMLU', 
                        'CMMLU', 
                        'C-Eval', 
                        #'ZBench', 
                        ]
    
    category_one_dict = {
                        'Zero Shot': 'zero_shot',
                        'Few Shot' : 'few_shot'
                         }

    category_two_dict = {
                         'IndoMMLU': 'indommlu_no_prompt',
                         'MMLU'    : 'mmlu_no_prompt',
                         'C-Eval'  : 'c_eval',
                         'CMMLU'   : 'cmmlu_no_prompt',
                         #'ZBench'  : 'zbench',
                         }

    left, center, middle, _, right = st.columns([0.2, 0.2, 0.2, 0.2 ,0.2])
    with left:
        category_one = st.selectbox('Zero or Few Shot', filters_levelone)
    with center:
        category_two = st.selectbox('Dataset', filters_leveltwo)
    with middle:
        model_size_range = st.selectbox('Model Size', ['All', '<10B', '10B-30B', '>30B'])
    
    sortby = 'Ascending'

    if category_one or category_two or sortby:
        category_one = category_one_dict[category_one]
        category_two = category_two_dict[category_two]
        draw('general_reasoning', category_one, category_two, 'Accuracy', sortby, model_size_range)


def flores():
    st.title("Task: FLORES-Translation")

    filters_levelone = ['Zero Shot', 'Few Shot']
    filters_leveltwo = [
                        'Indonesian to English',
                        'Vitenamese to English', 
                        'Chinese to English', 
                        'Malay to English'
                        ]
    
    category_one_dict = {
                        'Zero Shot': 'zero_shot',
                        'Few Shot' : 'few_shot'
                         }
    category_two_dict = {
                        'Indonesian to English': 'ind2eng',
                        'Vitenamese to English': 'vie2eng',
                        'Chinese to English'   : 'zho2eng',
                        'Malay to English'     : 'zsm2eng'
                         }
    

    left, center, middle, _, right = st.columns([0.2, 0.2, 0.2, 0.2 ,0.2])
    with left:
        category_one = st.selectbox('Zero or Few Shot', filters_levelone)
    with center:
        category_two = st.selectbox('Dataset', filters_leveltwo)
    with middle:
        model_size_range = st.selectbox('Model Size', ['All', '<10B', '10B-30B', '>30B'])

    sortby = 'Ascending'

    if category_one or category_two or sortby:
        category_one = category_one_dict[category_one]
        category_two = category_two_dict[category_two]
        draw('flores_translation', category_one, category_two, 'BLEU', sortby, model_size_range)

    st.markdown('For translation tasks, BLEU score is used as the evaluation metric. However, it is sensitive to the length of the sentence and the truncation of model outputs may not functioning properly. If found obvious discrepencies, it might from the truncation inaccruacy. Please contact for correction.')


def emotion():
    st.title("Task: Emotion")

    filters_levelone = ['Zero Shot', 'Few Shot']
    filters_leveltwo = [
                        'Indonesian Emotion  Classification', 
                        'SST2',
                        ]
    
    category_one_dict = {'Zero Shot': 'zero_shot', 
                         'Few Shot': 'few_shot'}
    category_two_dict = {
                            'Indonesian Emotion  Classification': 'ind_emotion',
                            'SST2'                              : 'sst2'
                             }

    left, center, middle, _, right = st.columns([0.2, 0.2, 0.2, 0.2 ,0.2])
    with left:
        category_one = st.selectbox('Zero or Few Shot', filters_levelone)
    with center:
        category_two = st.selectbox('Dataset', filters_leveltwo)
    with middle:
        model_size_range = st.selectbox('Model Size', ['All', '<10B', '10B-30B', '>30B'])
    
    sortby = 'Ascending'


    if category_one or category_two or sortby:
        category_one = category_one_dict[category_one]
        category_two = category_two_dict[category_two]
        draw('emotion', category_one, category_two, 'Accuracy', sortby, model_size_range)

def dialogue():
    st.title("Task: Dialogue")

    filters_levelone = ['Zero Shot', 'Few Shot']
    filters_leveltwo = [
                        'DREAM', 
                        'SAMSum', 
                        'DialogSum',
                        ]
    
    category_one_dict = {
                        'Zero Shot': 'zero_shot',
                        'Few Shot' : 'few_shot'
                         }
    category_two_dict = {
                        'DREAM'    : 'dream',
                        'SAMSum'   : 'samsum',
                        'DialogSum': 'dialogsum'
                         }

    left, center, middle, _, right = st.columns([0.2, 0.2, 0.2, 0.2 ,0.2])
    with left:
        category_one = st.selectbox('Zero or Few Shot', filters_levelone)
    with center:
        category_two = st.selectbox('Dataset', filters_leveltwo)
    with right:
        if category_two == 'DREAM':
            sort = st.selectbox('Sort', ['Accuracy'])
        else:
            sort = st.selectbox('Sort', ['Average', 'ROUGE-1', 'ROUGE-2', 'ROUGE-L'])

    with middle:
        model_size_range = st.selectbox('Model Size', ['All', '<10B', '10B-30B', '>30B'])
    
    sortby = 'Ascending'

    if category_one or category_two or sort or sortby:
        category_one = category_one_dict[category_one]
        category_two = category_two_dict[category_two]
        draw('dialogue', category_one, category_two, sort, sortby, model_size_range)


def fundamental_nlp_tasks():
    st.title("Task: Fundamental NLP Tasks")

    filters_levelone = ['Zero Shot', 'Few Shot']
    filters_leveltwo = ['OCNLI', 'C3', 'COLA', 'QQP', 'MNLI', 'QNLI', 'WNLI', 'RTE', 'MRPC']
    
    category_one_dict = {
                         'Zero Shot': 'zero_shot',
                         'Few Shot' : 'few_shot'
                         }
    category_two_dict = {
                        'OCNLI': 'ocnli',
                        'C3'   : 'c3',
                        'COLA' : 'cola',
                        'QQP'  : 'qqp',
                        'MNLI' : 'mnli',
                        'QNLI' : 'qnli',
                        'WNLI' : 'wnli',
                        'RTE'  : 'rte',
                        'MRPC' : 'mrpc'
                        }

    left, center, middle, _, right = st.columns([0.2, 0.2, 0.2, 0.2 ,0.2])
    with left:
        category_one = st.selectbox('Zero or Few Shot', filters_levelone)
    with center:
        category_two = st.selectbox('Dataset', filters_leveltwo)
    with middle:
        model_size_range = st.selectbox('Model Size', ['All', '<10B', '10B-30B', '>30B'])
    
    sortby = 'Ascending'

    if category_one or category_two or sortby:
        category_one = category_one_dict[category_one]
        category_two = category_two_dict[category_two]
        draw('fundamental_nlp_tasks', category_one, category_two, 'Accuracy', sortby, model_size_range)