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app/draw_diagram.py

@@ -2,77 +2,15 @@ import streamlit as st
 import pandas as pd
 import numpy as np
 from streamlit_echarts import st_echarts
-# from streamlit_echarts import JsCode
-# from streamlit_javascript import st_javascript
-# from PIL import Image 
-
-# links_dic = {"random": "https://seaeval.github.io/", 
-#              "meta_llama_3_8b": "https://huggingface.co/meta-llama/Meta-Llama-3-8B", 
-#              "mistral_7b_instruct_v0_2": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2", 
-#              "sailor_0_5b": "https://huggingface.co/sail/Sailor-0.5B", 
-#              "sailor_1_8b": "https://huggingface.co/sail/Sailor-1.8B", 
-#              "sailor_4b": "https://huggingface.co/sail/Sailor-4B", 
-#              "sailor_7b": "https://huggingface.co/sail/Sailor-7B", 
-#              "sailor_0_5b_chat": "https://huggingface.co/sail/Sailor-0.5B-Chat", 
-#              "sailor_1_8b_chat": "https://huggingface.co/sail/Sailor-1.8B-Chat", 
-#              "sailor_4b_chat": "https://huggingface.co/sail/Sailor-4B-Chat", 
-#              "sailor_7b_chat": "https://huggingface.co/sail/Sailor-7B-Chat", 
-#              "sea_mistral_highest_acc_inst_7b": "https://seaeval.github.io/", 
-#              "meta_llama_3_8b_instruct": "https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct", 
-#              "flan_t5_base": "https://huggingface.co/google/flan-t5-base", 
-#              "flan_t5_large": "https://huggingface.co/google/flan-t5-large", 
-#              "flan_t5_xl": "https://huggingface.co/google/flan-t5-xl", 
-#              "flan_t5_xxl": "https://huggingface.co/google/flan-t5-xxl", 
-#              "flan_ul2": "https://huggingface.co/google/flan-t5-ul2", 
-#              "flan_t5_small": "https://huggingface.co/google/flan-t5-small", 
-#              "mt0_xxl": "https://huggingface.co/bigscience/mt0-xxl", 
-#              "seallm_7b_v2": "https://huggingface.co/SeaLLMs/SeaLLM-7B-v2", 
-#              "gpt_35_turbo_1106": "https://openai.com/blog/chatgpt", 
-#              "meta_llama_3_70b": "https://huggingface.co/meta-llama/Meta-Llama-3-70B", 
-#              "meta_llama_3_70b_instruct": "https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct", 
-#              "sea_lion_3b": "https://huggingface.co/aisingapore/sea-lion-3b", 
-#              "sea_lion_7b": "https://huggingface.co/aisingapore/sea-lion-7b", 
-#              "qwen1_5_110b": "https://huggingface.co/Qwen/Qwen1.5-110B", 
-#              "qwen1_5_110b_chat": "https://huggingface.co/Qwen/Qwen1.5-110B-Chat", 
-#              "llama_2_7b_chat": "https://huggingface.co/meta-llama/Llama-2-7b-chat-hf", 
-#              "gpt4_1106_preview": "https://openai.com/blog/chatgpt", 
-#              "gemma_2b": "https://huggingface.co/google/gemma-2b", 
-#              "gemma_7b": "https://huggingface.co/google/gemma-7b", 
-#              "gemma_2b_it": "https://huggingface.co/google/gemma-2b-it", 
-#              "gemma_7b_it": "https://huggingface.co/google/gemma-7b-it", 
-#              "qwen_1_5_7b": "https://huggingface.co/Qwen/Qwen1.5-7B", 
-#              "qwen_1_5_7b_chat": "https://huggingface.co/Qwen/Qwen1.5-7B-Chat", 
-#              "sea_lion_7b_instruct": "https://huggingface.co/aisingapore/sea-lion-7b-instruct", 
-#              "sea_lion_7b_instruct_research": "https://huggingface.co/aisingapore/sea-lion-7b-instruct-research", 
-#              "LLaMA_3_Merlion_8B": "https://seaeval.github.io/", 
-#              "LLaMA_3_Merlion_8B_v1_1": "https://seaeval.github.io/"}
-
-# links_dic = {k.lower().replace('_', '-') : v for k, v in links_dic.items()}
-
-# # huggingface_image = Image.open('style/huggingface.jpg')
-
-# def nav_to(value):
-#     try:
-#         url = links_dic[str(value).lower()]
-#         js = f'window.open("{url}", "_blank").then(r => window.parent.location.href);'
-#         st_javascript(js)
-#     except:
-#         pass
-
-#     # nav_script = """
-#     #     <meta http-equiv="refresh" content="0; url='%s'">
-#     # """ % (url)
-#     # st.write(nav_script, unsafe_allow_html=True)
-
-# def highlight_table_line(model_name):
-
-#     st.write(model_name)
-
+from streamlit.components.v1 import html
+import pandas as pd
 
-def draw_cross_lingual(category_one, category_two, sort, sorted):
+path = "./style/Leaderboard-Rename-SeaEval.csv"
+info_df = pd.read_csv(path).dropna(axis=0)
 
-    folder = "./results/cross_lingual/"
-    subtitle = ''
+def draw(folder_name, category_one, category_two, sort, num_sort):
+    
+    folder = f"./results/{folder_name}/"
     data_path = f'{folder}/{category_one}/{category_two}.csv'
     chart_data = pd.read_csv(data_path).dropna(axis='columns').round(3)
     st.markdown("""
@@ -86,386 +24,55 @@ def draw_cross_lingual(category_one, category_two, sort, sorted):
                 }
                 </style>
                 """, unsafe_allow_html=True)
-    models = st.multiselect("Please choose the models", chart_data['Model'].tolist(), default = chart_data['Model'].tolist())
-    chart_data = chart_data[chart_data['Model'].isin(models)]
-    
-    if sorted == 'Ascending':
-        ascend = True 
-    else:
-        ascend = False
-    
-    chart_data = chart_data.sort_values(by=[sort], ascending=ascend)
-
-    min_value = round(chart_data.iloc[:, 1::].min().min() - 0.1, 1) 
-    max_value = round(chart_data.iloc[:, 1::].max().max() + 0.1, 1) 
-    
-    
-
-    if category_two in ['cross_mmlu', 'cross_logiqa']:  
-        # print(category_two)
-        
-        if category_two == 'cross_mmlu':
-            subtitle = 'Cross-MMLU'
-        
-        elif category_two == 'cross_logiqa':
-            subtitle = 'Cross-LogiQA'
-        
-        options = {
-            "title": {"text": f"{subtitle}"},
-            "tooltip": {
-                "trigger": "axis",
-                "axisPointer": {"type": "cross", "label": {"backgroundColor": "#6a7985"}},
-                "triggerOn": 'mousemove',
-            },
-            "legend": {"data": ['Overall Accuracy','Cross-Lingual Consistency', 'AC3',
-                                'English', 'Chinese', 'Spanish', 'Vietnamese', 'Indonesian', 'Malay', 'Filipino']},
-            "toolbox": {"feature": {"saveAsImage": {}}},
-            "grid": {"left": "3%", "right": "4%", "bottom": "3%", "containLabel": True},
-            "xAxis": [
-                {
-                    "type": "category",
-                    "boundaryGap": True,
-                    "triggerEvent": True,
-                    "data": chart_data['Model'].tolist(),
-                }
-            ],
-            "yAxis": [{"type": "value", 
-                    "min": min_value,
-                    "max": max_value, 
-                    "boundaryGap": True
-                    # "splitNumber": 10
-                    }],
-            "series": [
-                {
-                    "name": "Overall Accuracy",
-                    "type": "bar", # "line"
-                    "data": chart_data['Accuracy'].tolist(),
-                },
-                {
-                    "name": "Cross-Lingual Consistency",
-                    "type": "bar",
-                    "data": chart_data["Cross-Lingual Consistency"].tolist(),
-                },
-                {
-                    "name": "AC3",
-                    "type": "bar",
-                    "data": chart_data["AC3"].tolist(),
-                },
-                {
-                    "name": "English",
-                    "type": "bar",
-                    "data": chart_data["English"].tolist(),
-                },
-                {
-                    "name": "Chinese",
-                    "type": "bar",
-                    "data": chart_data["Chinese"].tolist(),
-                },
-                {
-                    "name": "Spanish",
-                    "type": "bar",
-                    "data": chart_data["Spanish"].tolist(),
-                },
-                {
-                    "name": "Vietnamese",
-                    "type": "bar",
-                    "data": chart_data["Vietnamese"].tolist(),
-                },
-                {
-                    "name": "Indonesian",
-                    "type": "bar",
-                    "data": chart_data["Indonesian"].tolist(),
-                },
-                {
-                    "name": "Malay",
-                    "type": "bar",
-                    "data": chart_data["Malay"].tolist(),
-                },
-                {
-                    "name": "Filipino",
-                    "type": "bar",
-                    "data": chart_data["Filipino"].tolist(),
-                },
-            ],
-        }
-        
-        # events = {
-        #     "click": "function(params) { return params.value }",
-        #     # "dblclick": "function(params) { return params.value }"
-        # }
-
-        value = st_echarts(options=options,  height="500px") #events=events,
-
-        
-        # if value != None:
-        #     # print(value)
-        #     nav_to(value)
-        
-        # if value != None:
-        #     highlight_table_line(value)
-
-
-    elif category_two == 'cross_xquad':
-        
-        subtitle = 'Cross-XQUAD'
-
-        options = {
-            "title": {"text": f"{subtitle}"},
-            "tooltip": {
-                "trigger": "axis",
-                "axisPointer": {"type": "cross", "label": {"backgroundColor": "#6a7985"}},
-                "triggerOn": 'mousemove',
-            },
-            "legend": {"data": ['Overall Accuracy','Cross-Lingual Consistency', 'AC3',
-                                'English', 'Chinese', 'Spanish', 'Vietnamese', 'Indonesian', 'Malay', 'Filipino']},
-            "toolbox": {"feature": {"saveAsImage": {}}},
-            "grid": {"left": "3%", "right": "4%", "bottom": "3%", "containLabel": True},
-            "xAxis": [
-                {
-                    "type": "category",
-                    "boundaryGap": True,
-                    "data": chart_data['Model'].tolist(),
-                }
-            ],
-            "yAxis": [{"type": "value",
-                    "min": min_value,
-                    "max": max_value, 
-                    "boundaryGap": True
-                    # "splitNumber": 10
-                    }],
-            "series": [
-                {
-                    "name": "Overall Accuracy",
-                    "type": "bar",
-                    "data": chart_data['Accuracy'].tolist(),
-                },
-                {
-                    "name": "Cross-Lingual Consistency",
-                    "type": "bar",
-                    "data": chart_data["Cross-Lingual Consistency"].tolist(),
-                },
-                {
-                    "name": "AC3",
-                    "type": "bar",
-                    "data": chart_data["AC3"].tolist(),
-                },
-                {
-                    "name": "English",
-                    "type": "bar",
-                    "data": chart_data["English"].tolist(),
-                },
-                {
-                    "name": "Chinese",
-                    "type": "bar",
-                    "data": chart_data["Chinese"].tolist(),
-                },
-                {
-                    "name": "Spanish",
-                    "type": "bar",
-                    "data": chart_data["Spanish"].tolist(),
-                },
-                {
-                    "name": "Vietnamese",
-                    "type": "bar",
-                    "data": chart_data["Vietnamese"].tolist(),
-                },
-            ],
-        }
-
-        # events = {
-        #     "click": "function(params) { return params.value }"
-        # }
-
-        value = st_echarts(options=options, height="500px")
-        
-        # if value != None:
-        #     # print(value)
-        #     nav_to(value)
-
-        # if value != None:
-        #     highlight_table_line(value)
-    
-    ### create table
-    st.divider()
-    # chart_data['Link'] = chart_data['Model'].map(links_dic)
-    st.dataframe(chart_data,
-                #  column_config = {
-                #      "Link": st.column_config.LinkColumn(
-                #          display_text= st.image(huggingface_image)
-                #      ),
-                #  }, 
-                    hide_index = True, 
-                    use_container_width=True)
-    
     
+    # remap model names
+    display_model_names = {key.strip() :val.strip() for key, val in zip(info_df['Original Name'], info_df['Proper Display Name'])}
+    chart_data['model_show'] = chart_data['Model'].map(display_model_names)
+    chart_data['model_show'] = chart_data['model_show'].fillna(chart_data['Model'].apply(lambda x: x.replace('_', '-')))
 
-def draw_only_acc(folder_name, category_one, category_two, sorted):
-    # Cultural Reasonling / General Reasoning / Emotion / Fundamental NLP Tasks
-    
-    folder = f"./results/{folder_name}/"
-    category_two_dict = {}
-    
-    if folder_name == 'cultural_reasoning':
-        category_two_dict = {'SG EVAL': 'sg_eval',
-                             'SG EVAL V1 Cleaned': 'sg_eval_v1_cleaned',
-                             'SG EVAL V2 MCQ': 'sg_eval_v2_mcq',
-                             'SG EVAL V2 Open Ended': 'sg_eval_v2_open',
-                             'US EVAL': 'us_eval', 
-                             'CN EVAL': 'cn_eval', 
-                             'PH EVAL': 'ph_eval'}
-    elif folder_name == 'general_reasoning':
-        category_two_dict = {'MMLU': 'mmlu', 
-                         'C Eval': 'c_eval', 
-                         'CMMLU': 'cmmlu', 
-                         'ZBench': 'zbench', 
-                         'IndoMMLU': 'indommlu'}
-    
-    elif folder_name == 'emotion':
-        category_two_dict = {'Indonesian Emotion  Classification': 'ind_emotion', 
-                             'SST2': 'sst2'}
-    
-    elif folder_name == 'fundamental_nlp_tasks':
-        category_two_dict = {'OCNLI': 'ocnli', 
-                             'C3': 'c3', 
-                             'COLA': 'cola', 
-                             'QQP': 'qqp', 
-                             'MNLI': 'mnli', 
-                             'QNLI': 'qnli', 
-                             'WNLI': 'wnli', 
-                             'RTE': 'rte', 
-                             'MRPC': 'mrpc'}
+    st.session_state.models = st.multiselect("Please choose the model", 
+                                                sorted(chart_data['model_show'].tolist()), 
+                                                default = sorted(chart_data['model_show'].tolist()))
     
-    subtitle = category_two_dict[category_two]
-    data_path = f'{folder}/{category_one}/{subtitle}.csv'
-    chart_data = pd.read_csv(data_path).round(3)
+    chart_data = chart_data[chart_data['model_show'].isin(st.session_state.models)]
 
-    st.markdown("""
-                <style>
-                .stMultiSelect [data-baseweb=select] span {
-                    max-width: 800px;
-                    font-size: 0.9rem;
-                    background-color: #3C6478 !important; /* Background color for selected items */
-                    color: white; /* Change text color */
-                    back
-                }
-                </style>
-                """, unsafe_allow_html=True)
-    models = st.multiselect("Please choose the models", chart_data['Model'].tolist(), default = chart_data['Model'].tolist())
-    chart_data = chart_data[chart_data['Model'].isin(models)]
-    
-    if sorted == 'Ascending':
+    if num_sort == 'Ascending':
         ascend = True 
     else:
         ascend = False
     
-    chart_data = chart_data.sort_values(by=['Accuracy'], ascending=ascend)
-    
-    min_value = round(chart_data.iloc[:, 1::].min().min() - 0.1, 1) 
-    max_value = round(chart_data.iloc[:, 1::].max().max() + 0.1, 1) 
-
-    options = {
-        "title": {"text": f"{category_two}"},
-        "tooltip": {
-            "trigger": "axis",
-            "axisPointer": {"type": "cross", "label": {"backgroundColor": "#6a7985"}},
-            "triggerOn": 'mousemove',
-        },
-        "legend": {"data": ['Overall Accuracy']},
-        "toolbox": {"feature": {"saveAsImage": {}}},
-        "grid": {"left": "3%", "right": "4%", "bottom": "3%", "containLabel": True},
-        "xAxis": [
-            {
-                "type": "category",
-                "boundaryGap": True,
-                "triggerEvent": True,
-                "data": chart_data['Model'].tolist(),
-            }
-        ],
-        "yAxis": [{"type": "value", 
-                    "min": min_value,
-                    "max": max_value, 
-                    "boundaryGap": True
-                    # "splitNumber": 10
-                    }],
-        "series": [
-            {
-                "name": "Overall Accuracy",
-                "type": "bar",
-                "data": chart_data['Accuracy'].tolist(),
-            },
-
-        ],
+    chart_data = chart_data.sort_values(by=[sort], ascending=ascend).dropna(axis=0)
+
+    if len(chart_data) == 0:
+        return
+    
+    min_value = round(min(chart_data.iloc[:, 1]) - 0.1*min(chart_data.iloc[:, 1]), 1) 
+    max_value = round(max(chart_data.iloc[:, 1]) + 0.1*max(chart_data.iloc[:, 1]), 1)         
+
+    display_names = {
+        'cross_mmlu': 'Cross-MMLU',
+        'cross_logiqa': 'Cross-LogiQA',
+        'cross_xquad': 'Cross-XQUAD',
+        'sg_eval': 'SG EVAL',
+        'sg_eval_v1_cleaned': 'SG EVAL V1 Cleaned',
+        'sg_eval_v2_mcq': 'SG EVAL V2 MCQ',
+        'sg_eval_v2_open': 'SG EVAL V2 Open Ended',
+        'us_eval': 'US EVAL', 
+        'cn_eval': 'CN EVAL', 
+        'ph_eval': 'PH EVAL'
     }
-    
-    # events = {
-    #     "click": "function(params) { return params.value }"
-    # }
-
-    value = st_echarts(options=options, height="500px")
-    
-    # if value != None:
-    #     # print(value)
-    #     nav_to(value)
-
-    # if value != None:
-    #     highlight_table_line(value)
-
-    ### create table
-    st.divider()
-    # chart_data['Link'] = chart_data['Model'].map(links_dic)
-    st.dataframe(chart_data,
-                #  column_config = {
-                #      "Link": st.column_config.LinkColumn(
-                #          display_text= st.image(huggingface_image)
-                #      ),
-                #  }, 
-                    hide_index = True, 
-                    use_container_width=True)
 
-def draw_flores_translation(category_one, category_two, sorted):
-    folder = "./results/flores_translation/"
-    category_two_dict = {'Indonesian to English': 'ind2eng', 
-                         'Vitenamese to English': 'vie2eng', 
-                         'Chinese to English': 'zho2eng', 
-                         'Malay to English': 'zsm2eng'}
-    
-    subtitle = category_two_dict[category_two]
-    
-    data_path = f'{folder}/{category_one}/{subtitle}.csv'
-    chart_data = pd.read_csv(data_path).round(3)
-    st.markdown("""
-                <style>
-                .stMultiSelect [data-baseweb=select] span {
-                    max-width: 800px;
-                    font-size: 0.9rem;
-                    background-color: #3C6478 !important; /* Background color for selected items */
-                    color: white; /* Change text color */
-                    back
-                }
-              
-                </style>
-                """, unsafe_allow_html=True)
-    models = st.multiselect("Please choose the models", chart_data['Model'].tolist(), default = chart_data['Model'].tolist())
-    chart_data = chart_data[chart_data['Model'].isin(models)]
-    
-    if sorted == 'Ascending':
-        ascend = True 
-    else:
-        ascend = False
-    
-    chart_data = chart_data.sort_values(by=['BLEU'], ascending=ascend)
+    # breakpoint()
+    data_columns = [i for i in chart_data.columns if i not in ['Model', 'model_show']]
 
-    min_value = round(chart_data.iloc[:, 1::].min().min() - 0.1, 1) 
-    max_value = round(chart_data.iloc[:, 1::].max().max() + 0.1, 1) 
-    
     options = {
-        "title": {"text": f"{category_two}"},
+        # "title": {"text": f"{display_names[category_two]}"},
         "tooltip": {
             "trigger": "axis",
             "axisPointer": {"type": "cross", "label": {"backgroundColor": "#6a7985"}},
             "triggerOn": 'mousemove',
         },
-        "legend": {"data": ['BLEU']},
+        "legend": {"data": data_columns},
         "toolbox": {"feature": {"saveAsImage": {}}},
         "grid": {"left": "3%", "right": "4%", "bottom": "3%", "containLabel": True},
         "xAxis": [
@@ -473,7 +80,7 @@ def draw_flores_translation(category_one, category_two, sorted):
                 "type": "category",
                 "boundaryGap": True,
                 "triggerEvent": True,
-                "data": chart_data['Model'].tolist(),
+                "data":  chart_data['model_show'].tolist(),
             }
         ],
         "yAxis": [{"type": "value", 
@@ -482,181 +89,50 @@ def draw_flores_translation(category_one, category_two, sorted):
                     "boundaryGap": True
                     # "splitNumber": 10
                     }],
-        "series": [
-            {
-                "name": "BLEU",
+        "series": [{
+                "name": f"{col}",
                 "type": "bar",
-                "data": chart_data['BLEU'].tolist(),
-            },
-
-        ],
+                "data": chart_data[f'{col}'].tolist(),
+            } for col in data_columns],
     }
     
-    # events = {
-    #     "click": "function(params) { return params.value }"
-    # }
-
-    value = st_echarts(options=options, height="500px")
-    
-    # if value != None:
-    #     # print(value)
-    #     nav_to(value)
-
-
-    ### create table
-    st.divider()
-    # chart_data['Link'] = chart_data['Model'].map(links_dic)
-    st.dataframe(chart_data,
-                #  column_config = {
-                #      "Link": st.column_config.LinkColumn(
-                #          display_text= st.image(huggingface_image)
-                #      ),
-                #  }, 
-                    hide_index = True, 
-                    use_container_width=True)
-    
+    events = {
+        "click": "function(params) { return params.value }"
+    }
 
-def draw_dialogue(category_one, category_two, sort, sorted):
-    folder = "./results/dialogue"
-    category_two_dict = {'DREAM': 'dream', 
-                         'SAMSum': 'samsum', 
-                         'DialogSum': 'dialogsum'}
-    
-    subtitle = category_two_dict[category_two]
-    
-    data_path = f'{folder}/{category_one}/{subtitle}.csv'
-    chart_data = pd.read_csv(data_path).round(3)
+    value = st_echarts(options=options, events=events, height="500px")
+
+    '''
+    Show table
+    '''
+    # st.divider()
+    with st.container():
+        # st.write("")
+        st.markdown('##### TABLE')
+        # custom_css = """
+                   
+        #             """
+        # st.markdown(custom_css, unsafe_allow_html=True)
+        
+        model_link = {key.strip(): val for key, val in zip(info_df['Proper Display Name'], info_df['Link'])}
 
-    st.markdown("""
-                <style>
-                .stMultiSelect [data-baseweb=select] span {
-                    max-width: 800px;
-                    font-size: 0.9rem;
-                    background-color: #3C6478 !important; /* Background color for selected items */
-                    color: white; /* Change text color */
-                    back
-                }
-                </style>
-                """, unsafe_allow_html=True)
-    models = st.multiselect("Please choose the models", chart_data['Model'].tolist(), default = chart_data['Model'].tolist())
-    chart_data = chart_data[chart_data['Model'].isin(models)]
-    
-    if sorted == 'Ascending':
-        ascend = True 
-    else:
-        ascend = False
-    
-    chart_data = chart_data.sort_values(by=[sort], ascending=ascend)
+        chart_data['model_link'] = chart_data['model_show'].map(model_link) 
 
-    min_value = round(chart_data.iloc[:, 1::].min().min() - 0.1, 1) 
-    max_value = round(chart_data.iloc[:, 1::].max().max() + 0.1, 1) 
-    
+        # import pdb
+        # pdb.set_trace()
 
-    options = {}
-    if category_two in ['SAMSum', 'DialogSum']:
-        options = {
-            "title": {"text": f"{category_two}"},
-            "tooltip": {
-                "trigger": "axis",
-                "axisPointer": {"type": "cross", "label": {"backgroundColor": "#6a7985"}},
-                "triggerOn": 'mousemove',
-            },
-            "legend": {"data": list(chart_data.columns)},
-            "toolbox": {"feature": {"saveAsImage": {}}},
-            "grid": {"left": "3%", "right": "4%", "bottom": "3%", "containLabel": True},
-            "xAxis": [
-                {
-                    "type": "category",
-                    "boundaryGap": True,
-                    "triggerEvent": True,
-                    "data": chart_data['Model'].tolist(),
-                }
-            ],
-            "yAxis": [{"type": "value", 
-                        "min": min_value,
-                        "max": max_value, 
-                        "boundaryGap": True
-                        # "splitNumber": 10
-                        }],
-            "series": [
-                {
-                    "name": "Average",
-                    "type": "bar",
-                    "data": chart_data['Average'].tolist(),
-                },
-                {
-                    "name": "ROUGE-1",
-                    "type": "bar",
-                    "data": chart_data["ROUGE-1"].tolist(),
-                },
-                {
-                    "name": "ROUGE-2",
-                    "type": "bar",
-                    "data": chart_data["ROUGE-2"].tolist(),
-                },
-                {
-                    "name": "ROUGE-L",
-                    "type": "bar",
-                    "data": chart_data["ROUGE-L"].tolist(),
-                },
+        chart_data_table = chart_data[['model_show', 'model_link'] + data_columns]
 
-            ],
-        }
-    
-    elif category_two == 'DREAM':
-        options = {
-            "title": {"text": f"{category_two}"},
-            "tooltip": {
-                "trigger": "axis",
-                "axisPointer": {"type": "cross", "label": {"backgroundColor": "#6a7985"}},
-                "triggerOn": 'mousemove',
-            },
-            "legend": {"data": list(chart_data.columns)},
-            "toolbox": {"feature": {"saveAsImage": {}}},
-            "grid": {"left": "3%", "right": "4%", "bottom": "3%", "containLabel": True},
-            "xAxis": [
-                {
-                    "type": "category",
-                    "boundaryGap": True,
-                    "triggerEvent": True,
-                    "data": chart_data['Model'].tolist(),
-                }
-            ],
-            "yAxis": [{"type": "value", 
-                        "min": min_value,
-                        "max": max_value, 
-                        # "splitNumber": 10
-                        "boundaryGap": True
-                        }],
-            "series": [
-                {
-                    "name": "Accuracy",
-                    "type": "bar",
-                    "data": chart_data['Accuracy'].tolist(),
+        st.dataframe(
+                chart_data_table,
+                column_config={
+                    'model_show': 'Model',
+                    chart_data_table.columns[1]: {'alignment': 'center'},
+                    "model_link": st.column_config.LinkColumn(
+                        "Model Link",
+                    ),
                 },
+                hide_index=True,
+                use_container_width=True
+            )
 
-            ],
-        }
-
-    # events = {
-    #     "click": "function(params) { return params.value }"
-    # }
-
-    value = st_echarts(options=options, height="500px")
-    
-    # if value != None:
-    #     # print(value)
-    #     nav_to(value)
-
-
-    ### create table
-    st.divider()
-    # chart_data['Link'] = chart_data['Model'].map(links_dic)
-    st.dataframe(chart_data,
-                #  column_config = {
-                #      "Link": st.column_config.LinkColumn(
-                #          display_text= st.image(huggingface_image)
-                #      ),
-                #  }, 
-                    hide_index = True, 
-                    use_container_width=True)

app/pages.py

@@ -90,15 +90,15 @@ def cross_lingual_consistency():
         sort = st.selectbox('Sort', ['Accuracy','Cross-Lingual Consistency', 'AC3',
                                      'English', 'Chinese', 'Spanish', 'Vietnamese'])
     with right:
-        sorted = st.selectbox('by', ['Ascending', 'Descending'])
+        sortby = st.selectbox('by', ['Ascending', 'Descending'])
 
-    if category_one or category_two or sort or sorted:
+    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, sorted)
-    else:
-        draw_cross_lingual('zero_shot', 'cross_mmlu', 'Accuracy', 'Descending')
+        draw('cross_lingual',category_one, category_two, sort, sortby)
+    # else:
+    #     draw('zero_shot', 'cross_mmlu', 'Accuracy', 'Descending')
 
 def cultural_reasoning():
     st.title("Cultural Reasoning")
@@ -116,6 +116,13 @@ def cultural_reasoning():
     
     category_one_dict = {'Zero Shot': 'zero_shot', 
                          'Few Shot': 'few_shot'}
+    category_two_dict = {'SG EVAL': 'sg_eval',
+                         'SG EVAL V1 Cleaned': 'sg_eval_v1_cleaned',
+                         'SG EVAL V2 MCQ': 'sg_eval_v2_mcq',
+                         'SG EVAL V2 Open Ended': 'sg_eval_v2_open',
+                         'US EVAL': 'us_eval', 
+                         'CN EVAL': 'cn_eval', 
+                         'PH EVAL': 'ph_eval'}
 
     left, center, _, right = st.columns([0.2, 0.2, 0.4, 0.2])
     with left:
@@ -123,13 +130,14 @@ def cultural_reasoning():
     with center:
         category_two = st.selectbox('Select the sub-category', filters_leveltwo)
     with right:
-        sorted = st.selectbox('sorted by', ['Ascending', 'Descending'])
+        sortby = st.selectbox('sorted by', ['Ascending', 'Descending'])
     
-    if category_one or category_two or sorted:
+    if category_one or category_two or sortby:
         category_one = category_one_dict[category_one]
-        draw_only_acc('cultural_reasoning', category_one, category_two, sorted)
-    else:
-        draw_only_acc('cultural_reasoning', 'zero_shot', 'sg_eval', 'Descending')
+        category_two = category_two_dict[category_two]
+        draw('cultural_reasoning', category_one, category_two, 'Accuracy',sortby)
+    # else:
+    #     draw_only_acc('cultural_reasoning', 'zero_shot', 'sg_eval', 'Descending')
 
 
 def general_reasoning():
@@ -146,6 +154,11 @@ def general_reasoning():
     
     category_one_dict = {'Zero Shot': 'zero_shot', 
                          'Few Shot': 'few_shot'}
+    category_two_dict = {'MMLU': 'mmlu', 
+                         'C Eval': 'c_eval', 
+                         'CMMLU': 'cmmlu', 
+                         'ZBench': 'zbench', 
+                         'IndoMMLU': 'indommlu'}
 
     left, center, _, right = st.columns([0.2, 0.2, 0.4, 0.2])
     with left:
@@ -153,13 +166,14 @@ def general_reasoning():
     with center:
         category_two = st.selectbox('Select the sub-category', filters_leveltwo)
     with right:
-        sorted = st.selectbox('sorted by', ['Ascending', 'Descending'])
+        sortby = st.selectbox('sorted by', ['Ascending', 'Descending'])
     
-    if category_one or category_two or sorted:
+    if category_one or category_two or sortby:
         category_one = category_one_dict[category_one]
-        draw_only_acc('general_reasoning', category_one, category_two, sorted)
-    else:
-        draw_only_acc('general_reasoning', 'zero_shot', 'MMLU Full', 'Descending')
+        category_two = category_two_dict[category_two]
+        draw('general_reasoning', category_one, category_two, 'Accuracy',sortby)
+    # else:
+    #     draw_only_acc('general_reasoning', 'zero_shot', 'MMLU Full', 'Descending')
 
 def flores():
     st.title("FLORES-Translation")
@@ -173,6 +187,10 @@ def flores():
     
     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, _, right = st.columns([0.2, 0.2, 0.4, 0.2])
@@ -181,13 +199,14 @@ def flores():
     with center:
         category_two = st.selectbox('Select the sub-category', filters_leveltwo)
     with right:
-        sorted = st.selectbox('sorted by', ['Ascending', 'Descending'])
+        sortby = st.selectbox('sorted by', ['Ascending', 'Descending'])
     
-    if category_one or category_two or sorted:
+    if category_one or category_two or sortby:
         category_one = category_one_dict[category_one]
-        draw_flores_translation(category_one, category_two, sorted)
-    else:
-        draw_flores_translation('zero_shot', 'Indonesian to English', 'Descending')
+        category_two = category_two_dict[category_two]
+        draw('flores_translation', category_one, category_two, 'BLEU',sortby)
+    # else:
+    #     draw_flores_translation('zero_shot', 'Indonesian to English', 'Descending')
 
 def emotion():
     st.title("Emotion")
@@ -200,6 +219,8 @@ def emotion():
     
     category_one_dict = {'Zero Shot': 'zero_shot', 
                          'Few Shot': 'few_shot'}
+    category_two_dict = {'Indonesian Emotion  Classification': 'ind_emotion', 
+                             'SST2': 'sst2'}
 
     left, center, _, right = st.columns([0.2, 0.2, 0.4, 0.2])
     with left:
@@ -207,13 +228,14 @@ def emotion():
     with center:
         category_two = st.selectbox('Select the sub-category', filters_leveltwo)
     with right:
-        sorted = st.selectbox('sorted by', ['Ascending', 'Descending'])
+        sortby = st.selectbox('sorted by', ['Ascending', 'Descending'])
     
-    if category_one or category_two or sorted:
+    if category_one or category_two or sortby:
         category_one = category_one_dict[category_one]
-        draw_only_acc('emotion', category_one, category_two, sorted)
-    else:
-        draw_only_acc('emotion', 'zero_shot', 'Indonesian Emotion  Classification', 'Descending')
+        category_two = category_two_dict[category_two]
+        draw('emotion', category_one, category_two, 'Accuracy', sortby)
+    # else:
+    #     draw_only_acc('emotion', 'zero_shot', 'Indonesian Emotion  Classification', 'Descending')
 
 def dialogue():
     st.title("Dialogue")
@@ -227,6 +249,9 @@ def dialogue():
     
     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:
@@ -240,13 +265,14 @@ def dialogue():
             sort = st.selectbox('Sort', ['Average', 'ROUGE-1', 'ROUGE-2', 'ROUGE-L'])
     
     with right:
-        sorted = st.selectbox('by', ['Ascending', 'Descending'])
+        sortby = st.selectbox('by', ['Ascending', 'Descending'])
     
-    if category_one or category_two or sort or sorted:
+    if category_one or category_two or sort or sortby:
         category_one = category_one_dict[category_one]
-        draw_dialogue(category_one, category_two, sort, sorted)
-    else:
-        draw_dialogue('zero_shot', 'DREAM', sort[0],'Descending')
+        category_two = category_two_dict[category_two]
+        draw('dialogue', category_one, category_two, sort, sortby)
+    # else:
+    #     draw_dialogue('zero_shot', 'DREAM', sort[0],'Descending')
 
 def fundamental_nlp_tasks():
     st.title("Fundamental NLP Tasks")
@@ -256,6 +282,15 @@ def fundamental_nlp_tasks():
     
     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, _, right = st.columns([0.2, 0.2, 0.4, 0.2])
     with left:
@@ -263,10 +298,11 @@ def fundamental_nlp_tasks():
     with center:
         category_two = st.selectbox('Select the sub-category', filters_leveltwo)
     with right:
-        sorted = st.selectbox('sorted by', ['Ascending', 'Descending'])
+        sortby = st.selectbox('sorted by', ['Ascending', 'Descending'])
     
-    if category_one or category_two or sorted:
+    if category_one or category_two or sortby:
         category_one = category_one_dict[category_one]
-        draw_only_acc('fundamental_nlp_tasks', category_one, category_two, sorted)
-    else:
-        draw_only_acc('fundamental_nlp_tasks', 'zero_shot', 'OCNLI', 'Descending')
+        category_two = category_two_dict[category_two]
+        draw('fundamental_nlp_tasks', category_one, category_two, 'Accuracy', sortby)
+    # else:
+    #     draw_only_acc('fundamental_nlp_tasks', 'zero_shot', 'OCNLI', 'Descending')