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| import json | |
| import gradio as gr | |
| import pandas as pd | |
| from css_html import custom_css | |
| from text_content import ABOUT_TEXT, CITATION_BUTTON_TEXT, CITATION_BUTTON_LABEL, ACKNOWLEDGEMENT_TEXT, NOTES_TEXT, HEAD_TEXT | |
| from utils import ( | |
| AutoEvalColumn, | |
| fields, | |
| lang_map, | |
| ) | |
| result_path = './results.json' | |
| task_type = ["input reasoning", "output reasoning"] | |
| cur_task = "input" | |
| next_task = "output" | |
| with open(result_path, 'r') as f: | |
| data = json.load(f) | |
| rows = [] | |
| for model_name, sub_col in data.items(): | |
| row = {} | |
| for lang in sub_col["pass@1"]: | |
| if cur_task in lang: | |
| row[lang_map[lang.replace(f"_{cur_task}", "")]] = sub_col["pass@1"][lang] | |
| row['Average'] = sum(row.values()) / len(row.values()) | |
| row['Average'] = round(row['Average'], 1) | |
| row['Model'] = model_name | |
| row['Size'] = sub_col['size'] | |
| rows.append(row) | |
| df = pd.DataFrame(rows) | |
| df = df.sort_values(by='Average', ascending=False) | |
| rows = [] | |
| for model_name, sub_col in data.items(): | |
| row = {} | |
| for lang in sub_col["pass@1"]: | |
| if next_task in lang: | |
| row[lang_map[lang.replace(f"_{next_task}", "")]] = sub_col["pass@1"][lang] | |
| row['Average'] = sum(row.values()) / len(row.values()) | |
| row['Average'] = round(row['Average'], 1) | |
| row['Model'] = model_name | |
| row['Size'] = sub_col['size'] | |
| rows.append(row) | |
| df_next = pd.DataFrame(rows) | |
| df_next = df_next.sort_values(by='Average', ascending=False) | |
| COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden] | |
| TYPES = [c.type for c in fields(AutoEvalColumn) if not c.hidden] | |
| COLS_LITE = [ | |
| c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden | |
| ] | |
| TYPES_LITE = [ | |
| c.type for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden | |
| ] | |
| def select_columns(df, columns): | |
| always_here_cols = [ | |
| AutoEvalColumn.model.name, | |
| AutoEvalColumn.size.name, | |
| ] | |
| # We use COLS to maintain sorting | |
| filtered_df = df[ | |
| always_here_cols + [c for c in COLS if c in df.columns and c in columns] | |
| ] | |
| return filtered_df | |
| def select_tasks(df, columns, df_next): | |
| always_here_cols = [ | |
| AutoEvalColumn.model.name, | |
| AutoEvalColumn.size.name, | |
| ] | |
| df,df_next = df_next,df | |
| filtered_df = df[ | |
| always_here_cols + [c for c in COLS if c in df.columns and c in columns] | |
| ] | |
| return df,filtered_df,df_next | |
| demo = gr.Blocks(css=custom_css) | |
| with demo: | |
| with gr.Column(): | |
| gr.Markdown( | |
| """<div style="text-align: center;"><h1>CRUXEVAL-X Leaderboard</h1></div>\ | |
| <br>\ | |
| """, | |
| elem_classes="markdown-text", | |
| ) | |
| gr.Markdown(HEAD_TEXT, elem_classes="markdown-text") | |
| with gr.Tabs(elem_classes="tab-buttons") as tabs: | |
| with gr.Column(): | |
| with gr.Tabs(elem_classes="A100-tabs") as A100_tabs: | |
| with gr.TabItem("π Evaluation Table", id=0): | |
| with gr.Column(): | |
| with gr.Accordion("β¬ Tasks", open=True): | |
| shown_tasks = gr.Radio( | |
| choices=[ | |
| c | |
| for c in task_type | |
| ], | |
| value=[ | |
| c | |
| for c in task_type | |
| if cur_task in c | |
| ][0] if any(cur_task in c for c in task_type) else None, | |
| label="", | |
| elem_id="task-select", | |
| interactive=True, | |
| ) | |
| with gr.Accordion("β¬ Languages", open=True): | |
| shown_languages = gr.CheckboxGroup( | |
| choices=[ | |
| c | |
| for c in COLS | |
| if c | |
| not in [ | |
| AutoEvalColumn.model.name, | |
| AutoEvalColumn.size.name | |
| ] | |
| ], | |
| value=[ | |
| c | |
| for c in COLS_LITE | |
| if c | |
| not in [ | |
| AutoEvalColumn.model.name, | |
| AutoEvalColumn.size.name | |
| ] | |
| ], | |
| label="", | |
| elem_id="column-select", | |
| interactive=True, | |
| ) | |
| leaderboard_df = gr.components.Dataframe( | |
| value=df[ | |
| [ | |
| AutoEvalColumn.model.name, | |
| AutoEvalColumn.size.name, | |
| ] | |
| + shown_languages.value | |
| ], | |
| headers=COLS, | |
| datatype=TYPES, | |
| elem_id="leaderboard-table", | |
| interactive=False, | |
| ) | |
| hidden_leaderboard_df = gr.components.Dataframe( | |
| value=df, | |
| headers=COLS, | |
| datatype=["str" for _ in range(len(COLS))], | |
| visible=False, | |
| ) | |
| leaderboard_next = gr.components.Dataframe( | |
| value=df_next, | |
| headers=COLS, | |
| datatype=["str" for _ in range(len(COLS))], | |
| visible=False, | |
| ) | |
| shown_languages.change( | |
| select_columns, | |
| [hidden_leaderboard_df, shown_languages], | |
| leaderboard_df, | |
| ) | |
| shown_tasks.change( | |
| select_tasks, | |
| [hidden_leaderboard_df, shown_languages, leaderboard_next], | |
| [hidden_leaderboard_df, leaderboard_df, leaderboard_next], | |
| ) | |
| gr.Markdown(NOTES_TEXT, elem_classes="markdown-text") | |
| with gr.TabItem("π About", id=1): | |
| gr.Markdown(ABOUT_TEXT, elem_classes="markdown-text") | |
| with gr.Row(): | |
| with gr.Accordion("π Citation", open=False): | |
| citation_button = gr.Textbox( | |
| value=CITATION_BUTTON_TEXT, | |
| label=CITATION_BUTTON_LABEL, | |
| lines=10, | |
| elem_id="citation-button", | |
| show_copy_button=True, | |
| ) | |
| with gr.Row(): | |
| with gr.Accordion("π Acknowledgement", open=False): | |
| gr.Markdown(ACKNOWLEDGEMENT_TEXT) | |
| demo.launch() |