Create app.py
Browse files
app.py
ADDED
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import gradio as gr
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from utils import MEGABenchEvalDataLoader
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import os
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from constants import *
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# Get the directory of the current script
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current_dir = os.path.dirname(os.path.abspath(__file__))
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# Construct paths to CSS files
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base_css_file = os.path.join(current_dir, "static", "css", "style.css")
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table_css_file = os.path.join(current_dir, "static", "css", "table.css")
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# Read CSS files
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with open(base_css_file, "r") as f:
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base_css = f.read()
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with open(table_css_file, "r") as f:
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table_css = f.read()
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# Initialize data loaders
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default_loader = MEGABenchEvalDataLoader("./static/eval_results/Default")
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si_loader = MEGABenchEvalDataLoader("./static/eval_results/SI")
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with gr.Blocks() as block:
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# Add a style element that we'll update
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css_style = gr.HTML(
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f"<style>{base_css}\n{table_css}</style>",
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visible=False
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)
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with gr.Tabs(elem_classes="tab-buttons") as tabs:
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with gr.TabItem("📚 Introduction", elem_id="intro-tab", id=0):
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gr.Markdown(
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LEADERBOARD_INTRODUCTION
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)
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with gr.TabItem("📊 MEGA-Bench", elem_id="qa-tab-table1", id=1):
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with gr.Row():
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with gr.Accordion("Citation", open=False):
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citation_button = gr.Textbox(
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value=CITATION_BUTTON_TEXT,
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label=CITATION_BUTTON_LABEL,
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elem_id="citation-button",
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lines=10,
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)
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gr.Markdown(
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TABLE_INTRODUCTION
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)
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with gr.Row():
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table_selector = gr.Radio(
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choices=["Default", "Single Image"],
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label="Select table to display. Default: all MEGA-Bench tasks; Single Image: single-image tasks only.",
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value="Default"
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)
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# Define different captions for each table
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default_caption = "**Table 1: MEGA-Bench full results.** The number in the parentheses is the number of tasks of each keyword. <br> The Core set contains $N_{\\text{core}} = 440$ tasks evaluated by rule-based metrics, and the Open-ended set contains $N_{\\text{open}} = 65$ tasks evaluated by a VLM judge (we use GPT-4o-0806). <br> Different from the results in our paper, we only use the Core results with CoT prompting here for clarity and compatibility with the released data. <br> $\\text{Overall} \\ = \\ \\frac{\\text{Core} \\ \\cdot \\ N_{\\text{core}} \\ + \\ \\text{Open-ended} \\ \\cdot \\ N_{\\text{open}}}{N_{\\text{core}} \\ + \\ N_{\\text{open}}}$ <br> * indicates self-reported results from the model authors."
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single_image_caption = "**Table 2: MEGA-Bench Single-image setting results.** The number in the parentheses is the number of tasks in each keyword. <br> This subset contains 273 single-image tasks from the Core set and 42 single-image tasks from the Open-ended set. For open-source models, we drop the image input in the 1-shot demonstration example so that the entire query contains a single image only. <br> Compared to the default table, some models with only single-image support are added."
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caption_component = gr.Markdown(
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value=default_caption,
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elem_classes="table-caption",
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latex_delimiters=[{"left": "$", "right": "$", "display": False}],
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)
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with gr.Row():
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super_group_selector = gr.Radio(
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choices=list(default_loader.SUPER_GROUPS.keys()),
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label="Select a dimension to display breakdown results. We use different column colors to distinguish the overall benchmark scores and breakdown results.",
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value=list(default_loader.SUPER_GROUPS.keys())[0]
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)
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model_group_selector = gr.Radio(
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choices=list(BASE_MODEL_GROUPS.keys()),
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label="Select a model group",
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value="All"
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)
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initial_headers, initial_data = default_loader.get_leaderboard_data(list(default_loader.SUPER_GROUPS.keys())[0], "All")
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data_component = gr.Dataframe(
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value=initial_data,
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headers=initial_headers,
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datatype=["number", "html"] + ["number"] * (len(initial_headers) - 2),
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interactive=False,
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elem_classes="custom-dataframe",
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max_height=2400,
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column_widths=["100px", "240px"] + ["160px"] * 3 + ["210px"] * (len(initial_headers) - 5),
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)
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def update_table_and_caption(table_type, super_group, model_group):
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if table_type == "Default":
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headers, data = default_loader.get_leaderboard_data(super_group, model_group)
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caption = default_caption
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else: # Single-image
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headers, data = si_loader.get_leaderboard_data(super_group, model_group)
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caption = single_image_caption
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return [
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gr.Dataframe(
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value=data,
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headers=headers,
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datatype=["number", "html"] + ["number"] * (len(headers) - 2),
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interactive=False,
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column_widths=["100px", "240px"] + ["160px"] * 3 + ["210px"] * (len(headers) - 5),
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),
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caption,
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f"<style>{base_css}\n{table_css}</style>"
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]
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def update_selectors(table_type):
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loader = default_loader if table_type == "Default" else si_loader
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return [
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gr.Radio(choices=list(loader.SUPER_GROUPS.keys())),
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gr.Radio(choices=list(loader.MODEL_GROUPS.keys()))
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]
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refresh_button = gr.Button("Refresh")
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# Update click and change handlers to include caption updates
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refresh_button.click(
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fn=update_table_and_caption,
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inputs=[table_selector, super_group_selector, model_group_selector],
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outputs=[data_component, caption_component, css_style]
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)
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super_group_selector.change(
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fn=update_table_and_caption,
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inputs=[table_selector, super_group_selector, model_group_selector],
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outputs=[data_component, caption_component, css_style]
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)
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model_group_selector.change(
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fn=update_table_and_caption,
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inputs=[table_selector, super_group_selector, model_group_selector],
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outputs=[data_component, caption_component, css_style]
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)
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table_selector.change(
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fn=update_selectors,
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inputs=[table_selector],
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outputs=[super_group_selector, model_group_selector]
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).then(
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fn=update_table_and_caption,
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inputs=[table_selector, super_group_selector, model_group_selector],
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outputs=[data_component, caption_component, css_style]
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)
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+
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with gr.TabItem("📝 Data Information", elem_id="qa-tab-table2", id=2):
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gr.Markdown(DATA_INFO, elem_classes="markdown-text")
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+
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with gr.TabItem("🚀 Submit", elem_id="submit-tab", id=3):
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with gr.Row():
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gr.Markdown(SUBMIT_INTRODUCTION, elem_classes="markdown-text")
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if __name__ == "__main__":
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block.launch(share=True, show_api=False)
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