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Update app.py
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app.py
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import json
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import os
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from datetime import datetime, timezone
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import gradio as gr
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import numpy as np
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import pandas as pd
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from apscheduler.schedulers.background import BackgroundScheduler
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from huggingface_hub import HfApi
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from transformers import AutoConfig
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from src.auto_leaderboard.get_model_metadata import apply_metadata
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from src.assets.text_content import *
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from src.
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from src.assets.hardcoded_evals import gpt4_values, gpt35_values, baseline
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from src.assets.css_html_js import custom_css, get_window_url_params
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from src.utils_display import AutoEvalColumn, EvalQueueColumn, fields, styled_error, styled_warning, styled_message
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from src.init import get_all_requested_models, load_all_info_from_hub
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def get_leaderboard_df():
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data = {
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'Datasets': ['metrics','SOTA(FT)', 'SOTA(ZS)', 'FLAN-T5', '
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'KQApro': ['Acc','93.85', '94.20', '37.27', '38.28', '38.01', '40.35', '47.93', '57.20'],
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'LC-quad2': ['F1','33.10', '-', '30.14', '33.04', '33.77', '39.04', '42.76', '54.95'],
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'WQSP': ['Acc','73.10', '62.98', '59.87', '67.68', '72.34', '79.60', '83.70', '90.45'],
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return df
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original_df = get_leaderboard_df()
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leaderboard_df = original_df.copy()
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def search_table(df, query):
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return df
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else:
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return df[df.apply(lambda row: query.lower() in row.astype(str).lower(), axis=1).any()]
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demo = gr.Blocks(css=custom_css)
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with demo:
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gr.HTML(TITLE)
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)
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with gr.Tabs(elem_classes="tab-buttons") as tabs:
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with gr.TabItem("π
LLM Benchmark", elem_id="llm-benchmark-tab-table", id=1):
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leaderboard_table = gr.components.Dataframe(
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value=leaderboard_df,
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with gr.TabItem("About", elem_id="llm-benchmark-tab-table", id=2):
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gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
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with gr.Row():
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with gr.Accordion("π Citation", open=False):
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citation_button = gr.Textbox(
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elem_id="citation-button",
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).style(show_copy_button=True)
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demo.queue(concurrency_count=40).launch()
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import gradio as gr
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import pandas as pd
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from src.assets.text_content import *
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from src.assets.css_html_js import custom_css
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def get_leaderboard_df():
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data = {
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'Datasets': ['metrics','SOTA(FT)', 'SOTA(ZS)', 'FLAN-T5-XXL', 'text-davinci-001', 'text-davinci-002', 'text-davinci-003', 'ChatGPT', 'GPT-4'],
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'KQApro': ['Acc','93.85', '94.20', '37.27', '38.28', '38.01', '40.35', '47.93', '57.20'],
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'LC-quad2': ['F1','33.10', '-', '30.14', '33.04', '33.77', '39.04', '42.76', '54.95'],
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'WQSP': ['Acc','73.10', '62.98', '59.87', '67.68', '72.34', '79.60', '83.70', '90.45'],
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return df
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def search_table(df, query):
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return df[df.apply(lambda row: row.astype(str).str.contains(query).any(), axis=1)]
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original_df = get_leaderboard_df()
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leaderboard_df = original_df.copy()
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demo = gr.Blocks(css=custom_css)
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with demo:
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gr.HTML(TITLE)
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)
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with gr.Tabs(elem_classes="tab-buttons") as tabs:
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with gr.TabItem("π
LLM Benchmark", elem_id="llm-benchmark-tab-table", id=1):
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leaderboard_table = gr.components.Dataframe(
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value=leaderboard_df,
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with gr.TabItem("About", elem_id="llm-benchmark-tab-table", id=2):
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gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
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with gr.Row():
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with gr.Accordion("π Citation", open=False):
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citation_button = gr.Textbox(
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elem_id="citation-button",
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).style(show_copy_button=True)
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demo.queue(concurrency_count=40).launch()
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