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# import gradio as gr
# from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
# import pandas as pd
# from apscheduler.schedulers.background import BackgroundScheduler
# from huggingface_hub import snapshot_download
# from src.about import (
# CITATION_BUTTON_LABEL,
# CITATION_BUTTON_TEXT,
# EVALUATION_QUEUE_TEXT,
# INTRODUCTION_TEXT,
# LLM_BENCHMARKS_TEXT,
# TITLE,
# )
# from src.display.css_html_js import custom_css
# from src.display.utils import (
# BENCHMARK_COLS,
# COLS,
# EVAL_COLS,
# EVAL_TYPES,
# AutoEvalColumn,
# ModelType,
# fields,
# WeightType,
# Precision
# )
# from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
# from src.populate import get_evaluation_queue_df, get_leaderboard_df
# from src.submission.submit import add_new_eval
# def restart_space():
# API.restart_space(repo_id=REPO_ID)
# ### Space initialization
# try:
# snapshot_download(
# repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
# )
# except Exception:
# restart_space()
# try:
# snapshot_download(
# repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
# )
# except Exception:
# restart_space()
# # Prepare your DataFrame
# LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
# # Initialize DataFrames for evaluation queues
# finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
# def init_leaderboard(dataframe):
# if dataframe is None or dataframe.empty:
# raise ValueError("Leaderboard DataFrame is empty or None.")
# return Leaderboard(
# value=dataframe,
# datatype=[c.type for c in fields(AutoEvalColumn)],
# select_columns=SelectColumns(
# default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
# cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
# label="Select Columns to Display:",
# ),
# search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
# hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
# filter_columns=[
# ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
# ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
# ColumnFilter(
# AutoEvalColumn.params.name,
# type="slider",
# min=0.01,
# max=150,
# label="Select the number of parameters (B)",
# ),
# ColumnFilter(
# AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True
# ),
# ],
# bool_checkboxgroup_label="Hide models",
# interactive=False,
# )
# # Start Gradio interface
# demo = gr.Blocks(css=custom_css)
# with demo:
# gr.HTML(TITLE)
# gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
# with gr.Tabs(elem_classes="tab-buttons") as tabs:
# with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
# leaderboard = init_leaderboard(LEADERBOARD_DF) # Use the prepared DataFrame
# gr.Row().update(leaderboard) # Ensure the leaderboard is included
# with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
# gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
# with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3):
# with gr.Column():
# with gr.Row():
# gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
# with gr.Column():
# with gr.Accordion(
# f"✅ Finished Evaluations ({len(finished_eval_queue_df)})",
# open=False,
# ):
# with gr.Row():
# finished_eval_table = gr.components.Dataframe(
# value=finished_eval_queue_df,
# headers=EVAL_COLS,
# datatype=EVAL_TYPES,
# row_count=5,
# )
# with gr.Accordion(
# f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})",
# open=False,
# ):
# with gr.Row():
# running_eval_table = gr.components.Dataframe(
# value=running_eval_queue_df,
# headers=EVAL_COLS,
# datatype=EVAL_TYPES,
# row_count=5,
# )
# with gr.Accordion(
# f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})",
# open=False,
# ):
# with gr.Row():
# pending_eval_table = gr.components.Dataframe(
# value=pending_eval_queue_df,
# headers=EVAL_COLS,
# datatype=EVAL_TYPES,
# row_count=5,
# )
# with gr.Row():
# gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")
# with gr.Row():
# with gr.Column():
# model_name_textbox = gr.Textbox(label="Model name")
# revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
# model_type = gr.Dropdown(
# choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
# label="Model type",
# multiselect=False,
# value=None,
# interactive=True,
# )
# with gr.Column():
# precision = gr.Dropdown(
# choices=[i.value.name for i in Precision if i != Precision.Unknown],
# label="Precision",
# multiselect=False,
# value="float16",
# interactive=True,
# )
# weight_type = gr.Dropdown(
# choices=[i.value.name for i in WeightType],
# label="Weights type",
# multiselect=False,
# value="Original",
# interactive=True,
# )
# base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")
# submit_button = gr.Button("Submit Eval")
# submission_result = gr.Markdown()
# submit_button.click(
# add_new_eval,
# [
# model_name_textbox,
# base_model_name_textbox,
# revision_name_textbox,
# precision,
# weight_type,
# model_type,
# ],
# submission_result,
# )
# with gr.Row():
# with gr.Accordion("📙 Citation", open=False):
# citation_button = gr.Textbox(
# value=CITATION_BUTTON_TEXT,
# label=CITATION_BUTTON_LABEL,
# lines=20,
# elem_id="citation-button",
# show_copy_button=True,
# )
# scheduler = BackgroundScheduler()
# scheduler.add_job(restart_space, "interval", seconds=1800)
# scheduler.start()
# demo.queue(default_concurrency_limit=40).launch()
import gradio as gr
import pandas as pd
from tqdm import tqdm
# Parameters
models = ["modelA", "modelB", "modelC"] # Replace with your actual models
dataset = "my_dataset" # Replace with your actual dataset name
ROUNDS = 3 # Number of rounds
# Load and concatenate data
data = []
for model in tqdm(models):
model_name = model.replace("/", "_")
for i in range(ROUNDS):
try:
df = pd.read_pickle(f"./results/tagged/{dataset}_{model_name}_{i}.pkl")[["Category", "Sub-Category", "model", "round", "tag"]]
data.append(df)
except Exception as e:
print(f"skipping {dataset}_{model_name}_{i}")
raw_data = pd.concat(data)
# Gradio Interface
with gr.Blocks() as demo:
gr.Markdown("# Aggregated Benchmark Results")
gr.DataFrame(value=raw_data, label="Benchmark Table", interactive=False) # Display the DataFrame
# Launch the Gradio app
demo.launch()
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