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from apscheduler.schedulers.background import BackgroundScheduler | |
import gradio as gr | |
import pandas as pd | |
# Load leaderboard data | |
leaderboard_df = pd.read_csv('leaderboard.csv') | |
# Constants | |
TITLE = "Financial Natural Language Understanding and Prediction Evaluation Benchmark (FLARE) Leaderboard" | |
INTRODUCTION_TEXT = "The leaderboard shows the performance of various models in financial natural language understanding and prediction tasks." | |
COLS = [ | |
("Model", "str"), | |
("FPB-acc", "number"), | |
("FPB-F1", "number"), | |
("FiQA-SA-F1", "number"), | |
("Headline-AvgF1", "number"), | |
("NER-EntityF1", "number"), | |
("FinQA-EmAcc", "number"), | |
("ConvFinQA-EmAcc", "number"), | |
("BigData22-Acc", "number"), | |
("BigData22-MCC", "number"), | |
("ACL18-Acc", "number"), | |
("ACL18-MCC", "number"), | |
("CIKM18-Acc", "number"), | |
("CIKM18-MCC", "number") | |
] | |
TYPES = [col_type for _, col_type in COLS] | |
def launch_gradio(): | |
demo = gr.Blocks() | |
with demo: | |
gr.HTML(TITLE) | |
gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text") | |
# Create a gradio table from pandas dataframe | |
leaderboard_table = gr.components.Dataframe( | |
value=leaderboard_df.values.tolist(), | |
headers=[col_name for col_name, _ in COLS], | |
datatype=TYPES, | |
max_rows=5, | |
elem_id="leaderboard-table", | |
) | |
demo.launch() | |
scheduler = BackgroundScheduler() | |
scheduler.add_job(launch_gradio, "interval", seconds=3600) | |
scheduler.start() | |
# Launch immediately | |
launch_gradio() | |