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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()
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