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import pandas as pd | |
import gradio as gr | |
from apscheduler.schedulers.background import BackgroundScheduler | |
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") | |
] | |
COLS_AUTO = [ | |
("Model", "str"), | |
("FPB-acc", "number"), | |
("FPB-F1", "number"), | |
("FPB-missing", "number"), | |
("FiQA-SA-F1", "number"), | |
("FiQA-SA-missing", "number"), | |
("Headline-AvgF1", "number"), | |
("NER-EntityF1", "number"), | |
("FinQA-EmAcc", "number"), | |
("BigData22-Acc", "number"), | |
("BigData22-MCC", "number"), | |
("BigData22-missing", "number"), | |
("ACL18-Acc", "number"), | |
("ACL18-MCC", "number"), | |
("ACL18-missing", "number"), | |
("CIKM18-Acc", "number"), | |
("CIKM18-MCC", "number"), | |
("CIKM18-missing", "number"), | |
("FOMC-acc", "number"), | |
("FOMC-F1", "number"), | |
("FOMC-missing", "number"), | |
("FinerOrd-EntityF1", "number"), | |
("FinerOrd-F1", "number"), | |
("German-Acc", "number"), | |
("German-MCC", "number"), | |
("German-missing", "number"), | |
("Australian-Acc", "number"), | |
("Australian-MCC", "number"), | |
("Australian-missing", "number"), | |
("TSA-RMSE", "number"), | |
("TSA-missing", "number") | |
] | |
TYPES = [col_type for _, col_type in COLS] | |
TYPES_AUTO = [col_type for _, col_type in COLS_AUTO] | |
# Extract column names | |
cols = [col_name for col_name, _ in COLS] | |
cols_auto = [col_name for col_name, _ in COLS_AUTO] | |
# Load leaderboard data with column names | |
leaderboard_df = pd.read_csv('leaderboard.csv', names=cols) | |
leaderboard_auto_df = pd.read_csv('leaderboard_auto.csv', names=cols_auto) | |
common_cols = list(set(cols) & set(cols_auto)) | |
# Merge dataframes and replace NaN values with an empty string | |
merged_df = pd.merge( | |
leaderboard_df, leaderboard_auto_df, how="outer", on=common_cols).fillna("") | |
merged_df = merged_df.sort_index(axis=1) | |
# Move 'key' column to the front | |
merged_df = merged_df[ ['Model'] + [ col for col in merged_df.columns if col != 'Model' ] ] | |
merged_cols = merged_df.columns | |
merged_types = ["str"] + ["number"] * (len(merged_cols)-1) | |
# 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." | |
def create_leaderboard_table(df, headers, types): | |
return gr.components.Dataframe( | |
value=df.values.tolist(), | |
headers=[col_name for col_name in headers], | |
datatype=types, | |
max_rows=10, | |
) | |
def launch_gradio(): | |
demo = gr.Blocks() | |
with demo: | |
gr.HTML(TITLE) | |
gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text") | |
lt = create_leaderboard_table(merged_df, merged_cols, merged_types) | |
demo.launch() | |
scheduler = BackgroundScheduler() | |
scheduler.add_job(launch_gradio, "interval", seconds=3600) | |
scheduler.start() | |
# Launch immediately | |
launch_gradio() | |