feature: add auto evaluation tab

app.py.bak

@@ -0,0 +1,95 @@
+from apscheduler.schedulers.background import BackgroundScheduler
+import gradio as gr
+import pandas as pd
+
+# Load leaderboard data
+leaderboard_df = pd.read_csv('leaderboard.csv', header=None)
+print (leaderboard_df)
+leaderboard_auto_df = pd.read_csv('leaderboard_auto.csv', header=None)
+
+# 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")
+]
+
+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")
+]
+
+TYPES = [col_type for _, col_type in COLS]
+TYPES_AUTO = [col_type for _, col_type in COLS_AUTO]
+
+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")
+
+        # Create tabs for self-hosted and automatic evaluation
+        with gr.Tab("Self-hosted evaluation"):
+            lt = create_leaderboard_table(leaderboard_df, COLS, TYPES)
+        with gr.Tab("Automatic evaluation"):
+            lat = create_leaderboard_table(leaderboard_auto_df, COLS_AUTO, TYPES_AUTO)
+
+    demo.launch()
+
+scheduler = BackgroundScheduler()
+scheduler.add_job(launch_gradio, "interval", seconds=3600)
+scheduler.start()
+
+# Launch immediately
+launch_gradio()