remove prints

app.py

@@ -103,41 +103,41 @@ with demo:
                 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.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("# ✉️✨ Queue your model here!", elem_classes="markdown-text")
 

src/populate.py

@@ -11,7 +11,9 @@ from src.leaderboard.read_evals import get_raw_eval_results
 def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
     """Creates a dataframe from all the individual experiment results"""
     raw_data = get_raw_eval_results(results_path, requests_path)
-    all_data_json = [v.to_dict() for v in raw_data]
+    mina = min([a.results["mpcc_delta"] for a in raw_data.values()])
+    maxa = max([a.results["mpcc_delta"] for a in raw_data.values()])
+    all_data_json = [v.to_dict(mina, maxa) for v in raw_data]
 
     df = pd.DataFrame.from_records(all_data_json)
     df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)