edit upload logic

app.py

@@ -64,8 +64,6 @@ def init_leaderboard(dataframe, track):
     if dataframe is None or dataframe.empty:
         raise ValueError("Leaderboard DataFrame is empty or None.")
     # filter for correct track
-    print(dataframe["Track"])
-    print(track)
     dataframe = dataframe.loc[dataframe["Track"] == track]
     return Leaderboard(
         value=dataframe,
@@ -97,7 +95,8 @@ def process_json(temp_file):
     except Exception as e:
         raise gr.Error(f"Error processing file: {str(e)}")
 
-    return data, data   # two: one for state (passed to data handler), one for display
+    gr.Markdown("Upload successful!")
+    return data
 
 
 demo = gr.Blocks(css=custom_css)
@@ -116,7 +115,7 @@ with demo:
         with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=4):
             gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
 
-        with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=5):
+        with gr.TabItem("👶 Submit", elem_id="llm-benchmark-tab-table", id=5):
             with gr.Column():
                 with gr.Row():
                     gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
@@ -174,11 +173,10 @@ with demo:
 
                     predictions_data = gr.State()
                     upload_button = gr.UploadButton(label="Upload predictions", file_types=[".json", ".gz"], file_count="single")
-                    json_display = gr.JSON()
                     upload_button.upload(
                         fn=process_json,
                         inputs=upload_button,
-                        outputs=(predictions_data, json_display),
+                        outputs=predictions_data,
                         api_name="upload_json"
                     )
                 

src/submission/submit.py

@@ -28,7 +28,6 @@ def add_new_eval(
         REQUESTED_MODELS, USERS_TO_SUBMISSION_DATES = already_submitted_models(EVAL_REQUESTS_PATH)
 
     out_message = ""
-    print(predictions)  # debugging
 
     user_name = ""
     model_path = model_name