Update app.py

app.py

@@ -10,78 +10,59 @@ LAST_UPDATED = "OCT 2nd 2024"
 
 column_names = {
     "MODEL": "Model",
-    "Avg. WER": "Average WER ⬇️",    
-    "Avg. RTFx": "RTFx ⬆️️",
-    "AMI WER": "AMI",
-    "Earnings22 WER": "Earnings22",
-    "Gigaspeech WER": "Gigaspeech",
-    "LS Clean WER": "LS Clean",
-    "LS Other WER": "LS Other",
-    "SPGISpeech WER": "SPGISpeech",
-    "Tedlium WER": "Tedlium",
-    "Voxpopuli WER": "Voxpopuli",
+    "WER": "WER ⬇️",
+    "CER": "CER ⬇️",
 }
 
+# Load evaluation results
 eval_queue_repo, requested_models, csv_results = load_all_info_from_dataset_hub()
 
 if not csv_results.exists():
     raise Exception(f"CSV file {csv_results} does not exist locally")
-    
-# Get csv with data and parse columns
+
+# Read CSV with data and parse columns
 original_df = pd.read_csv(csv_results)
 
-# Formats the columns
+# Format the columns
 def formatter(x):
     if type(x) is str:
-        x = x
-    else: 
-        x = round(x, 2)
-    return x
+        return x
+    else:
+        return round(x, 2)
 
 for col in original_df.columns:
     if col == "model":
         original_df[col] = original_df[col].apply(lambda x: x.replace(x, make_clickable_model(x)))
     else:
-        original_df[col] = original_df[col].apply(formatter) # For numerical values
- 
+        original_df[col] = original_df[col].apply(formatter)
+
 original_df.rename(columns=column_names, inplace=True)
-original_df.sort_values(by='Average WER ⬇️', inplace=True)
+original_df.sort_values(by='WER ⬇️', inplace=True)
 
 COLS = [c.name for c in fields(AutoEvalColumn)]
 TYPES = [c.type for c in fields(AutoEvalColumn)]
 
-
-def request_model(model_text, chbcoco2017):
-    
-    # Determine the selected checkboxes
-    dataset_selection = []
-    if chbcoco2017:
-        dataset_selection.append("ESB Datasets tests only")
-
-    if len(dataset_selection) == 0:
-        return styled_error("You need to select at least one dataset")
-        
+def request_model(model_text):
+    # Check if the model exists on the Hub
     base_model_on_hub, error_msg = is_model_on_hub(model_text)
 
     if not base_model_on_hub:
         return styled_error(f"Base model '{model_text}' {error_msg}")
-    
+
     # Construct the output dictionary
     current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
-    required_datasets = ', '.join(dataset_selection)
     eval_entry = {
         "date": current_time,
         "model": model_text,
-        "datasets_selected": required_datasets
+        "dataset": "vargha/common_voice_fa"
     }
-    
-    # Prepare file path 
+
+    # Prepare file path
     DIR_OUTPUT_REQUESTS.mkdir(parents=True, exist_ok=True)
-    
-    fn_datasets = '@ '.join(dataset_selection)
-    filename = model_text.replace("/","@") + "@@" + fn_datasets 
+
+    filename = model_text.replace("/", "@")
     if filename in requested_models:
-        return styled_error(f"A request for this model '{model_text}' and dataset(s) was already made.")
+        return styled_error(f"A request for this model '{model_text}' was already made.")
     try:
         filename_ext = filename + ".txt"
         out_filepath = DIR_OUTPUT_REQUESTS / filename_ext
@@ -89,18 +70,18 @@ def request_model(model_text, chbcoco2017):
         # Write the results to a text file
         with open(out_filepath, "w") as f:
             f.write(json.dumps(eval_entry))
-            
+
         upload_file(filename, out_filepath)
-        
+
         # Include file in the list of uploaded files
         requested_models.append(filename)
-        
+
         # Remove the local file
         out_filepath.unlink()
 
         return styled_message("🤗 Your request has been submitted and will be evaluated soon!</p>")
     except Exception as e:
-        return styled_error(f"Error submitting request!")
+        return styled_error(f"Error submitting request: {e}")
 
 with gr.Blocks() as demo:
     gr.HTML(BANNER, elem_id="banner")
@@ -114,7 +95,7 @@ with gr.Blocks() as demo:
                 elem_id="leaderboard-table",
                 interactive=False,
                 visible=True,
-                )
+            )
 
         with gr.TabItem("📈 Metrics", elem_id="od-benchmark-tab-table", id=1):
             gr.Markdown(METRICS_TAB_TEXT, elem_classes="markdown-text")
@@ -122,20 +103,13 @@ with gr.Blocks() as demo:
         with gr.TabItem("✉️✨ Request a model here!", elem_id="od-benchmark-tab-table", id=2):
             with gr.Column():
                 gr.Markdown("# ✉️✨ Request results for a new model here!", elem_classes="markdown-text")
-            with gr.Column():
-                gr.Markdown("Select a dataset:", elem_classes="markdown-text")
-                with gr.Column():
-                    model_name_textbox = gr.Textbox(label="Model name (user_name/model_name)")
-                    chb_coco2017 = gr.Checkbox(label="COCO validation 2017 dataset", visible=False, value=True, interactive=False)
-                with gr.Column():
-                    mdw_submission_result = gr.Markdown()
-                    btn_submitt = gr.Button(value="🚀 Request")
-                    btn_submitt.click(request_model, 
-                                      [model_name_textbox, chb_coco2017], 
-                                      mdw_submission_result)
+                model_name_textbox = gr.Textbox(label="Model name (user_name/model_name)")
+                mdw_submission_result = gr.Markdown()
+                btn_submit = gr.Button(value="🚀 Request")
+                btn_submit.click(request_model, [model_name_textbox], mdw_submission_result)
 
     gr.Markdown(f"Last updated on **{LAST_UPDATED}**", elem_classes="markdown-text")
-    
+
     with gr.Row():
         with gr.Accordion("📙 Citation", open=False):
             gr.Textbox(