Improve error handling in model information retrieval and enforce model size constraints

utils.py

@@ -47,7 +47,7 @@ def get_model_info(model_id, verbose=False):
             print(f"The model supports the following precisions: {supported_precisions}")
         return num_downloads, num_likes, license, num_parameters, supported_precisions
     except Exception as e:
-        gr.Error(f"Error: Could not fetch model information. {str(e)}")
+        print(f"Error: Could not fetch model information. {str(e)}")
         return 0, 0, "Unknown", 0, []
 
 def fetch_model_information(model_name):
@@ -61,11 +61,12 @@ def fetch_model_information(model_name):
     return gr.update(choices=supported_precisions, value=supported_precisions[0]), license, num_parameters, num_downloads, num_likes
 
 def submit_model(model_name, revision, precision, params, license, task, pending_gradio_df):
-    try:
-        if float(params) > 5000:
-            return "Model size should be less than 5000 million parameters (5 billion) 👀", pending_gradio_df
-    except ValueError:
-        gr.Error("The parameter count is not present or is not a number. Please make sure its available and its correct"),
+
+    if float(params) == 0:
+        return "Model size cannot be zero. If the Auto Fetch feature failed and your model is suitable for evaluation, please open a community discussion for help.", pending_gradio_df
+    
+    if float(params) > 5000:
+        return "Model size should be less than 5000 million parameters (5 billion) 👀", pending_gradio_df
 
     # Handle 'Missing' precision
     if precision == 'Missing':