Update app.py

app.py

@@ -1,6 +1,6 @@
 import streamlit as st
 import pandas as pd
-from huggingface_hub import HfApi, ModelCard, login
+from huggingface_hub import HfApi, ModelCard
 from huggingface_hub.utils import RepositoryNotFoundError, RevisionNotFoundError
 import re
 from io import StringIO
@@ -16,26 +16,21 @@ def cached_model_info(_api, model):
     """Fetch model information from the Hugging Face API and cache the result."""
     try:
         return _api.model_info(repo_id=str(model))
-    except (RepositoryNotFoundError, RevisionNotFoundError) as e:
-        st.error(f"Error fetching model info for {model}: {str(e)}")
-        return None
-    except Exception as e:
-        st.error(f"Unexpected error fetching model info for {model}: {str(e)}")
+    except (RepositoryNotFoundError, RevisionNotFoundError):
         return None
 
 @st.cache_data
 def get_model_info(df):
     """Get model information and update the DataFrame with likes and tags."""
     api = HfApi()
-    with st.spinner("Fetching model information..."):
-        for index, row in df.iterrows():
-            model_info = cached_model_info(api, row['Model'].strip())
-            if model_info:
-                df.loc[index, 'Likes'] = model_info.likes
-                df.loc[index, 'Tags'] = ', '.join(model_info.tags)
-            else:
-                df.loc[index, 'Likes'] = -1
-                df.loc[index, 'Tags'] = ''
+    for index, row in df.iterrows():
+        model_info = cached_model_info(api, row['Model'].strip())
+        if model_info:
+            df.loc[index, 'Likes'] = model_info.likes
+            df.loc[index, 'Tags'] = ', '.join(model_info.tags)
+        else:
+            df.loc[index, 'Likes'] = -1
+            df.loc[index, 'Tags'] = ''
     return df
 
 def convert_markdown_table_to_dataframe(md_content):
@@ -99,23 +94,11 @@ def fetch_merge_configs(df):
     except Exception as e:
         st.error(f"Error while fetching merge configs: {str(e)}")
 
-def authenticate_hf():
-    """Authenticate with the Hugging Face API."""
-    token = st.text_input("Enter your Hugging Face API token", type="password")
-    if token:
-        login(token=token)
-        st.success("Authenticated successfully")
-    else:
-        st.warning("You need to enter a Hugging Face API token to access private or gated models")
-
 def main():
     """Main function to set up the Streamlit app and display the leaderboard."""
     st.set_page_config(page_title="YALL - Yet Another LLM Leaderboard", layout="wide")
     st.title("🏆 YALL - Yet Another LLM Leaderboard")
     st.markdown("Leaderboard made with 🧐 [LLM AutoEval](https://github.com/mlabonne/llm-autoeval) using [Nous](https://huggingface.co/NousResearch) benchmark suite.")
-
-    authenticate_hf()
-
     content = create_yall()
     tab1, tab2 = st.tabs(["🏆 Leaderboard", "📝 About"])