Update app.py

app.py

@@ -6,6 +6,8 @@ from huggingface_hub.utils import RepositoryNotFoundError, RevisionNotFoundError
 from itertools import combinations
 import re
 from functools import cache
+from io import StringIO  # Corrected import for StringIO
+
 
 # Define function to cache model info from Hugging Face API
 @cache
@@ -18,7 +20,7 @@ def cached_model_info(api, model):
 # Convert markdown table to DataFrame and extract Hugging Face URLs
 def convert_markdown_table_to_dataframe(md_content):
     cleaned_content = re.sub(r'\|\s*$', '', re.sub(r'^\|\s*', '', md_content, flags=re.MULTILINE), flags=re.MULTILINE)
-    df = pd.read_csv(pd.compat.StringIO(cleaned_content), sep="\|", engine='python')
+    df = pd.read_csv(StringIO(cleaned_content), sep="\|", engine='python')  # Corrected use of StringIO
     df = df.drop(0).reset_index(drop=True)
     df.columns = df.columns.str.strip()
     model_link_pattern = r'\[(.*?)\]\((.*?)\)\s*\[.*?\]\(.*?\)'
@@ -39,21 +41,6 @@ def get_and_update_model_info(df):
             df.loc[index, 'Tags'] = ''
     return df
 
-    
-
-# Function to get model info from Hugging Face API using caching
-@cache
-def cached_model_info(api, model):
-    try:
-        return api.model_info(repo_id=str(model))
-    except (RepositoryNotFoundError, RevisionNotFoundError):
-        return None
-
-# Define the score columns
-score_columns = ['Average', 'AGIEval', 'GPT4All', 'TruthfulQA', 'Bigbench']
-
-
-
 # Calculate the highest combined score for a given column
 def calculate_highest_combined_score(data, column):
     scores = data[column].dropna().tolist()
@@ -79,18 +66,6 @@ def display_highest_combined_scores(data, score_columns):
                     st.write(f"Score: {score}, Models: {', '.join(combination)}")
 
 
-# Function to calculate the highest combined score for a given column
-def calculate_highest_combined_score(data, column):
-    scores = data[column].tolist()
-    models = data['Model'].tolist()
-    top_combinations = {2: [], 3: [], 4: [], 5: [], 6: []}
-    for r in range(2, 7):
-        for combination in combinations(zip(scores, models), r):
-            combined_score = sum(score for score, _ in combination)
-            top_combinations[r].append((combined_score, tuple(model for _, model in combination)))
-        top_combinations[r] = sorted(top_combinations[r], key=lambda x: x[0], reverse=True)[:3]
-    return column, top_combinations
-
 # Function to create bar chart for a given category
 def create_bar_chart(df, category):
     """Create and display a bar chart for a given category."""
@@ -121,9 +96,8 @@ def main():
     st.markdown("Displaying top combinations of models based on scores.")
 
     # Placeholder for content fetching or upload
-    # For demonstration, let's assume 'content' is a markdown string from somewhere
-    content = """Your markdown table content here"""
-    
+    content = """Your markdown table content here"""  # Placeholder content
+
     if content:
         df = convert_markdown_table_to_dataframe(content)
         df = get_and_update_model_info(df)
@@ -136,6 +110,7 @@ def main():
             df[col] = pd.to_numeric(df[col], errors='coerce')
 
         display_highest_combined_scores(df, score_columns)
+    
     # Create tabs for leaderboard and about section
     content = create_yall()
     tab1, tab2 = st.tabs(["🏆 Leaderboard", "📝 About"])