Update app.py

app.py

@@ -53,6 +53,7 @@ def get_trending_accounts(limit=100):
                                       timeout=30)
         
         # Process spaces data
+        spaces_owners = []
         if spaces_response.status_code == 200:
             spaces = spaces_response.json()
             
@@ -70,8 +71,10 @@ def get_trending_accounts(limit=100):
             # Get top owners by count for spaces
             top_owners_spaces = sorted(owner_counts_spaces.items(), key=lambda x: x[1], reverse=True)[:limit]
             trending_data["spaces"] = top_owners_spaces
+            spaces_owners = [owner for owner, _ in top_owners_spaces]
         
         # Process models data
+        models_owners = []
         if models_response.status_code == 200:
             models = models_response.json()
             
@@ -89,30 +92,22 @@ def get_trending_accounts(limit=100):
             # Get top owners by count for models
             top_owners_models = sorted(owner_counts_models.items(), key=lambda x: x[1], reverse=True)[:limit]
             trending_data["models"] = top_owners_models
+            models_owners = [owner for owner, _ in top_owners_models]
         
-        # Combine rankings for overall trending (weighted average)
-        combined_scores = {}
-        
-        # Add scores from spaces
-        for owner, count in trending_data["spaces"]:
-            if owner not in combined_scores:
-                combined_scores[owner] = {"spaces": 0, "models": 0, "total": 0}
-            combined_scores[owner]["spaces"] = count
-        
-        # Add scores from models
-        for owner, count in trending_data["models"]:
-            if owner not in combined_scores:
-                combined_scores[owner] = {"spaces": 0, "models": 0, "total": 0}
-            combined_scores[owner]["models"] = count
-        
-        # Calculate total score (spaces + models)
-        for owner in combined_scores:
-            combined_scores[owner]["total"] = combined_scores[owner]["spaces"] + combined_scores[owner]["models"]
-        
-        # Sort by total score
-        sorted_combined = sorted(combined_scores.items(), key=lambda x: x[1]["total"], reverse=True)[:limit]
+        # Combine rankings for overall trending based on appearance in both lists
+        combined_score = {}
+        for i, owner in enumerate(spaces_owners):
+            if owner not in combined_score:
+                combined_score[owner] = 0
+            combined_score[owner] += (limit - i)  # Higher rank gives more points
+            
+        for i, owner in enumerate(models_owners):
+            if owner not in combined_score:
+                combined_score[owner] = 0
+            combined_score[owner] += (limit - i)  # Higher rank gives more points
         
-        # Extract just the owner names for dropdown
+        # Sort by combined score
+        sorted_combined = sorted(combined_score.items(), key=lambda x: x[1], reverse=True)[:limit]
         trending_authors = [owner for owner, _ in sorted_combined]
         
         return trending_authors, trending_data["spaces"], trending_data["models"]
@@ -274,34 +269,62 @@ with st.sidebar:
     st.title("👤 Contributor")
     
     # Create tabs for Spaces and Models rankings
-    tab1, tab2 = st.tabs(["Spaces Ranking", "Models Ranking"])
+    tab1, tab2, tab3 = st.tabs(["Overall Ranking", "Spaces Ranking", "Models Ranking"])
     
     with tab1:
+        # Show combined trending accounts list
+        st.subheader("🔥 Top 100 Overall Contributors")
+        
+        # Display the top 100 accounts list
+        st.markdown("### Combined Contributors Ranking")
+        
+        # Create a data frame for the table
+        if trending_accounts:
+            # Create a mapping from username to Spaces and Models rankings
+            spaces_rank = {owner: idx+1 for idx, (owner, _) in enumerate(top_owners_spaces)}
+            models_rank = {owner: idx+1 for idx, (owner, _) in enumerate(top_owners_models)}
+            
+            # Create the overall ranking dataframe
+            overall_data = []
+            for idx, username in enumerate(trending_accounts[:100]):
+                spaces_position = spaces_rank.get(username, "-")
+                models_position = models_rank.get(username, "-")
+                overall_data.append([username, spaces_position, models_position])
+            
+            ranking_data_overall = pd.DataFrame(
+                overall_data, 
+                columns=["Contributor", "Spaces Rank", "Models Rank"]
+            )
+            ranking_data_overall.index = ranking_data_overall.index + 1  # Start index from 1 for ranking
+            
+            st.dataframe(
+                ranking_data_overall,
+                column_config={
+                    "Contributor": st.column_config.TextColumn("Contributor"),
+                    "Spaces Rank": st.column_config.TextColumn("Spaces Rank (top 100)"),
+                    "Models Rank": st.column_config.TextColumn("Models Rank (top 100)")
+                },
+                use_container_width=True,
+                hide_index=False
+            )
+    
+    with tab2:
         # Show trending accounts list by Spaces
-        st.subheader("🚀 Top 30 by Spaces")
+        st.subheader("🚀 Top 100 by Spaces")
         
-        # Display the top 30 accounts list with their scores
+        # Display the top 100 accounts list
         st.markdown("### Spaces Contributors Ranking")
         
         # Create a data frame for the table
         if top_owners_spaces:
-            ranking_data_spaces = pd.DataFrame(top_owners_spaces[:30], columns=["Contributor", "Spaces Count"])
+            ranking_data_spaces = pd.DataFrame(top_owners_spaces[:100], columns=["Contributor", "Spaces Count"])
             ranking_data_spaces.index = ranking_data_spaces.index + 1  # Start index from 1 for ranking
             
-            # Add a score column based on spaces count
-            ranking_data_spaces["Score"] = ranking_data_spaces["Spaces Count"].apply(lambda x: x * 10)  # Multiply by 10 for a score metric
-            
             st.dataframe(
                 ranking_data_spaces,
                 column_config={
                     "Contributor": st.column_config.TextColumn("Contributor"),
-                    "Spaces Count": st.column_config.NumberColumn("Spaces Count (based on top 500 spaces)", format="%d"),
-                    "Score": st.column_config.ProgressColumn(
-                        "Score (within TOP 500 SPACES)",
-                        min_value=0,
-                        max_value=ranking_data_spaces["Score"].max() * 1.1,  # Add 10% to max for visual scale
-                        format="%d pts"
-                    )
+                    "Spaces Count": st.column_config.NumberColumn("Spaces Count (based on top 500 spaces)", format="%d")
                 },
                 use_container_width=True,
                 hide_index=False
@@ -327,30 +350,21 @@ with st.sidebar:
     
     with tab2:
         # Show trending accounts list by Models
-        st.subheader("🧠 Top 30 by Models")
+        st.subheader("🧠 Top 100 by Models")
         
-        # Display the top 30 accounts list with their scores
+        # Display the top 100 accounts list
         st.markdown("### Models Contributors Ranking")
         
         # Create a data frame for the table
         if top_owners_models:
-            ranking_data_models = pd.DataFrame(top_owners_models[:30], columns=["Contributor", "Models Count"])
+            ranking_data_models = pd.DataFrame(top_owners_models[:100], columns=["Contributor", "Models Count"])
             ranking_data_models.index = ranking_data_models.index + 1  # Start index from 1 for ranking
             
-            # Add a score column based on models count
-            ranking_data_models["Score"] = ranking_data_models["Models Count"].apply(lambda x: x * 10)  # Multiply by 10 for a score metric
-            
             st.dataframe(
                 ranking_data_models,
                 column_config={
                     "Contributor": st.column_config.TextColumn("Contributor"),
-                    "Models Count": st.column_config.NumberColumn("Models Count (based on top 500 models)", format="%d"),
-                    "Score": st.column_config.ProgressColumn(
-                        "Score (within TOP 500 MODELS)",
-                        min_value=0,
-                        max_value=ranking_data_models["Score"].max() * 1.1,  # Add 10% to max for visual scale
-                        format="%d pts"
-                    )
+                    "Models Count": st.column_config.NumberColumn("Models Count (based on top 500 models)", format="%d")
                 },
                 use_container_width=True,
                 hide_index=False
@@ -378,7 +392,7 @@ with st.sidebar:
     st.subheader("Select Contributor")
     selected_trending = st.selectbox(
         "Select trending account",
-        options=trending_accounts[:30],  # Limit to top 30
+        options=trending_accounts[:100],  # Limit to top 100
         index=0 if trending_accounts else None,
         key="trending_selectbox"
     )
@@ -411,21 +425,35 @@ st.title("🤗 Hugging Face Contributions")
 if username:
     with st.spinner(f"Fetching commit data for {username}..."):
         # Display contributor rank if in top 30
-        if username in trending_accounts[:30]:
+        if username in trending_accounts[:100]:
             rank = trending_accounts.index(username) + 1
             st.success(f"🏆 {username} is ranked #{rank} in the top trending contributors!")
             
             # Find user in spaces ranking
+            spaces_rank = None
             for i, (owner, count) in enumerate(top_owners_spaces):
                 if owner == username:
-                    st.info(f"🚀 Spaces Ranking: #{i+1} with {count} spaces")
+                    spaces_rank = i+1
+                    st.info(f"🚀 Spaces Ranking: #{spaces_rank} with {count} spaces")
                     break
             
             # Find user in models ranking
+            models_rank = None
             for i, (owner, count) in enumerate(top_owners_models):
                 if owner == username:
-                    st.info(f"🧠 Models Ranking: #{i+1} with {count} models")
+                    models_rank = i+1
+                    st.info(f"🧠 Models Ranking: #{models_rank} with {count} models")
                     break
+                    
+            # Display combined ranking info
+            combined_info = []
+            if spaces_rank and spaces_rank <= 100:
+                combined_info.append(f"Spaces: #{spaces_rank}")
+            if models_rank and models_rank <= 100:
+                combined_info.append(f"Models: #{models_rank}")
+                
+            if combined_info:
+                st.success(f"Combined Rankings (Top 100): {', '.join(combined_info)}")
         
         # Create a dictionary to store commits by type
         commits_by_type = {}