chore: top 20 auths, cap score

app.py

@@ -107,7 +107,7 @@ def compute_coverage_score(eval_data):
                 "fields_total": 0,
             }
 
-    return max(round(total_score, 2), 100), scores
+    return min(round(total_score, 2), 100), scores
 
 
 def get_llm_feedback(yaml_content, api_token=None):
@@ -216,6 +216,7 @@ def load_all_eval_cards():
 
                     # Compute coverage score
                     score, score_details = compute_coverage_score(eval_data)
+                    score = min(score, 100)
 
                     # Extract key metadata
                     eval_cards.append(
@@ -367,14 +368,29 @@ def refresh_gallery():
     # Convert data to pandas DataFrame for table view
     table_data = []
     for card in eval_cards:
-        table_data.append(
-            {
-                "Title": card["title"],
-                "Authors": card["authors"][5],
-                "Creation Date": card["creation_date"],
-                "Coverage Score": f"{card['coverage_score']}%",
-            }
-        )
+        author_counts = {}
+        for card in eval_cards:
+            authors = card["authors"].split(", ")
+            for author in authors:
+                if author in author_counts:
+                    author_counts[author] += 1
+                else:
+                    author_counts[author] = 1
+
+        top_authors = sorted(author_counts.items(), key=lambda x: x[1], reverse=True)[:5]
+        top_authors = [author for author, count in top_authors]
+
+        for card in eval_cards:
+            authors = card["authors"].split(", ")
+            filtered_authors = [author for author in authors if author in top_authors]
+            table_data.append(
+                {
+                    "Title": card["title"],
+                    "Authors": ", ".join(filtered_authors),
+                    "Creation Date": card["creation_date"],
+                    "Coverage Score": f"{card['coverage_score']}%",
+                }
+            )
 
     df = pd.DataFrame(table_data)