Fixed text wrapping in tables & Added Contact Info

app.py

@@ -14,13 +14,11 @@ logging.basicConfig(
     format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
 )
 
-# Disable all potentially sensitive loggers immediately
 logging.getLogger("httpx").setLevel(logging.ERROR)
 logging.getLogger("urllib3").setLevel(logging.ERROR)
 logging.getLogger("matplotlib").setLevel(logging.WARNING)
 logging.getLogger("huggingface_hub").setLevel(logging.ERROR)
 
-# Minimize the OAuth imports to prevent errors
 from gradio.oauth import OAuthProfile
 
 from src.display.about import (
@@ -214,7 +212,7 @@ def create_demo():
                                 value=leaderboard_df,
                                 label="Model Performance Comparison",
                                 interactive=False,
-                                column_widths=["300px", "165px" ,"165px", "120px", "120px", "180px", "220px", "100px", "100px", "120px"]
+                                column_widths=["300px", "180px" ,"180px", "150px", "150px", "180px", "220px", "100px", "100px", "120px"]
 
                             )
                             
@@ -251,7 +249,7 @@ def create_demo():
                                 value=human_arena_df,
                                 label="Human Arena Results",
                                 interactive=False,
-                                column_widths=["300px", "150px", "110px", "110px", "110px", "156px", "169px", "100px", "120px"]
+                                column_widths=["300px", "180px", "110px", "110px", "110px", "156px", "169px", "100px", "120px"]
 
                             )
                             
@@ -267,7 +265,7 @@ def create_demo():
                                 value=rag_details_df,
                                 label="Retrieval Detailed Results",
                                 interactive=False,
-                                column_widths=["280px", "120px", "140px", "140px", "140px", "120px", "160px", "100px", "120px"]
+                                column_widths=["300px", "120px", "140px", "140px", "140px", "120px", "160px", "100px", "120px"]
 
                             )
                             

src/display/about.py

@@ -60,6 +60,8 @@ Evaluate your model's performance in the following categories:
 7. 🧩 **Structured Outputs** - Coming soon!
 
 Evaluate your model in any or all of these categories to discover its capabilities and areas of excellence.
+
+For any questions, please contact us at info@newmind.ai
 """
 
 # Detailed explanation of benchmarks and reproduction steps

src/display/css_html_js.py

@@ -44,12 +44,15 @@ custom_css = """
     display: none;
 }
 
-/* Limit the width of the first AutoEvalColumn so that names don't expand too much */
+/* Allow model names to display fully with text wrapping */
 table td:first-child,
 table th:first-child {
-    max-width: 400px;
-    overflow: auto;
-    white-space: nowrap;
+    min-width: 250px;
+    max-width: 500px;
+    overflow: visible;
+    white-space: normal;
+    word-wrap: break-word;
+    line-height: 1.4;
 }
 
 .tab-buttons button {

src/utils.py

@@ -917,6 +917,33 @@ def create_raw_details_table(benchmark_data, benchmark_type):
     # Create DataFrame
     df = pd.DataFrame(flattened_data)
     
+    # Format confidence interval for arena data
+    if benchmark_type == "arena" and "95%(CI)" in df.columns:
+        def format_confidence_interval(ci_value):
+            """Convert '-1.65/+2.66' to '+2.66/-1.65' format"""
+            if isinstance(ci_value, str) and "/" in ci_value:
+                parts = ci_value.split("/")
+                if len(parts) == 2:
+                    negative_part = parts[0].strip()
+                    positive_part = parts[1].strip()
+                    
+                    # Remove the signs and get the numbers
+                    if negative_part.startswith("-"):
+                        negative_num = negative_part[1:]
+                    else:
+                        negative_num = negative_part
+                    
+                    if positive_part.startswith("+"):
+                        positive_num = positive_part[1:]
+                    else:
+                        positive_num = positive_part
+                    
+                    # Return in +positive/-negative format
+                    return f"+{positive_num}/-{negative_num}"
+            return ci_value
+        
+        df["95%(CI)"] = df["95%(CI)"].apply(format_confidence_interval)
+    
     # Ensure model_name is first column
     if "model_name" in df.columns:
         cols = ["model_name"] + [col for col in df.columns if col != "model_name"]