Update leaderboard display

app.py

@@ -1,114 +1,114 @@
-
-import gradio as gr
-import json
-import pandas as pd
-import plotly.express as px
-import plotly.graph_objects as go
-
-def load_results():
-    with open('results.json', 'r') as f:
-        return json.load(f)
-
-def create_metrics_df(results):
-    rows = []
-    for r in results:
-        row = {
-            'Model': r['model_name'],
-            'Timestamp': r['timestamp'],
-            'Embeddings': r['config']['embedding_model'],
-            'Retriever': r['config']['retriever_type'],
-            'Top-K': r['config']['retrieval_config'].get('top_k', 'N/A')
-        }
-        
-        # Add metrics
-        metrics = r['metrics']
-        for category in ['retrieval', 'generation']:
-            if category in metrics:
-                for metric_name, value in metrics[category].items():
-                    row[f"{category}_{metric_name}"] = round(value, 4)
-        
-        rows.append(row)
-    
-    return pd.DataFrame(rows)
-
-def create_comparison_plot(df, metric_category):
-    metrics = [col for col in df.columns if col.startswith(metric_category)]
-    if not metrics:
-        return None
-        
-    fig = go.Figure()
-    for metric in metrics:
-        fig.add_trace(go.Bar(
-            name=metric.split('_')[-1],
-            x=df['Model'],
-            y=df[metric],
-            text=df[metric].round(3),
-            textposition='auto',
-        ))
-    
-    fig.update_layout(
-        title=f"{metric_category.capitalize()} Metrics Comparison",
-        xaxis_title="Model",
-        yaxis_title="Score",
-        barmode='group'
-    )
-    return fig
-
-def create_interface():
-    results = load_results()
-    df = create_metrics_df(results)
-    
-    with gr.Blocks() as demo:
-        gr.Markdown("# RAG Evaluation Leaderboard")
-        
-        with gr.Tabs():
-            with gr.Tab("Leaderboard"):
-                gr.Dataframe(
-                    df,
-                    headers=df.columns.tolist(),
-                    interactive=False
-                )
-            
-            with gr.Tab("Retrieval Metrics"):
-                gr.Plot(create_comparison_plot(df, 'retrieval'))
-            
-            with gr.Tab("Generation Metrics"):
-                gr.Plot(create_comparison_plot(df, 'generation'))
-            
-            with gr.Tab("Configuration Details"):
-                config_df = df[['Model', 'Embeddings', 'Retriever', 'Top-K', 'Timestamp']]
-                gr.Dataframe(config_df)
-        
-        gr.Markdown('''
-        ## How to Submit
-        
-        To submit your results:
-        ```python
-        from rag_leaderboard import RAGLeaderboard
-        
-        # Initialize leaderboard
-        leaderboard = RAGLeaderboard(
-            repo_id="your-username/repo-name",
-            token="your-hf-token"
-        )
-        
-        # Submit results
-        leaderboard.submit_results(
-            model_name="Your Model Name",
-            metrics={
-                "retrieval": {"hit_rate": 0.8, "mrr": 0.6},
-                "generation": {"rouge1": 0.7, "rouge2": 0.5, "rougeL": 0.6}
-            },
-            config={
-                "embedding_model": "your-embedding-model",
-                "retriever_type": "dense",
-                "retrieval_config": {"top_k": 3}
-            }
-        )
-        ```
-        ''')
-    
-    return demo
-
-demo = create_interface()
-demo.launch()
+
+import gradio as gr
+import json
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+
+def load_results():
+    with open('results.json', 'r') as f:
+        return json.load(f)
+
+def create_metrics_df(results):
+    rows = []
+    for r in results:
+        row = {
+            'Model': r['model_name'],
+            'Timestamp': r['timestamp'],
+            'Embeddings': r['config']['embedding_model'],
+            'Retriever': r['config']['retriever_type'],
+            'Top-K': r['config']['retrieval_config'].get('top_k', 'N/A')
+        }
+        
+        # Add metrics
+        metrics = r['metrics']
+        for category in ['retrieval', 'generation']:
+            if category in metrics:
+                for metric_name, value in metrics[category].items():
+                    row[f"{category}_{metric_name}"] = round(value, 4)
+        
+        rows.append(row)
+    
+    return pd.DataFrame(rows)
+
+def create_comparison_plot(df, metric_category):
+    metrics = [col for col in df.columns if col.startswith(metric_category)]
+    if not metrics:
+        return None
+        
+    fig = go.Figure()
+    for metric in metrics:
+        fig.add_trace(go.Bar(
+            name=metric.split('_')[-1],
+            x=df['Model'],
+            y=df[metric],
+            text=df[metric].round(3),
+            textposition='auto',
+        ))
+    
+    fig.update_layout(
+        title=f"{metric_category.capitalize()} Metrics Comparison",
+        xaxis_title="Model",
+        yaxis_title="Score",
+        barmode='group'
+    )
+    return fig
+
+def create_interface():
+    results = load_results()
+    df = create_metrics_df(results)
+    
+    with gr.Blocks() as demo:
+        gr.Markdown("# RAG Evaluation Leaderboard")
+        
+        with gr.Tabs():
+            with gr.Tab("Leaderboard"):
+                gr.Dataframe(
+                    df,
+                    headers=df.columns.tolist(),
+                    interactive=False
+                )
+            
+            with gr.Tab("Retrieval Metrics"):
+                gr.Plot(create_comparison_plot(df, 'retrieval'))
+            
+            with gr.Tab("Generation Metrics"):
+                gr.Plot(create_comparison_plot(df, 'generation'))
+            
+            with gr.Tab("Configuration Details"):
+                config_df = df[['Model', 'Embeddings', 'Retriever', 'Top-K', 'Timestamp']]
+                gr.Dataframe(config_df)
+        
+        gr.Markdown('''
+        ## How to Submit
+        
+        To submit your results:
+        ```python
+        from rag_leaderboard import RAGLeaderboard
+        
+        # Initialize leaderboard
+        leaderboard = RAGLeaderboard(
+            repo_id="your-username/repo-name",
+            token="your-hf-token"
+        )
+        
+        # Submit results
+        leaderboard.submit_results(
+            model_name="Your Model Name",
+            metrics={
+                "retrieval": {"hit_rate": 0.8, "mrr": 0.6},
+                "generation": {"rouge1": 0.7, "rouge2": 0.5, "rougeL": 0.6}
+            },
+            config={
+                "embedding_model": "your-embedding-model",
+                "retriever_type": "dense",
+                "retrieval_config": {"top_k": 3}
+            }
+        )
+        ```
+        ''')
+    
+    return demo
+
+demo = create_interface()
+demo.launch()