Update app.py

app.py

@@ -1,75 +1,26 @@
 import pandas as pd
 import gradio as gr
-import plotly.graph_objects as go
 
-# Create the DataFrame
 data = {
-    'Method': ['GPT-4o', 'GPT-4o-mini', 'Gemini-1.5-Pro', 'Gemini-1.5-Flash', 'Qwen2-VL-2B'],
-    'MM Understanding & Reasoning': [57.90, 48.82, 46.67, 45.58, 40.59],
-    'OCR & Document Understanding': [59.11, 42.89, 36.59, 33.59, 25.68],
-    'Charts & Diagram Understanding': [73.57, 64.98, 47.06, 48.25, 27.83],
-    'Video Understanding': [74.27, 68.11, 42.94, 53.31, 38.90],
-    'Cultural Specific Understanding': [80.86, 65.92, 56.24, 46.54, 34.27],
-    'Medical Imaging': [49.90, 47.37, 33.77, 42.86, 29.12],
-    'Agro Specific': [80.75, 79.58, 72.12, 76.06, 52.02],
-    'Remote Sensing Understanding': [22.85, 16.93, 17.07, 14.95, 12.56]
+    "Method": ["GPT-4o", "GPT-4o-mini", "Gemini-1.5-Pro", "Gemini-1.5-Flash", "Qwen2-VL-2B"],
+    "MM Understanding & Reasoning": [57.90, 48.82, 46.67, 45.58, 40.59],
+    "OCR & Document Understanding": [59.11, 42.89, 36.59, 33.59, 25.68],
+    "Charts & Diagram Understanding": [73.57, 64.98, 47.06, 48.25, 27.83],
+    "Video Understanding": [74.27, 68.11, 42.94, 53.31, 38.90],
+    "Cultural Specific Understanding": [80.86, 65.92, 56.24, 46.54, 34.27],
+    "Medical Imaging": [49.90, 47.37, 33.77, 42.86, 29.12],
+    "Agro Specific": [80.75, 79.58, 72.12, 76.06, 52.02],
+    "Remote Sensing Understanding": [22.85, 16.93, 17.07, 14.95, 12.56]
 }
 
 df = pd.DataFrame(data)
 
-def plot_performance():
-    categories = df.columns[1:]
-    fig = go.Figure()
-
-    for method in df['Method']:
-        values = df[df['Method'] == method].iloc[0, 1:].tolist()
-        fig.add_trace(go.Scatterpolar(
-            r=values,
-            theta=categories,
-            fill='toself',
-            name=method
-        ))
-
-    fig.update_layout(
-        polar=dict(
-            radialaxis=dict(
-                visible=True,
-                range=[0, 100]
-            )),
-        showlegend=True,
-        title="Performance Comparison across Categories"
-    )
-    return fig
-
-def create_leaderboard():
+def display_data():
     return df
 
-# Define the Gradio interface
 with gr.Blocks() as demo:
-    gr.Markdown("# Multimodal Understanding Leaderboard")
-    
-    with gr.Tabs():
-        with gr.TabItem("📊 Performance Plot"):
-            gr.Plot(plot_performance)
-        
-        with gr.TabItem("🔍 Leaderboard Table"):
-            gr.DataFrame(create_leaderboard)
-        
-        with gr.TabItem("📝 About"):
-            gr.Markdown("""
-            This leaderboard compares the performance of various models across different categories of multimodal understanding tasks. The scores represent the accuracy or performance metric for each model in the respective category.
-
-            **Categories:**
-            - MM Understanding & Reasoning
-            - OCR & Document Understanding
-            - Charts & Diagram Understanding
-            - Video Understanding
-            - Cultural Specific Understanding
-            - Medical Imaging
-            - Agro Specific
-            - Remote Sensing Understanding
-
-            The data is presented both as a radar chart for visual comparison and as a table for detailed viewing.
-            """)
+    gr.Markdown("# Model Performance Across Various Understanding Tasks")
+    gr.Markdown("This table shows the performance of different models across various tasks including OCR, chart understanding, video, medical imaging, and more.")
+    gr.Dataframe(value=df, label="Model Performance", interactive=False)
 
-demo.launch()
+demo.launch()