Refactor of model page

app.py

@@ -2,11 +2,12 @@ import matplotlib.pyplot as plt
 import matplotlib
 import pandas as pd
 import gradio as gr
-import threading
 
 from data import CIResults
-from utils import logger, generate_underlined_line
+from utils import logger
 from summary_page import create_summary_page
+from model_page import plot_model_stats
+
 
 # Configure matplotlib to prevent memory warnings and set dark background
 matplotlib.rcParams['figure.facecolor'] = '#000000'
@@ -22,216 +23,6 @@ Ci_results.load_data()
 Ci_results.schedule_data_reload()
 
 
-def plot_model_stats(model_name: str) -> tuple[plt.Figure, str, str]:
-    """Draws a pie chart of model's passed, failed, skipped, and error stats."""
-    if Ci_results.df.empty or model_name not in Ci_results.df.index:
-        # Handle case where model data is not available
-        fig, ax = plt.subplots(figsize=(10, 8), facecolor='#000000')
-        ax.set_facecolor('#000000')
-        ax.text(0.5, 0.5, f'No data available for {model_name}', 
-                horizontalalignment='center', verticalalignment='center',
-                transform=ax.transAxes, fontsize=16, color='#888888',
-                fontfamily='monospace', weight='normal')
-        ax.set_xlim(0, 1)
-        ax.set_ylim(0, 1)
-        ax.axis('off')
-        return fig, "No data available", "No data available"
-    
-    row = Ci_results.df.loc[model_name]
-    
-    # Handle missing values and get counts directly from dataframe
-    success_amd = int(row.get('success_amd', 0)) if pd.notna(row.get('success_amd', 0)) else 0
-    success_nvidia = int(row.get('success_nvidia', 0)) if pd.notna(row.get('success_nvidia', 0)) else 0
-    failed_multi_amd = int(row.get('failed_multi_no_amd', 0)) if pd.notna(row.get('failed_multi_no_amd', 0)) else 0
-    failed_multi_nvidia = int(row.get('failed_multi_no_nvidia', 0)) if pd.notna(row.get('failed_multi_no_nvidia', 0)) else 0
-    failed_single_amd = int(row.get('failed_single_no_amd', 0)) if pd.notna(row.get('failed_single_no_amd', 0)) else 0
-    failed_single_nvidia = int(row.get('failed_single_no_nvidia', 0)) if pd.notna(row.get('failed_single_no_nvidia', 0)) else 0
-    
-    # Calculate total failures
-    total_failed_amd = failed_multi_amd + failed_single_amd
-    total_failed_nvidia = failed_multi_nvidia + failed_single_nvidia
-    
-    # Softer color palette - less pastel, more vibrant
-    colors = {
-        'passed': '#4CAF50',    # Medium green
-        'failed': '#E53E3E',    # More red
-        'skipped': '#FFD54F',   # Medium yellow
-        'error': '#8B0000'      # Dark red
-    }
-    
-    # Create stats dictionaries directly from dataframe values
-    amd_stats = {
-        'passed': success_amd,
-        'failed': total_failed_amd,
-        'skipped': 0,  # Not available in this dataset
-        'error': 0     # Not available in this dataset
-    }
-    
-    nvidia_stats = {
-        'passed': success_nvidia,
-        'failed': total_failed_nvidia,
-        'skipped': 0,  # Not available in this dataset
-        'error': 0     # Not available in this dataset
-    }
-    
-    # Filter out categories with 0 values for cleaner visualization
-    amd_filtered = {k: v for k, v in amd_stats.items() if v > 0}
-    nvidia_filtered = {k: v for k, v in nvidia_stats.items() if v > 0}
-    
-    if not amd_filtered and not nvidia_filtered:
-        # Handle case where all values are 0 - minimal empty state
-        fig, ax = plt.subplots(figsize=(10, 8), facecolor='#000000')
-        ax.set_facecolor('#000000')
-        ax.text(0.5, 0.5, 'No test results available', 
-                horizontalalignment='center', verticalalignment='center',
-                transform=ax.transAxes, fontsize=16, color='#888888',
-                fontfamily='monospace', weight='normal')
-        ax.set_xlim(0, 1)
-        ax.set_ylim(0, 1)
-        ax.axis('off')
-        return fig, "", ""
-    
-    # Create figure with two subplots side by side with padding
-    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 9), facecolor='#000000')
-    ax1.set_facecolor('#000000')
-    ax2.set_facecolor('#000000')
-    
-    def create_pie_chart(ax, device_label, filtered_stats):
-        if not filtered_stats:
-            ax.text(0.5, 0.5, 'No test results', 
-                   horizontalalignment='center', verticalalignment='center',
-                   transform=ax.transAxes, fontsize=14, color='#888888',
-                   fontfamily='monospace', weight='normal')
-            ax.set_title(device_label, 
-                        fontsize=28, weight='bold', pad=2, color='#FFFFFF', 
-                        fontfamily='monospace')
-            ax.axis('off')
-            return
-            
-        chart_colors = [colors[category] for category in filtered_stats.keys()]
-        
-        # Create minimal pie chart - full pie, no donut effect
-        wedges, texts, autotexts = ax.pie(
-            filtered_stats.values(), 
-            labels=[label.lower() for label in filtered_stats.keys()],  # Lowercase for minimal look
-            colors=chart_colors,
-            autopct=lambda pct: f'{int(pct/100*sum(filtered_stats.values()))}',
-            startangle=90,
-            explode=None,  # No separation
-            shadow=False,
-            wedgeprops=dict(edgecolor='#1a1a1a', linewidth=0.5),  # Minimal borders
-            textprops={'fontsize': 12, 'weight': 'normal', 'color': '#CCCCCC', 'fontfamily': 'monospace'}
-        )
-        
-        # Enhanced percentage text styling for better readability
-        for autotext in autotexts:
-            autotext.set_color('#000000')  # Black text for better contrast
-            autotext.set_weight('bold')
-            autotext.set_fontsize(14)
-            autotext.set_fontfamily('monospace')
-        
-        # Minimal category labels
-        for text in texts:
-            text.set_color('#AAAAAA')
-            text.set_weight('normal')
-            text.set_fontsize(13)
-            text.set_fontfamily('monospace')
-        
-        # Device label closer to chart and bigger
-        ax.set_title(device_label, 
-                    fontsize=28, weight='normal', pad=2, color='#FFFFFF', 
-                    fontfamily='monospace')
-    
-    # Create both pie charts with device labels
-    create_pie_chart(ax1, "amd", amd_filtered)
-    create_pie_chart(ax2, "nvidia", nvidia_filtered)
-    
-    # Add subtle separation line between charts - stops at device labels level
-    line_x = 0.5
-    fig.add_artist(plt.Line2D([line_x, line_x], [0.0, 0.85], 
-                              color='#333333', linewidth=1, alpha=0.5,
-                              transform=fig.transFigure))
-    
-    # Add central shared title for model name
-    fig.suptitle(f'{model_name.lower()}', 
-                fontsize=32, weight='bold', color='#CCCCCC', 
-                fontfamily='monospace', y=1)
-    
-    # Clean layout with padding and space for central title
-    plt.tight_layout()
-    plt.subplots_adjust(top=0.85, wspace=0.4)  # Added wspace for padding between charts
-    
-    # Generate failure info directly from dataframe
-    failures_amd = row.get('failures_amd', {})
-    failures_nvidia = row.get('failures_nvidia', {})
-    
-    amd_failed_info = extract_failure_info(failures_amd, 'AMD', failed_multi_amd, failed_single_amd)
-    nvidia_failed_info = extract_failure_info(failures_nvidia, 'NVIDIA', failed_multi_nvidia, failed_single_nvidia)
-    
-    return fig, amd_failed_info, nvidia_failed_info
-
-def extract_failure_info(failures_obj, device: str, multi_count: int, single_count: int) -> str:
-    """Extract failure information from failures object."""
-    if (not failures_obj or pd.isna(failures_obj)) and multi_count == 0 and single_count == 0:
-        return f"No failures on {device}"
-    
-    info_lines = []
-    
-    # Add counts summary
-    if multi_count > 0 or single_count > 0:
-        info_lines.append(generate_underlined_line(f"Failure Summary for {device}:"))
-        if multi_count > 0:
-            info_lines.append(f"Multi GPU failures: {multi_count}")
-        if single_count > 0:
-            info_lines.append(f"Single GPU failures: {single_count}")
-        info_lines.append("")
-    
-    # Try to extract detailed failure information
-    try:
-        if isinstance(failures_obj, dict):
-            # Check for multi and single failure categories
-            if 'multi' in failures_obj and failures_obj['multi']:
-                info_lines.append(generate_underlined_line(f"Multi GPU failure details:"))
-                if isinstance(failures_obj['multi'], list):
-                    # Handle list of failures (could be strings or dicts)
-                    for i, failure in enumerate(failures_obj['multi'][:10]):  # Limit to first 10
-                        if isinstance(failure, dict):
-                            # Extract meaningful info from dict (e.g., test name, line, etc.)
-                            failure_str = failure.get('line', failure.get('test', failure.get('name', str(failure))))
-                            info_lines.append(f"  {i+1}. {failure_str}")
-                        else:
-                            info_lines.append(f"  {i+1}. {str(failure)}")
-                    if len(failures_obj['multi']) > 10:
-                        info_lines.append(f"... and {len(failures_obj['multi']) - 10} more")
-                else:
-                    info_lines.append(str(failures_obj['multi']))
-                info_lines.append("")
-            
-            if 'single' in failures_obj and failures_obj['single']:
-                info_lines.append(generate_underlined_line(f"Single GPU failure details:"))
-                if isinstance(failures_obj['single'], list):
-                    # Handle list of failures (could be strings or dicts)
-                    for i, failure in enumerate(failures_obj['single'][:10]):  # Limit to first 10
-                        if isinstance(failure, dict):
-                            # Extract meaningful info from dict (e.g., test name, line, etc.)
-                            failure_str = failure.get('line', failure.get('test', failure.get('name', str(failure))))
-                            info_lines.append(f"  {i+1}. {failure_str}")
-                        else:
-                            info_lines.append(f"  {i+1}. {str(failure)}")
-                    if len(failures_obj['single']) > 10:
-                        info_lines.append(f"... and {len(failures_obj['single']) - 10} more")
-                else:
-                    info_lines.append(str(failures_obj['single']))
-        
-        return "\n".join(info_lines) if info_lines else f"No detailed failure info for {device}"
-    
-    except Exception as e:
-        if multi_count > 0 or single_count > 0:
-            return f"Failures detected on {device} (Multi: {multi_count}, Single: {single_count})\nDetails unavailable: {str(e)}"
-        return f"Error processing failure info for {device}: {str(e)}"
-
-
-
 # Load CSS from external file
 def load_css():
     try:
@@ -241,6 +32,7 @@ def load_css():
         logger.warning("styles.css not found, using minimal default styles")
         return "body { background: #000; color: #fff; }"
 
+
 # Create the Gradio interface with sidebar and dark theme
 with gr.Blocks(title="Model Test Results Dashboard", css=load_css()) as demo:
     
@@ -331,7 +123,7 @@ with gr.Blocks(title="Model Test Results Dashboard", css=load_css()) as demo:
     for i, btn in enumerate(model_buttons):
         model_name = model_choices[i]
         btn.click(
-            fn=lambda selected_model=model_name: plot_model_stats(selected_model),
+            fn=lambda selected_model=model_name: plot_model_stats(Ci_results.df, selected_model),
             outputs=[plot_output, amd_failed_tests_output, nvidia_failed_tests_output]
         ).then(
             fn=lambda: [gr.update(visible=False), gr.update(visible=True)],
@@ -432,5 +224,7 @@ with gr.Blocks(title="Model Test Results Dashboard", css=load_css()) as demo:
         outputs=[ci_links_display]
     )
 
+
+# Gradio entrypoint
 if __name__ == "__main__":
     demo.launch()

model_page.py

@@ -0,0 +1,216 @@
+import matplotlib.pyplot as plt
+import pandas as pd
+from utils import generate_underlined_line
+
+
+def extract_failure_info(failures_obj, device: str, multi_count: int, single_count: int) -> str:
+    """Extract failure information from failures object."""
+    if (not failures_obj or pd.isna(failures_obj)) and multi_count == 0 and single_count == 0:
+        return f"No failures on {device}"
+    
+    info_lines = []
+    
+    # Add counts summary
+    if multi_count > 0 or single_count > 0:
+        info_lines.append(generate_underlined_line(f"Failure Summary for {device}:"))
+        if multi_count > 0:
+            info_lines.append(f"Multi GPU failures: {multi_count}")
+        if single_count > 0:
+            info_lines.append(f"Single GPU failures: {single_count}")
+        info_lines.append("")
+    
+    # Try to extract detailed failure information
+    try:
+        if isinstance(failures_obj, dict):
+            # Check for multi and single failure categories
+            if 'multi' in failures_obj and failures_obj['multi']:
+                info_lines.append(generate_underlined_line(f"Multi GPU failure details:"))
+                if isinstance(failures_obj['multi'], list):
+                    # Handle list of failures (could be strings or dicts)
+                    for i, failure in enumerate(failures_obj['multi'][:10]):  # Limit to first 10
+                        if isinstance(failure, dict):
+                            # Extract meaningful info from dict (e.g., test name, line, etc.)
+                            failure_str = failure.get('line', failure.get('test', failure.get('name', str(failure))))
+                            info_lines.append(f"  {i+1}. {failure_str}")
+                        else:
+                            info_lines.append(f"  {i+1}. {str(failure)}")
+                    if len(failures_obj['multi']) > 10:
+                        info_lines.append(f"... and {len(failures_obj['multi']) - 10} more")
+                else:
+                    info_lines.append(str(failures_obj['multi']))
+                info_lines.append("")
+            
+            if 'single' in failures_obj and failures_obj['single']:
+                info_lines.append(generate_underlined_line(f"Single GPU failure details:"))
+                if isinstance(failures_obj['single'], list):
+                    # Handle list of failures (could be strings or dicts)
+                    for i, failure in enumerate(failures_obj['single'][:10]):  # Limit to first 10
+                        if isinstance(failure, dict):
+                            # Extract meaningful info from dict (e.g., test name, line, etc.)
+                            failure_str = failure.get('line', failure.get('test', failure.get('name', str(failure))))
+                            info_lines.append(f"  {i+1}. {failure_str}")
+                        else:
+                            info_lines.append(f"  {i+1}. {str(failure)}")
+                    if len(failures_obj['single']) > 10:
+                        info_lines.append(f"... and {len(failures_obj['single']) - 10} more")
+                else:
+                    info_lines.append(str(failures_obj['single']))
+        
+        return "\n".join(info_lines) if info_lines else f"No detailed failure info for {device}"
+    
+    except Exception as e:
+        if multi_count > 0 or single_count > 0:
+            return f"Failures detected on {device} (Multi: {multi_count}, Single: {single_count})\nDetails unavailable: {str(e)}"
+        return f"Error processing failure info for {device}: {str(e)}"
+
+
+def plot_model_stats(
+    df: pd.DataFrame,
+    model_name: str,
+) -> tuple[plt.Figure, str, str]:
+    """Draws a pie chart of model's passed, failed, skipped, and error stats."""
+    if df.empty or model_name not in df.index:
+        # Handle case where model data is not available
+        fig, ax = plt.subplots(figsize=(10, 8), facecolor='#000000')
+        ax.set_facecolor('#000000')
+        ax.text(0.5, 0.5, f'No data available for {model_name}', 
+                horizontalalignment='center', verticalalignment='center',
+                transform=ax.transAxes, fontsize=16, color='#888888',
+                fontfamily='monospace', weight='normal')
+        ax.set_xlim(0, 1)
+        ax.set_ylim(0, 1)
+        ax.axis('off')
+        return fig, "No data available", "No data available"
+    
+    row = df.loc[model_name]
+    
+    # Handle missing values and get counts directly from dataframe
+    success_amd = int(row.get('success_amd', 0)) if pd.notna(row.get('success_amd', 0)) else 0
+    success_nvidia = int(row.get('success_nvidia', 0)) if pd.notna(row.get('success_nvidia', 0)) else 0
+    failed_multi_amd = int(row.get('failed_multi_no_amd', 0)) if pd.notna(row.get('failed_multi_no_amd', 0)) else 0
+    failed_multi_nvidia = int(row.get('failed_multi_no_nvidia', 0)) if pd.notna(row.get('failed_multi_no_nvidia', 0)) else 0
+    failed_single_amd = int(row.get('failed_single_no_amd', 0)) if pd.notna(row.get('failed_single_no_amd', 0)) else 0
+    failed_single_nvidia = int(row.get('failed_single_no_nvidia', 0)) if pd.notna(row.get('failed_single_no_nvidia', 0)) else 0
+    
+    # Calculate total failures
+    total_failed_amd = failed_multi_amd + failed_single_amd
+    total_failed_nvidia = failed_multi_nvidia + failed_single_nvidia
+    
+    # Softer color palette - less pastel, more vibrant
+    colors = {
+        'passed': '#4CAF50',    # Medium green
+        'failed': '#E53E3E',    # More red
+        'skipped': '#FFD54F',   # Medium yellow
+        'error': '#8B0000'      # Dark red
+    }
+    
+    # Create stats dictionaries directly from dataframe values
+    amd_stats = {
+        'passed': success_amd,
+        'failed': total_failed_amd,
+        'skipped': 0,  # Not available in this dataset
+        'error': 0     # Not available in this dataset
+    }
+    
+    nvidia_stats = {
+        'passed': success_nvidia,
+        'failed': total_failed_nvidia,
+        'skipped': 0,  # Not available in this dataset
+        'error': 0     # Not available in this dataset
+    }
+    
+    # Filter out categories with 0 values for cleaner visualization
+    amd_filtered = {k: v for k, v in amd_stats.items() if v > 0}
+    nvidia_filtered = {k: v for k, v in nvidia_stats.items() if v > 0}
+    
+    if not amd_filtered and not nvidia_filtered:
+        # Handle case where all values are 0 - minimal empty state
+        fig, ax = plt.subplots(figsize=(10, 8), facecolor='#000000')
+        ax.set_facecolor('#000000')
+        ax.text(0.5, 0.5, 'No test results available', 
+                horizontalalignment='center', verticalalignment='center',
+                transform=ax.transAxes, fontsize=16, color='#888888',
+                fontfamily='monospace', weight='normal')
+        ax.set_xlim(0, 1)
+        ax.set_ylim(0, 1)
+        ax.axis('off')
+        return fig, "", ""
+    
+    # Create figure with two subplots side by side with padding
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 9), facecolor='#000000')
+    ax1.set_facecolor('#000000')
+    ax2.set_facecolor('#000000')
+    
+    def create_pie_chart(ax, device_label, filtered_stats):
+        if not filtered_stats:
+            ax.text(0.5, 0.5, 'No test results', 
+                   horizontalalignment='center', verticalalignment='center',
+                   transform=ax.transAxes, fontsize=14, color='#888888',
+                   fontfamily='monospace', weight='normal')
+            ax.set_title(device_label, 
+                        fontsize=28, weight='bold', pad=2, color='#FFFFFF', 
+                        fontfamily='monospace')
+            ax.axis('off')
+            return
+            
+        chart_colors = [colors[category] for category in filtered_stats.keys()]
+        
+        # Create minimal pie chart - full pie, no donut effect
+        wedges, texts, autotexts = ax.pie(
+            filtered_stats.values(), 
+            labels=[label.lower() for label in filtered_stats.keys()],  # Lowercase for minimal look
+            colors=chart_colors,
+            autopct=lambda pct: f'{int(pct/100*sum(filtered_stats.values()))}',
+            startangle=90,
+            explode=None,  # No separation
+            shadow=False,
+            wedgeprops=dict(edgecolor='#1a1a1a', linewidth=0.5),  # Minimal borders
+            textprops={'fontsize': 12, 'weight': 'normal', 'color': '#CCCCCC', 'fontfamily': 'monospace'}
+        )
+        
+        # Enhanced percentage text styling for better readability
+        for autotext in autotexts:
+            autotext.set_color('#000000')  # Black text for better contrast
+            autotext.set_weight('bold')
+            autotext.set_fontsize(14)
+            autotext.set_fontfamily('monospace')
+        
+        # Minimal category labels
+        for text in texts:
+            text.set_color('#AAAAAA')
+            text.set_weight('normal')
+            text.set_fontsize(13)
+            text.set_fontfamily('monospace')
+        
+        # Device label closer to chart and bigger
+        ax.set_title(device_label, 
+                    fontsize=28, weight='normal', pad=2, color='#FFFFFF', 
+                    fontfamily='monospace')
+    
+    # Create both pie charts with device labels
+    create_pie_chart(ax1, "amd", amd_filtered)
+    create_pie_chart(ax2, "nvidia", nvidia_filtered)
+    
+    # Add subtle separation line between charts - stops at device labels level
+    line_x = 0.5
+    fig.add_artist(plt.Line2D([line_x, line_x], [0.0, 0.85], 
+                              color='#333333', linewidth=1, alpha=0.5,
+                              transform=fig.transFigure))
+    
+    # Add central shared title for model name
+    fig.suptitle(f'{model_name.lower()}', 
+                fontsize=32, weight='bold', color='#CCCCCC', 
+                fontfamily='monospace', y=1)
+    
+    # Clean layout with padding and space for central title
+    plt.tight_layout()
+    plt.subplots_adjust(top=0.85, wspace=0.4)  # Added wspace for padding between charts
+    
+    # Generate failure info directly from dataframe
+    failures_amd = row.get('failures_amd', {})
+    failures_nvidia = row.get('failures_nvidia', {})
+    
+    amd_failed_info = extract_failure_info(failures_amd, 'AMD', failed_multi_amd, failed_single_amd)
+    nvidia_failed_info = extract_failure_info(failures_nvidia, 'NVIDIA', failed_multi_nvidia, failed_single_nvidia)
+    
+    return fig, amd_failed_info, nvidia_failed_info