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Browse files
app.py
CHANGED
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@@ -4,14 +4,53 @@ import numpy as np
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import gradio as gr
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# Sample
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MODELS = {
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"llama"
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}
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def plot_model_stats(model_name: str) -> plt.Figure:
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"""Draws a pie chart of model's passed, failed, skipped, and error stats."""
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model_stats = MODELS[model_name]
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@@ -23,10 +62,15 @@ def plot_model_stats(model_name: str) -> plt.Figure:
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'error': '#8B0000' # Dark red
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}
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# Filter out categories with 0 values for cleaner visualization
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-
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if not
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# Handle case where all values are 0 - minimal empty state
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fig, ax = plt.subplots(figsize=(10, 8), facecolor='#000000')
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ax.set_facecolor('#000000')
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@@ -37,17 +81,27 @@ def plot_model_stats(model_name: str) -> plt.Figure:
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ax.set_xlim(0, 1)
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ax.set_ylim(0, 1)
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ax.axis('off')
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return fig
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# Create figure with two subplots side by side with padding
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 9), facecolor='#000000')
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ax1.set_facecolor('#000000')
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ax2.set_facecolor('#000000')
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# Create minimal pie chart - full pie, no donut effect
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wedges, texts, autotexts = ax.pie(
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filtered_stats.values(),
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@@ -81,18 +135,19 @@ def plot_model_stats(model_name: str) -> plt.Figure:
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fontfamily='monospace')
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# Create both pie charts with device labels
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create_pie_chart(ax1, "
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create_pie_chart(ax2, "
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# Add subtle separation line between charts
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line_x = 0.5
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fig.add_artist(plt.Line2D([line_x, line_x], [0.
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color='#333333', linewidth=1, alpha=0.5,
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transform=fig.transFigure))
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# Add central shared title for model name and test count
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fontsize=18, weight='normal', color='#CCCCCC',
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fontfamily='monospace', y=0.95)
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@@ -100,14 +155,26 @@ def plot_model_stats(model_name: str) -> plt.Figure:
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plt.tight_layout()
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plt.subplots_adjust(top=0.85, wspace=0.4) # Added wspace for padding between charts
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def get_model_stats_summary(model_name: str) -> tuple:
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"""Get summary stats for a model (total tests, success rate, status indicator)."""
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stats = MODELS[model_name]
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# Determine status indicator color
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if success_rate >= 80:
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@@ -281,6 +348,21 @@ h1, h2, h3, p, .markdown {
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padding: 20px !important;
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margin-left: 300px !important;
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}
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"""
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# Create the Gradio interface with sidebar and dark theme
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@@ -313,18 +395,39 @@ with gr.Blocks(title="Model Test Results Dashboard", css=dark_theme_css) as demo
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format="png",
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elem_classes=["plot-container"]
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)
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# Set up click handlers for each button
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for i, (model_name, button) in enumerate(zip(MODELS.keys(), model_buttons)):
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button.click(
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fn=lambda name=model_name: plot_model_stats(name),
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outputs=plot_output
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)
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# Initialize with the first model
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demo.load(
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fn=lambda: plot_model_stats(list(MODELS.keys())[0]),
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outputs=plot_output
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)
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if __name__ == "__main__":
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import gradio as gr
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# Sample test results with test names
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MODELS = {
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"llama": {
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"amd": {
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"passed": ["auth_login", "data_validation", "api_response", "file_upload", "cache_hit", "user_permissions", "db_query", "session_mgmt", "input_sanitize", "rate_limit", "error_handling", "memory_alloc", "thread_safety", "backup_restore"],
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"failed": ["network_timeout"],
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"skipped": ["gpu_accel", "cuda_ops", "ml_inference", "tensor_ops", "distributed", "multi_gpu"],
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"error": []
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},
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"nvidia": {
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"passed": ["auth_login", "data_validation", "api_response", "file_upload", "cache_hit", "user_permissions", "db_query", "session_mgmt", "input_sanitize", "rate_limit", "error_handling", "memory_alloc", "thread_safety", "backup_restore", "gpu_accel", "cuda_ops", "ml_inference", "tensor_ops"],
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"failed": ["network_timeout", "distributed"],
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"skipped": ["multi_gpu"],
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"error": []
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}
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},
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"gemma3": {
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"amd": {
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"passed": ["auth_login", "data_validation", "api_response", "file_upload", "cache_hit", "user_permissions", "db_query", "session_mgmt", "input_sanitize", "rate_limit", "error_handling", "memory_alloc", "thread_safety", "backup_restore", "config_load", "log_rotation", "health_check", "metrics", "alerts", "monitoring", "security_scan", "password_hash", "jwt_token", "oauth_flow", "csrf_protect", "xss_filter", "sql_injection", "rate_limiter", "load_balance", "circuit_break", "retry_logic", "timeout_handle", "graceful_shutdown", "hot_reload", "config_watch", "env_vars", "secrets_mgmt", "tls_cert", "encryption", "compression", "serialization", "deserialization", "validation"],
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"failed": ["gpu_accel", "cuda_ops", "ml_inference", "tensor_ops", "distributed", "multi_gpu"],
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"skipped": ["perf_test", "stress_test", "load_test", "endurance", "benchmark", "profiling", "memory_leak", "cpu_usage", "disk_io", "network_bw", "latency", "throughput"],
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"error": []
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},
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"nvidia": {
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"passed": ["auth_login", "data_validation", "api_response", "file_upload", "cache_hit", "user_permissions", "db_query", "session_mgmt", "input_sanitize", "rate_limit", "error_handling", "memory_alloc", "thread_safety", "backup_restore", "config_load", "log_rotation", "health_check", "metrics", "alerts", "monitoring", "security_scan", "password_hash", "jwt_token", "oauth_flow", "csrf_protect", "xss_filter", "sql_injection", "rate_limiter", "load_balance", "circuit_break", "retry_logic", "timeout_handle", "graceful_shutdown", "hot_reload", "config_watch", "env_vars", "secrets_mgmt", "tls_cert", "encryption", "compression", "serialization", "deserialization", "validation", "gpu_accel", "cuda_ops", "ml_inference", "tensor_ops"],
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"failed": ["distributed", "multi_gpu"],
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"skipped": ["perf_test", "stress_test", "load_test", "endurance", "benchmark", "profiling", "memory_leak", "cpu_usage", "disk_io", "network_bw"],
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"error": []
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}
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},
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"csm": {
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"amd": {
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"passed": [],
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"failed": [],
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"skipped": [],
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"error": ["system_crash"]
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},
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"nvidia": {
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"passed": [],
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"failed": [],
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"skipped": [],
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"error": ["system_crash"]
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}
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}
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}
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def plot_model_stats(model_name: str) -> tuple[plt.Figure, str, str]:
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"""Draws a pie chart of model's passed, failed, skipped, and error stats."""
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model_stats = MODELS[model_name]
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'error': '#8B0000' # Dark red
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}
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# Convert test lists to counts for chart display
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amd_stats = {k: len(v) for k, v in model_stats['amd'].items()}
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nvidia_stats = {k: len(v) for k, v in model_stats['nvidia'].items()}
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# Filter out categories with 0 values for cleaner visualization
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amd_filtered = {k: v for k, v in amd_stats.items() if v > 0}
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nvidia_filtered = {k: v for k, v in nvidia_stats.items() if v > 0}
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if not amd_filtered and not nvidia_filtered:
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# Handle case where all values are 0 - minimal empty state
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fig, ax = plt.subplots(figsize=(10, 8), facecolor='#000000')
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ax.set_facecolor('#000000')
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ax.set_xlim(0, 1)
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ax.set_ylim(0, 1)
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ax.axis('off')
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return fig, "", ""
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# Create figure with two subplots side by side with padding
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 9), facecolor='#000000')
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ax1.set_facecolor('#000000')
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ax2.set_facecolor('#000000')
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def create_pie_chart(ax, device_label, filtered_stats):
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if not filtered_stats:
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ax.text(0.5, 0.5, 'No test results',
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horizontalalignment='center', verticalalignment='center',
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transform=ax.transAxes, fontsize=14, color='#888888',
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fontfamily='monospace', weight='normal')
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ax.set_title(device_label,
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fontsize=28, weight='bold', pad=2, color='#FFFFFF',
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fontfamily='monospace')
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ax.axis('off')
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return
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chart_colors = [colors[category] for category in filtered_stats.keys()]
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# Create minimal pie chart - full pie, no donut effect
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wedges, texts, autotexts = ax.pie(
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filtered_stats.values(),
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fontfamily='monospace')
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# Create both pie charts with device labels
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create_pie_chart(ax1, "amd", amd_filtered)
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create_pie_chart(ax2, "nvidia", nvidia_filtered)
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# Add subtle separation line between charts - longer to match layout
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line_x = 0.5
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fig.add_artist(plt.Line2D([line_x, line_x], [0.05, 0.9],
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color='#333333', linewidth=1, alpha=0.5,
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transform=fig.transFigure))
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# Add central shared title for model name and test count
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total_amd = sum(amd_stats.values())
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total_nvidia = sum(nvidia_stats.values())
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fig.suptitle(f'{model_name.lower()} • amd: {total_amd} tests, nvidia: {total_nvidia} tests',
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fontsize=18, weight='normal', color='#CCCCCC',
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fontfamily='monospace', y=0.95)
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plt.tight_layout()
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plt.subplots_adjust(top=0.85, wspace=0.4) # Added wspace for padding between charts
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# Generate separate failed tests info for AMD and NVIDIA
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amd_failed = model_stats['amd']['failed']
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nvidia_failed = model_stats['nvidia']['failed']
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amd_failed_info = "\n".join(amd_failed) if amd_failed else "None"
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nvidia_failed_info = "\n".join(nvidia_failed) if nvidia_failed else "None"
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return fig, amd_failed_info, nvidia_failed_info
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def get_model_stats_summary(model_name: str) -> tuple:
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"""Get summary stats for a model (total tests, success rate, status indicator)."""
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stats = MODELS[model_name]
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# Combine AMD and NVIDIA results
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total_passed = len(stats['amd']['passed']) + len(stats['nvidia']['passed'])
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total_failed = len(stats['amd']['failed']) + len(stats['nvidia']['failed'])
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total_skipped = len(stats['amd']['skipped']) + len(stats['nvidia']['skipped'])
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total_error = len(stats['amd']['error']) + len(stats['nvidia']['error'])
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total = total_passed + total_failed + total_skipped + total_error
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success_rate = (total_passed / total * 100) if total > 0 else 0
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# Determine status indicator color
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if success_rate >= 80:
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padding: 20px !important;
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margin-left: 300px !important;
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}
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/* Failed tests display */
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.failed-tests {
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background-color: #1a1a1a !important;
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color: #CCCCCC !important;
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font-family: monospace !important;
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font-size: 12px !important;
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padding: 15px !important;
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margin-top: 20px !important;
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border-radius: 5px !important;
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border: 1px solid #333333 !important;
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max-height: 200px !important;
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overflow-y: auto !important;
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white-space: pre-line !important;
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}
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"""
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# Create the Gradio interface with sidebar and dark theme
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format="png",
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elem_classes=["plot-container"]
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)
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# Create two separate failed tests displays in a row layout
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with gr.Row():
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with gr.Column(scale=1):
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amd_failed_tests_output = gr.Textbox(
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label="AMD Failed Tests",
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value="",
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lines=8,
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max_lines=8,
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interactive=False,
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elem_classes=["failed-tests"]
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)
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with gr.Column(scale=1):
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nvidia_failed_tests_output = gr.Textbox(
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label="NVIDIA Failed Tests",
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value="",
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lines=8,
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max_lines=8,
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interactive=False,
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elem_classes=["failed-tests"]
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)
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# Set up click handlers for each button
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for i, (model_name, button) in enumerate(zip(MODELS.keys(), model_buttons)):
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button.click(
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fn=lambda name=model_name: plot_model_stats(name),
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outputs=[plot_output, amd_failed_tests_output, nvidia_failed_tests_output]
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)
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# Initialize with the first model
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demo.load(
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fn=lambda: plot_model_stats(list(MODELS.keys())[0]),
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outputs=[plot_output, amd_failed_tests_output, nvidia_failed_tests_output]
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)
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if __name__ == "__main__":
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