Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+
+# Define edge AI models and their benchmark metrics
+MODELS = {
+    "MobileNetV2": {"latency_ms": 25, "accuracy": 0.87, "size_mb": 14, "power_mw": 120},
+    "EfficientNet-B0": {"latency_ms": 32, "accuracy": 0.91, "size_mb": 20, "power_mw": 145},
+    "SqueezeNet": {"latency_ms": 18, "accuracy": 0.82, "size_mb": 5, "power_mw": 95},
+    "ResNet18": {"latency_ms": 45, "accuracy": 0.88, "size_mb": 44, "power_mw": 180},
+    "TinyYOLO": {"latency_ms": 38, "accuracy": 0.75, "size_mb": 34, "power_mw": 165},
+    "MobileViT-S": {"latency_ms": 28, "accuracy": 0.89, "size_mb": 18, "power_mw": 135},
+}
+
+def create_benchmark_chart(model1, model2, metric):
+    """Create comparison chart for selected models and metric"""
+    if not model1 or not model2:
+        return None, "Please select two models to compare"
+    
+    metrics_map = {
+        "Latency (ms)": "latency_ms",
+        "Accuracy (%)": "accuracy",
+        "Model Size (MB)": "size_mb",
+        "Power (mW)": "power_mw"
+    }
+    
+    metric_key = metrics_map[metric]
+    
+    m1_value = MODELS[model1][metric_key]
+    m2_value = MODELS[model2][metric_key]
+    
+    # Convert accuracy to percentage for display
+    if metric_key == "accuracy":
+        m1_value = m1_value * 100
+        m2_value = m2_value * 100
+    
+    fig = go.Figure(data=[
+        go.Bar(name=model1, x=[metric], y=[m1_value], marker_color='#4ecdc4'),
+        go.Bar(name=model2, x=[metric], y=[m2_value], marker_color='#ff6b6b')
+    ])
+    
+    fig.update_layout(
+        title=f"Edge AI Model Comparison: {metric}",
+        barmode='group',
+        height=400,
+        yaxis_title=metric,
+        showlegend=True
+    )
+    
+    winner = model1 if m1_value < m2_value and metric_key == "latency_ms" else \
+             (model1 if m1_value < m2_value and metric_key == "power_mw" else \
+             (model1 if m1_value > m2_value else model2))
+    
+    summary = f"✅ **{winner}** performs better on {metric}\n\n"
+    summary += f"**{model1}**: {m1_value:.2f}\n"
+    summary += f"**{model2}**: {m2_value:.2f}"
+    
+    return fig, summary
+
+def create_all_metrics_comparison(model1, model2):
+    """Create comprehensive comparison of all metrics"""
+    if not model1 or not model2:
+        return None, "Please select two models to compare"
+    
+    # Create subplot with 4 metrics
+    fig = make_subplots(
+        rows=2, cols=2,
+        subplot_titles=('Latency (Lower is Better)', 'Accuracy (Higher is Better)', 
+                       'Model Size (Lower is Better)', 'Power Consumption (Lower is Better)')
+    )
+    
+    # Latency
+    fig.add_trace(go.Bar(x=[model1, model2], 
+                         y=[MODELS[model1]["latency_ms"], MODELS[model2]["latency_ms"]],
+                         marker_color=['#4ecdc4', '#ff6b6b'],
+                         showlegend=False), row=1, col=1)
+    
+    # Accuracy
+    fig.add_trace(go.Bar(x=[model1, model2], 
+                         y=[MODELS[model1]["accuracy"]*100, MODELS[model2]["accuracy"]*100],
+                         marker_color=['#4ecdc4', '#ff6b6b'],
+                         showlegend=False), row=1, col=2)
+    
+    # Size
+    fig.add_trace(go.Bar(x=[model1, model2], 
+                         y=[MODELS[model1]["size_mb"], MODELS[model2]["size_mb"]],
+                         marker_color=['#4ecdc4', '#ff6b6b'],
+                         showlegend=False), row=2, col=1)
+    
+    # Power
+    fig.add_trace(go.Bar(x=[model1, model2], 
+                         y=[MODELS[model1]["power_mw"], MODELS[model2]["power_mw"]],
+                         marker_color=['#4ecdc4', '#ff6b6b'],
+                         showlegend=False), row=2, col=2)
+    
+    fig.update_layout(height=700, title_text="Complete Edge AI Model Benchmark Comparison")
+    
+    # Generate detailed comparison
+    summary = "## 📊 Complete Benchmark Analysis\n\n"
+    summary += f"### {model1} vs {model2}\n\n"
+    summary += f"**Latency**: {MODELS[model1]['latency_ms']}ms vs {MODELS[model2]['latency_ms']}ms\n"
+    summary += f"**Accuracy**: {MODELS[model1]['accuracy']*100:.1f}% vs {MODELS[model2]['accuracy']*100:.1f}%\n"
+    summary += f"**Model Size**: {MODELS[model1]['size_mb']}MB vs {MODELS[model2]['size_mb']}MB\n"
+    summary += f"**Power**: {MODELS[model1]['power_mw']}mW vs {MODELS[model2]['power_mw']}mW\n"
+    
+    return fig, summary
+
+# Create Gradio interface
+with gr.Blocks(title="Edge AI Model Benchmark - Anktechsol", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🚀 Edge AI Model Benchmark Tool
+    ### by **Anktechsol** - AI + IoT Experts
+    
+    Compare performance metrics of popular Edge AI models for deployment on IoT devices, edge gateways, and embedded systems.
+    Perfect for selecting the right model for your AIoT applications!
+    """)
+    
+    with gr.Tabs():
+        with gr.Tab("📊 Single Metric Comparison"):
+            with gr.Row():
+                with gr.Column():
+                    model1_single = gr.Dropdown(choices=list(MODELS.keys()), label="Model 1", value="MobileNetV2")
+                    model2_single = gr.Dropdown(choices=list(MODELS.keys()), label="Model 2", value="EfficientNet-B0")
+                    metric_select = gr.Dropdown(
+                        choices=["Latency (ms)", "Accuracy (%)", "Model Size (MB)", "Power (mW)"],
+                        label="Select Metric",
+                        value="Latency (ms)"
+                    )
+                    compare_btn = gr.Button("🔍 Compare Models", variant="primary")
+                
+                with gr.Column():
+                    plot_single = gr.Plot(label="Comparison Chart")
+                    summary_single = gr.Markdown()
+            
+            compare_btn.click(
+                fn=create_benchmark_chart,
+                inputs=[model1_single, model2_single, metric_select],
+                outputs=[plot_single, summary_single]
+            )
+        
+        with gr.Tab("📊📊 All Metrics Comparison"):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    model1_all = gr.Dropdown(choices=list(MODELS.keys()), label="Model 1", value="MobileNetV2")
+                    model2_all = gr.Dropdown(choices=list(MODELS.keys()), label="Model 2", value="SqueezeNet")
+                    compare_all_btn = gr.Button("🔍 Compare All Metrics", variant="primary", size="lg")
+                    gr.Markdown("""
+                    ---
+                    ### 🔗 Resources
+                    - [Anktechsol Website](https://anktechsol.com)
+                    - [More AIoT Tools](https://huggingface.co/anktechsol)
+                    """)
+                
+                with gr.Column(scale=3):
+                    plot_all = gr.Plot(label="Complete Benchmark")
+                    summary_all = gr.Markdown()
+            
+            compare_all_btn.click(
+                fn=create_all_metrics_comparison,
+                inputs=[model1_all, model2_all],
+                outputs=[plot_all, summary_all]
+            )
+    
+    # Auto-load default comparison
+    demo.load(
+        fn=create_all_metrics_comparison,
+        inputs=[gr.State("MobileNetV2"), gr.State("EfficientNet-B0")],
+        outputs=[plot_all, summary_all]
+    )
+
+if __name__ == "__main__":
+    demo.launch()