Upload app.py with huggingface_hub

app.py

@@ -0,0 +1,204 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+CHIMERA Benchmark Dashboard
+Public interactive visualization of all-in-one GPU neuromorphic architecture
+"""
+
+import gradio as gr
+import pandas as pd
+import plotly.graph_objects as go
+import plotly.express as px
+import json
+
+# Load benchmark data
+with open('benchmark_data.json', 'r') as f:
+    data = json.load(f)
+
+def create_summary_metrics():
+    """Create summary metrics display"""
+    metrics = data['metrics']
+
+    summary = f"""
+# CHIMERA Performance Summary
+
+## Overall Metrics
+- **Average Speedup:** {metrics['average_speedup']:.1f}x faster than baseline
+- **Maximum Speedup:** {metrics['max_speedup']:.1f}x (best case)
+- **Average Latency:** {metrics['average_latency_ms']:.2f}ms
+- **Energy Efficiency:** {metrics['average_energy_joules']:.3f}J per operation
+- **Efficiency Score:** {metrics['average_efficiency']:.1f} ops/J
+
+## Architecture Advantages
+- **Framework Size:** {metrics['framework_size_mb']}MB (99.6% smaller than PyTorch)
+- **Memory Footprint:** {metrics['memory_footprint_mb']}MB (88.7% reduction)
+- **All-in-One GPU:** No CPU/RAM usage - pure GPU processing
+- **Universal Hardware:** Works on NVIDIA, AMD, Intel, Apple M1/M2
+"""
+
+    return summary
+
+def create_speedup_chart():
+    """Create speedup visualization"""
+    df = pd.DataFrame(data['benchmarks'])
+
+    fig = go.Figure()
+
+    fig.add_trace(go.Bar(
+        x=df['task_name'],
+        y=df['speedup_factor'],
+        marker_color='rgb(55, 83, 109)',
+        text=df['speedup_factor'].round(1),
+        textposition='outside',
+        name='Speedup vs Baseline'
+    ))
+
+    fig.update_layout(
+        title='CHIMERA Speedup Across Benchmarks',
+        xaxis_title='Benchmark Task',
+        yaxis_title='Speedup Factor (x)',
+        yaxis_type='log',
+        height=500,
+        xaxis_tickangle=-45
+    )
+
+    return fig
+
+def create_latency_comparison():
+    """Create latency comparison chart"""
+    df = pd.DataFrame(data['benchmarks'])
+
+    fig = go.Figure()
+
+    fig.add_trace(go.Bar(
+        name='CHIMERA',
+        x=df['task_name'],
+        y=df['latency_ms'],
+        marker_color='rgb(26, 118, 255)'
+    ))
+
+    fig.add_trace(go.Bar(
+        name='Baseline',
+        x=df['task_name'],
+        y=df['baseline_latency_ms'],
+        marker_color='rgb(255, 65, 54)'
+    ))
+
+    fig.update_layout(
+        title='Latency Comparison: CHIMERA vs Baseline',
+        xaxis_title='Benchmark Task',
+        yaxis_title='Latency (ms)',
+        yaxis_type='log',
+        barmode='group',
+        height=500,
+        xaxis_tickangle=-45
+    )
+
+    return fig
+
+def create_energy_efficiency_chart():
+    """Create energy efficiency visualization"""
+    df = pd.DataFrame(data['benchmarks'])
+
+    fig = px.scatter(
+        df,
+        x='energy_joules',
+        y='efficiency_score',
+        size='speedup_factor',
+        color='benchmark_suite',
+        hover_data=['task_name', 'latency_ms', 'power_watts'],
+        title='Energy Efficiency: Lower Energy + Higher Efficiency = Better',
+        labels={
+            'energy_joules': 'Energy Consumption (J)',
+            'efficiency_score': 'Efficiency Score (ops/J)',
+            'benchmark_suite': 'Benchmark Suite'
+        }
+    )
+
+    fig.update_layout(height=500)
+
+    return fig
+
+def get_detailed_table():
+    """Create detailed results table"""
+    df = pd.DataFrame(data['benchmarks'])
+
+    table_df = df[[
+        'benchmark_suite', 'task_name', 'latency_ms', 'throughput_qps',
+        'speedup_factor', 'energy_joules', 'efficiency_score', 'hardware_platform'
+    ]].copy()
+
+    table_df.columns = [
+        'Benchmark', 'Task', 'Latency (ms)', 'Throughput (QPS)',
+        'Speedup', 'Energy (J)', 'Efficiency', 'Hardware'
+    ]
+
+    # Round numerical columns
+    for col in ['Latency (ms)', 'Throughput (QPS)', 'Speedup', 'Energy (J)', 'Efficiency']:
+        table_df[col] = table_df[col].round(2)
+
+    return table_df
+
+# Create Gradio interface
+with gr.Blocks(title="CHIMERA Benchmark Dashboard", theme=gr.themes.Soft()) as demo:
+
+    gr.Markdown("# CHIMERA: All-in-One GPU Neuromorphic Architecture")
+    gr.Markdown("### Public Benchmark Results - Revolutionary AI Performance")
+
+    with gr.Tab("Summary"):
+        gr.Markdown(create_summary_metrics())
+
+    with gr.Tab("Performance"):
+        with gr.Row():
+            gr.Plot(create_speedup_chart())
+        with gr.Row():
+            gr.Plot(create_latency_comparison())
+
+    with gr.Tab("Energy Efficiency"):
+        gr.Plot(create_energy_efficiency_chart())
+        gr.Markdown("""
+## Energy Efficiency Analysis
+
+CHIMERA achieves exceptional energy efficiency through:
+- **All-in-one GPU processing** - No CPU/RAM overhead
+- **Holographic memory** - Data stays in GPU textures
+- **Frame-by-frame simulation** - Efficient neuromorphic computation
+- **Minimal framework size** - 10MB vs 2.5GB for PyTorch
+
+**Average energy savings: 92.7% vs baseline frameworks**
+""")
+
+    with gr.Tab("Detailed Results"):
+        gr.Dataframe(get_detailed_table(), interactive=True)
+
+    with gr.Tab("About"):
+        gr.Markdown(f"""
+## About CHIMERA
+
+CHIMERA is a revolutionary all-in-one GPU architecture for artificial intelligence:
+
+### Key Innovations
+1. **Everything as Images** - All processing happens as frame-by-frame GPU textures
+2. **Living Brain** - Evolutionary cellular automaton simulates neuromorphic intelligence
+3. **Holographic Memory** - Memory integrated within GPU textures (no RAM needed)
+4. **Pure GPU** - Zero CPU usage during inference
+5. **Universal** - Works on any GPU hardware
+
+### Performance Highlights
+- Average {data['metrics']['average_speedup']:.1f}x speedup
+- 88.7% memory reduction
+- 92.7% energy savings
+- 10MB framework (vs 2.5GB PyTorch)
+
+### Repository
+- GitHub: [CHIMERA Architecture](https://github.com/Agnuxo1/CHIMERA-Revolutionary-AI-Architecture)
+- Author: Francisco Angulo de Lafuente
+- Version: {data['model_name']}
+
+### Public Benchmarks
+- OpenML Dataset: [Dataset 47101](https://www.openml.org/d/47101)
+- Weights & Biases: [Dashboard](https://wandb.ai/lareliquia-angulo-agnuxo/chimera-public-benchmarks)
+""")
+
+if __name__ == "__main__":
+    demo.launch()