app.py

#!/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
    )

    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
    )

    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_name',
        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_name': 'Benchmark'
        }
    )

    fig.update_layout(height=500)

    return fig

def create_hardware_scaling_chart():
    """Create hardware scalability visualization"""
    # Filter scalability benchmarks
    scaling_df = pd.DataFrame([
        b for b in data['benchmarks']
        if 'Scalability' in b['benchmark_name']
    ])

    if len(scaling_df) == 0:
        return go.Figure().update_layout(title="No scalability data available")

    fig = go.Figure()

    for platform in scaling_df['hardware_platform'].unique():
        platform_data = scaling_df[scaling_df['hardware_platform'] == platform]

        fig.add_trace(go.Bar(
            name=platform,
            x=['Latency', 'Power'],
            y=[
                platform_data['latency_ms'].values[0],
                platform_data['power_watts'].values[0]
            ]
        ))

    fig.update_layout(
        title='Hardware Scalability: CHIMERA Performance Across Platforms',
        yaxis_title='Value',
        barmode='group',
        height=500
    )

    return fig

def get_detailed_table():
    """Create detailed results table"""
    df = pd.DataFrame(data['benchmarks'])

    table_df = df[[
        'benchmark_name', '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"):
        gr.Plot(create_speedup_chart())
        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("Hardware Scalability"):
        gr.Plot(create_hardware_scaling_chart())
        gr.Markdown("""
        ## Universal Hardware Support

        CHIMERA works on any GPU with OpenGL 4.3+:
        - NVIDIA GeForce/RTX (CUDA 11.0+)
        - AMD Radeon (OpenGL 4.6)
        - Intel UHD/Iris (OpenGL 4.5)
        - Apple M1/M2 (Metal backend)
        - Raspberry Pi 4 (OpenGL 3.3)

        **No vendor lock-in - truly universal AI acceleration**
        """)

    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

        ### Architecture Principles
        - **Neuromorphic simulation** in every frame
        - **Cellular automaton** creates emergent intelligence
        - **Holographic encoding** for efficient memory
        - **OpenGL compute shaders** for universal compatibility

        ### 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']}

        ### Citation
        ```
        @software{{chimera2025,
          title={{CHIMERA: All-in-One GPU Neuromorphic Architecture}},
          author={{Angulo de Lafuente, Francisco}},
          year={{2025}},
          url={{https://github.com/Agnuxo1/CHIMERA-Revolutionary-AI-Architecture}}
        }}
        ```
        """)

if __name__ == "__main__":
    demo.launch()