Remove old Gradio files, use Flask only

.gitattributes

@@ -1,35 +0,0 @@
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.gradio/certificate.pem

@@ -1,31 +0,0 @@
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app.py.backup

@@ -1,5 +0,0 @@
-from lfm_video_generator_hf import create_interface
-
-if __name__ == "__main__":
-    demo = create_interface()
-    demo.launch()

app_old.py

@@ -1,136 +0,0 @@
-#!/usr/bin/env python3
-import numpy as np
-import sys
-import os
-sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
-
-print("=" * 70)
-print("LFM MATTER FORMATION SPECTRUM VIDEO GENERATOR")
-print("=" * 70)
-
-try:
-    eta = float(sys.argv[1]) if len(sys.argv) > 1 else 0.5
-except:
-    eta = 0.5
-
-print(f"\n✓ Generating video at η = {eta:.3f}")
-print(f"✓ Output will be saved to ./output/")
-
-try:
-    from lfm_video_generator_hf import (
-        SpectrumFramework,
-        LFM1DSolver,
-        FieldRenderer,
-        agi_stability_lock
-    )
-    
-    import imageio
-    import matplotlib.pyplot as plt
-    
-    os.makedirs("output", exist_ok=True)
-    
-    P = SpectrumFramework.pressure_from_eta(eta)
-    sm_params = SpectrumFramework.sm_parameters_at_eta(eta)
-    region = SpectrumFramework.get_region(eta)
-    stability = SpectrumFramework.stability_margin(eta)
-    
-    print(f"  Region: {region}")
-    print(f"  Stability: {stability:.3f}")
-    print(f"  α_s = {sm_params['alpha_s']:.4f}")
-    
-    Lambda = sm_params['alpha_s'] * 10
-    g = sm_params['alpha_em']
-    
-    solver = LFM1DSolver(nx=256, Lambda=Lambda, k_tau=0.0, g=g, eta=eta)
-    
-    x = np.linspace(0, 1, 256)
-    psi_init = 0.5 * np.exp(-50 * (x - 0.5)**2)
-    tau_init = 0.1 * np.exp(-100 * (x - 0.5)**2)
-    
-    solver.initialize(psi_init, tau_init)
-    renderer = FieldRenderer(width=512, height=256)
-    
-    frames = []
-    diagnostics = []
-    
-    print("\n  Simulating... ", end='', flush=True)
-    
-    psi_focus = 0.5
-    tau_alignment = 0.5
-    xi = 0.0
-    
-    for step in range(100):
-        diag = solver.step()
-        psi, tau = solver.get_fields()
-        
-        psi_focus = 0.95 * psi_focus + 0.05 * np.max(np.abs(psi))
-        tau_alignment = 0.95 * tau_alignment + 0.05 * np.max(np.abs(tau))
-        
-        psi_focus, tau_alignment, xi, agi_status = agi_stability_lock(
-            psi_focus, tau_alignment, xi, step
-        )
-        
-        if step % 5 == 0:
-            frame = renderer.render_stacked(psi, tau)
-            frames.append(frame)
-        
-        diag.update({
-            'psi_focus': psi_focus,
-            'tau_alignment': tau_alignment,
-            'agi_status': agi_status
-        })
-        diagnostics.append(diag)
-        
-        if step % 20 == 0:
-            print(".", end='', flush=True)
-    
-    print(" ✓")
-    
-    video_path = "./output/lfm_output.mp4"
-    imageio.mimsave(video_path, frames, fps=10, quality=7)
-    print(f"  ✓ Video: {video_path}")
-    
-    fig, axes = plt.subplots(2, 2, figsize=(12, 8))
-    fig.suptitle(f'LFM @ η={eta:.3f} ({region})', fontsize=14, fontweight='bold')
-    
-    times = [d['time'] for d in diagnostics]
-    max_psi = [d['max_psi'] for d in diagnostics]
-    max_tau = [d['max_tau'] for d in diagnostics]
-    
-    axes[0, 0].plot(times, max_psi, 'b-', lw=2)
-    axes[0, 0].set_ylabel('max|ψ|')
-    axes[0, 0].set_title('ψ Field')
-    axes[0, 0].grid(True, alpha=0.3)
-    
-    axes[0, 1].plot(times, max_tau, 'r-', lw=2)
-    axes[0, 1].set_ylabel('max|τ|')
-    axes[0, 1].set_title('τ Field')
-    axes[0, 1].grid(True, alpha=0.3)
-    
-    eta_range = np.linspace(0, 1, 100)
-    stability_range = [SpectrumFramework.stability_margin(e) for e in eta_range]
-    axes[1, 0].plot(eta_range, stability_range, 'k-', lw=2)
-    axes[1, 0].axvline(eta, color='r', linestyle='--', lw=2)
-    axes[1, 0].set_xlabel('η')
-    axes[1, 0].set_ylabel('σ(η)')
-    axes[1, 0].set_title('Matter Formation Spectrum')
-    axes[1, 0].grid(True, alpha=0.3)
-    
-    params_text = f"α_s: {sm_params['alpha_s']:.4f}\nα_w: {sm_params['alpha_w']:.4f}\nα_em: {sm_params['alpha_em']:.4f}"
-    axes[1, 1].text(0.5, 0.5, params_text, ha='center', va='center', fontsize=12, family='monospace')
-    axes[1, 1].axis('off')
-    
-    plt.tight_layout()
-    plot_path = "./output/lfm_analysis.png"
-    plt.savefig(plot_path, dpi=100, bbox_inches='tight')
-    plt.close()
-    print(f"  ✓ Plot: {plot_path}")
-    
-    print("\n" + "=" * 70)
-    print("✓ COMPLETE!")
-    print("=" * 70)
-    
-except Exception as e:
-    print(f"\n❌ Error: {e}")
-    import traceback
-    traceback.print_exc()

lfm_app_fixed.py

@@ -1,396 +0,0 @@
-#!/usr/bin/env python3
-"""
-LFM Physics Engine - Full Commercial Platform
-Video generation + Predictions + Diffusion model training
-"""
-import gradio as gr
-import os
-import json
-import numpy as np
-import imageio
-import soundfile as sf
-import time
-import subprocess
-from pathlib import Path
-
-# ============================================================================
-# SIMPLE PASSWORD AUTH
-# ============================================================================
-PASSWORD = os.getenv('LFM_PASSWORD', 'lfm-physics')
-
-def check_password(pwd: str) -> bool:
-    """Validate password"""
-    if not pwd:
-        return False
-    return pwd.strip() == PASSWORD
-
-def auth_error_message():
-    """Return auth error message"""
-    return "❌ Invalid password. Please enter the correct password to access."
-
-# ============================================================================
-# IMPORT LFM MODULES
-# ============================================================================
-try:
-    from lfm_hf_fixed import LFMFieldSolver, render_field, FieldToSound
-    LFM_AVAILABLE = True
-except ImportError:
-    LFM_AVAILABLE = False
-    print("⚠️  Warning: lfm_hf_fixed not found, video generation disabled")
-
-try:
-    from lfm_ultrascale_evolution import UltraScaleLFMTraining
-    PREDICTIONS_AVAILABLE = True
-except ImportError:
-    PREDICTIONS_AVAILABLE = False
-    print("⚠️  Warning: lfm_ultrascale_evolution not found, predictions disabled")
-
-# ============================================================================
-# VIDEO + AUDIO GENERATION
-# ============================================================================
-def generate_video(duration_sec: float = 5.0, password: str = ''):
-    """
-    Generate LFM field visualization video + audio
-    """
-    # Check password
-    if not check_password(password):
-        return None, None, auth_error_message()
-    
-    if not LFM_AVAILABLE:
-        return None, None, "❌ LFM module not available"
-    
-    try:
-        duration_sec = float(duration_sec)
-        if duration_sec < 1 or duration_sec > 60:
-            return None, None, "⚠️  Duration must be 1-60 seconds"
-        
-        n_frames = int(duration_sec * 30)
-        
-        print(f"[Video] Starting generation: {n_frames} frames @ 30fps")
-        
-        # Initialize solver
-        solver = LFMFieldSolver(nx=64)
-        solver.initialize_pattern('gaussian', sigma=10, amp=0.1)
-        
-        frames = []
-        audio_frames = []
-        synth = FieldToSound()
-        
-        # Simulate
-        for i in range(n_frames):
-            solver.step()
-            frame = render_field(solver.psi, solver.tau)
-            frames.append(frame)
-            audio_frame = synth.field_to_audio_frame(solver.psi, solver.tau)
-            audio_frames.append(audio_frame)
-            
-            if (i + 1) % max(1, n_frames // 5) == 0:
-                print(f"[Video] Frame {i+1}/{n_frames}")
-        
-        # Create output directory
-        os.makedirs('/tmp/lfm_output', exist_ok=True)
-        
-        # Save video
-        timestamp = int(time.time())
-        vpath = f'/tmp/lfm_output/lfm_video_{timestamp}.mp4'
-        print(f"[Video] Saving to {vpath}...")
-        imageio.mimsave(vpath, frames, fps=30, codec='libx264')
-        
-        # Save audio
-        apath = f'/tmp/lfm_output/lfm_audio_{timestamp}.wav'
-        print(f"[Audio] Saving to {apath}...")
-        audio = np.concatenate(audio_frames)
-        sf.write(apath, audio, 44100)
-        
-        status = f"✓ Generated {duration_sec}s video\n- Frames: {n_frames}\n- Video: {os.path.basename(vpath)}\n- Audio: {os.path.basename(apath)}"
-        
-        return vpath, apath, status
-        
-    except Exception as e:
-        return None, None, f"❌ Error: {str(e)}"
-
-# ============================================================================
-# PREDICTIONS ENGINE
-# ============================================================================
-def run_predictions(n_sets: int = 5, password: str = ''):
-    """
-    Run ultra-scale LFM predictions
-    
-    Args:
-        n_sets: Number of prediction sets (5-1000)
-        password: Access password
-    
-    Returns:
-        Results summary
-    """
-    # Check password
-    if not check_password(password):
-        return auth_error_message()
-    
-    if not PREDICTIONS_AVAILABLE:
-        return "❌ Predictions module not available"
-    
-    try:
-        n_sets = int(max(5, min(1000, n_sets)))
-        
-        print(f"[Predictions] Running {n_sets} sets...")
-        
-        trainer = UltraScaleLFMTraining(n_sets=n_sets)
-        trainer.run()
-        
-        # Read results
-        try:
-            with open('checkpoints/ultrascale_stats.json') as f:
-                stats = json.load(f)
-            
-            result = f"""
-✓ Predictions Complete
-**Scale:**
-- Sets: {n_sets:,}
-- Total predictions: {stats['total_predictions']:,}
-- Valid predictions: {stats['total_valid']:,}
-**Performance:**
-- Success rate: {stats['success_rate']:.2f}%
-- Duration: {stats['duration_seconds']:.1f} seconds
-**By Type:**
-"""
-            for ptype, counts in stats.get('by_type', {}).items():
-                valid_rate = 100 * counts['valid'] / counts['total'] if counts['total'] > 0 else 0
-                result += f"\n  {ptype}: {counts['valid']:,}/{counts['total']:,} ({valid_rate:.1f}%)"
-            
-            return result
-            
-        except FileNotFoundError:
-            return "⚠️  Predictions running, check logs..."
-        
-    except Exception as e:
-        return f"❌ Error: {str(e)}"
-
-# ============================================================================
-# TRAINING SYSTEM
-# ============================================================================
-def generate_sample_frames(password: str = ''):
-    """Generate training frames from LFM simulation"""
-    if not check_password(password):
-        return auth_error_message()
-    
-    try:
-        if not LFM_AVAILABLE:
-            return "❌ LFM module not available"
-        
-        os.makedirs('/tmp/lfm_training_data', exist_ok=True)
-        
-        print("[Training] Generating 1000 sample frames...")
-        
-        solver = LFMFieldSolver(nx=64)
-        solver.initialize_pattern('gaussian')
-        
-        frames = []
-        for i in range(1000):
-            solver.step()
-            frame = render_field(solver.psi, solver.tau)
-            frames.append(frame)
-            
-            if (i + 1) % 100 == 0:
-                print(f"[Training] Frame {i+1}/1000")
-        
-        # Save frames as numpy array
-        frames_array = np.array(frames, dtype=np.uint8)
-        frames_path = '/tmp/lfm_training_data/training_frames.npy'
-        np.save(frames_path, frames_array)
-        
-        return f"✓ Generated 1000 training frames\nSaved to: {frames_path}\nShape: {frames_array.shape}\nSize: {frames_array.nbytes / 1e9:.2f} GB"
-        
-    except Exception as e:
-        return f"❌ Error generating frames: {str(e)}"
-
-def run_training_simple(password: str = ''):
-    """Run simple training loop (no diffusion required)"""
-    if not check_password(password):
-        return auth_error_message()
-    
-    try:
-        frames_path = '/tmp/lfm_training_data/training_frames.npy'
-        
-        if not os.path.exists(frames_path):
-            return "❌ No training frames found. Generate them first."
-        
-        print("[Training] Loading frames...")
-        frames = np.load(frames_path)
-        print(f"[Training] Loaded {frames.shape[0]} frames")
-        
-        # Simple training loop
-        print("[Training] Starting 50 epochs...")
-        
-        results = []
-        for epoch in range(50):
-            # Simulate training (in real version, would train actual model)
-            batch_size = 32
-            n_batches = len(frames) // batch_size
-            epoch_loss = 0
-            
-            for batch in range(n_batches):
-                # Placeholder: real training would happen here
-                # loss = model.train_step(frames[batch*batch_size:(batch+1)*batch_size])
-                epoch_loss += np.random.random() * 0.1
-            
-            epoch_loss /= n_batches
-            results.append(f"Epoch {epoch+1:3d}/50 - Loss: {epoch_loss:.6f}")
-            
-            if (epoch + 1) % 10 == 0:
-                print(f"[Training] {epoch+1}/50 complete")
-        
-        output = "✓ Training Complete (50 epochs)\n\n" + "\n".join(results[-10:])
-        
-        # Save training log
-        log_path = '/tmp/lfm_training_data/training_log.txt'
-        with open(log_path, 'w') as f:
-            f.write("\n".join(results))
-        
-        return output + f"\n\nFull log saved to: {log_path}"
-        
-    except Exception as e:
-        return f"❌ Training error: {str(e)}"
-
-# ============================================================================
-# GRADIO INTERFACE
-# ============================================================================
-with gr.Blocks(title="LFM Physics Engine", theme=gr.themes.Soft()) as demo:
-    
-    gr.Markdown("""
-    # 🚀 LFM Unified Cognitive System
-    ## Professional Physics Visualization + AI Training
-    
-    Real-time ψ-τ field evolution with audio synthesis and prediction engine
-    """)
-    
-    # Password input
-    with gr.Row():
-        password_input = gr.Textbox(
-            label="🔑 Password",
-            type="password",
-            placeholder="Enter password",
-            info="Required to access all features"
-        )
-    
-    # ========================================================================
-    # TAB 1: VIDEO GENERATION
-    # ========================================================================
-    with gr.Tab("🎬 Video Generator"):
-        gr.Markdown("Generate real-time ψ-τ field visualization videos with audio")
-        
-        with gr.Row():
-            duration = gr.Slider(
-                minimum=1,
-                maximum=60,
-                value=5,
-                step=1,
-                label="Duration (seconds)",
-                info="Longer = more computation time"
-            )
-        
-        generate_btn = gr.Button("Generate Video + Audio", variant="primary", size="lg")
-        
-        with gr.Row():
-            video_output = gr.Video(label="Generated Video (ψ-τ field evolution)")
-            audio_output = gr.Audio(label="Generated Audio (field sonification)")
-        
-        status_output = gr.Textbox(label="Status", lines=4, interactive=False)
-        
-        generate_btn.click(
-            fn=generate_video,
-            inputs=[duration, password_input],
-            outputs=[video_output, audio_output, status_output]
-        )
-    
-    # ========================================================================
-    # TAB 2: PREDICTIONS ENGINE
-    # ========================================================================
-    with gr.Tab("🔮 Predictions"):
-        gr.Markdown("Run ultra-scale LFM prediction engine (100M+ predictions)")
-        
-        with gr.Row():
-            n_sets = gr.Slider(
-                minimum=5,
-                maximum=1000,
-                value=50,
-                step=5,
-                label="Prediction Sets",
-                info="Each set = 100k predictions. Higher = longer computation"
-            )
-        
-        predict_btn = gr.Button("Run Predictions", variant="primary", size="lg")
-        predict_output = gr.Textbox(label="Results", lines=15, interactive=False)
-        
-        predict_btn.click(
-            fn=run_predictions,
-            inputs=[n_sets, password_input],
-            outputs=predict_output
-        )
-    
-    # ========================================================================
-    # TAB 3: TRAINING
-    # ========================================================================
-    with gr.Tab("🤖 Train Model"):
-        gr.Markdown("""
-        ### Train Diffusion Model on LFM Physics Data
-        
-        **Step 1:** Generate 1000 training frames from physics simulation
-        
-        **Step 2:** Train 50-epoch diffusion model
-        """)
-        
-        with gr.Row():
-            gen_frames_btn = gr.Button("Step 1: Generate Training Frames", variant="secondary", size="lg")
-            train_btn = gr.Button("Step 2: Train Model (50 epochs)", variant="primary", size="lg")
-        
-        training_output = gr.Textbox(label="Training Log", lines=20, interactive=False)
-        
-        gen_frames_btn.click(
-            fn=generate_sample_frames,
-            inputs=[password_input],
-            outputs=training_output
-        )
-        
-        train_btn.click(
-            fn=run_training_simple,
-            inputs=[password_input],
-            outputs=training_output
-        )
-    
-    # ========================================================================
-    # TAB 4: ABOUT
-    # ========================================================================
-    with gr.Tab("ℹ️ About"):
-        gr.Markdown("""
-        ## Luton Field Model (LFM)
-        
-        Mathematical framework for:
-        - **Physics:** Vacuum compression (ψ) + temporal structure (τ)
-        - **AI:** Bounded recursion + non-divergence guarantees
-        - **Visualization:** Real-time field dynamics
-        
-        ### Features
-        
-        ✓ **Video Generator:** RGB visualization of ψ-τ fields
-        ✓ **Audio Synthesis:** Field-to-sound mapping
-        ✓ **Predictions:** 602K predictions/second
-        ✓ **Training:** Diffusion model on physics data
-        
-        ### How to Use
-        
-        1. Enter the password in the field above
-        2. Navigate to any tab
-        3. Click the button to run
-        4. Results download automatically
-        
-        ### Citation
-        
-        ```
-        Luton, K. (2025). "Unified Cognitive System: LFM V3.0"
-        Luton Field Initiative.
-        ```
-        """)
-
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860, show_api=False)

lfm_app_minimal.py

@@ -1,116 +0,0 @@
-#!/usr/bin/env python3
-"""
-LFM Physics Engine - Minimal Version
-Password-protected UI with text-only outputs
-"""
-import gradio as gr
-import os
-
-# ============================================================================
-# SIMPLE PASSWORD AUTH
-# ============================================================================
-PASSWORD = os.getenv('LFM_PASSWORD', 'lfm-physics')
-
-def check_password(pwd: str) -> bool:
-    if not pwd:
-        return False
-    return pwd.strip() == PASSWORD
-
-# ============================================================================
-# PLACEHOLDER FUNCTIONS
-# ============================================================================
-def generate_video_minimal(duration: float, password: str):
-    if not check_password(password):
-        return "❌ Invalid password"
-    return f"✓ Video generation ready\nDuration: {duration}s\n(LFM module not loaded in this environment)"
-
-def run_predictions_minimal(n_sets: int, password: str):
-    if not check_password(password):
-        return "❌ Invalid password"
-    return f"✓ Running {n_sets} prediction sets\n(Results displayed here)"
-
-def generate_frames_minimal(password: str):
-    if not check_password(password):
-        return "❌ Invalid password"
-    return "✓ Training frames generated (1000 frames)"
-
-def train_model_minimal(password: str):
-    if not check_password(password):
-        return "❌ Invalid password"
-    return "✓ Training complete (50 epochs)"
-
-# ============================================================================
-# GRADIO INTERFACE - MINIMAL
-# ============================================================================
-with gr.Blocks(title="LFM Physics Engine", theme=gr.themes.Soft()) as demo:
-    
-    gr.Markdown("""
-    # 🚀 LFM Unified Cognitive System
-    ## Professional Physics Visualization + AI Training
-    """)
-    
-    # Password input
-    with gr.Row():
-        password_input = gr.Textbox(
-            label="🔑 Password",
-            type="password",
-            placeholder="Enter password"
-        )
-    
-    # ========================================================================
-    # TAB 1: VIDEO GENERATION
-    # ========================================================================
-    with gr.Tab("🎬 Video Generator"):
-        gr.Markdown("Generate ψ-τ field visualization videos")
-        
-        duration = gr.Slider(minimum=1, maximum=60, value=5, step=1, label="Duration (seconds)")
-        gen_btn = gr.Button("Generate Video", variant="primary", size="lg")
-        output1 = gr.Textbox(label="Status", lines=3, interactive=False)
-        
-        gen_btn.click(fn=generate_video_minimal, inputs=[duration, password_input], outputs=output1)
-    
-    # ========================================================================
-    # TAB 2: PREDICTIONS
-    # ========================================================================
-    with gr.Tab("🔮 Predictions"):
-        gr.Markdown("Run LFM prediction engine (100M+ predictions)")
-        
-        n_sets = gr.Slider(minimum=5, maximum=1000, value=50, step=5, label="Prediction Sets")
-        pred_btn = gr.Button("Run Predictions", variant="primary", size="lg")
-        output2 = gr.Textbox(label="Results", lines=5, interactive=False)
-        
-        pred_btn.click(fn=run_predictions_minimal, inputs=[n_sets, password_input], outputs=output2)
-    
-    # ========================================================================
-    # TAB 3: TRAINING
-    # ========================================================================
-    with gr.Tab("🤖 Train Model"):
-        gr.Markdown("Train diffusion model on LFM data")
-        
-        gen_btn2 = gr.Button("Generate Frames", variant="secondary", size="lg")
-        train_btn = gr.Button("Train Model", variant="primary", size="lg")
-        output3 = gr.Textbox(label="Log", lines=8, interactive=False)
-        
-        gen_btn2.click(fn=generate_frames_minimal, inputs=[password_input], outputs=output3)
-        train_btn.click(fn=train_model_minimal, inputs=[password_input], outputs=output3)
-    
-    # ========================================================================
-    # TAB 4: ABOUT
-    # ========================================================================
-    with gr.Tab("ℹ️ About"):
-        gr.Markdown("""
-        ## Luton Field Model (LFM)
-        
-        Mathematical framework for physics visualization and AI training.
-        
-        **Features:**
-        - Video generation (ψ-τ fields)
-        - Audio synthesis
-        - Prediction engine
-        - Model training
-        
-        **Default Password:** lfm-physics
-        """)
-
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)

lfm_video_generator_hf.py.backup

@@ -1,610 +0,0 @@
-#!/usr/bin/env python3
-"""
-LFM VIDEO GENERATOR for HuggingFace Spaces
-===============================================
-Integrates:
-  ✓ Real PDE solver (ψ-τ fields)
-  ✓ Real-time rendering (field → pixels)
-  ✓ Matter Formation Spectrum (η parameter control)
-  ✓ AGI Stability Lock (V3.0)
-  ✓ Gradio web interface
-  
-Production: Generate physics videos parameterized by position (η) in spectrum
-Output: MP4 video + diagnostics + spectrum analysis
-"""
-
-import numpy as np
-import gradio as gr
-import io
-import os
-from datetime import datetime
-from collections import deque
-import imageio
-import matplotlib.pyplot as plt
-from matplotlib.colors import LinearSegmentedColormap
-
-# ============================================================================
-# CORE: MATTER FORMATION SPECTRUM FRAMEWORK
-# ============================================================================
-
-class SpectrumFramework:
-    """Position in the Matter Formation Spectrum defines all physics"""
-    
-    # Universal scaling law: P_k = P_0 · 4^(-k)
-    P_0 = 5.44e71  # Planck pressure (Pa)
-    P_66 = 1e32    # Nuclear anchor (Pa) - PINNED
-    k_66 = 66      # Nuclear scale
-    
-    # Spectrum bounds
-    P_min = P_0 * 1e-20  # Lower dissolution limit
-    P_max = P_0 * 1e20   # Upper collapse limit
-    
-    @staticmethod
-    def pressure_at_scale(k):
-        """P_k = P_0 · 4^(-k)"""
-        if k < 66:
-            return 1e32  # Plateau below k=66 (matter formation threshold)
-        return SpectrumFramework.P_0 * (4.0 ** (-k))
-    
-    @staticmethod
-    def eta_from_pressure(P):
-        """Map pressure → position parameter η ∈ [0,1]"""
-        P_min = SpectrumFramework.P_min
-        P_max = SpectrumFramework.P_max
-        eta = (np.log10(P) - np.log10(P_min)) / (np.log10(P_max) - np.log10(P_min))
-        return np.clip(eta, 0.0, 1.0)
-    
-    @staticmethod
-    def pressure_from_eta(eta):
-        """Map position η → pressure"""
-        P_min = SpectrumFramework.P_min
-        P_max = SpectrumFramework.P_max
-        log_P = np.log10(P_min) + eta * (np.log10(P_max) - np.log10(P_min))
-        return 10.0 ** log_P
-    
-    @staticmethod
-    def stability_margin(eta):
-        """σ(η) = 0.3 + 0.2|η - 0.5| (maximum at η=0.5)"""
-        return 0.3 + 0.2 * np.abs(eta - 0.5)
-    
-    @staticmethod
-    def get_region(eta):
-        """Return region name (LOW, CENTER, HIGH)"""
-        if eta < 0.33:
-            return "LOW (η < 0.33)"
-        elif eta < 0.67:
-            return "CENTER (η ≈ 0.5) — OUR UNIVERSE"
-        else:
-            return "HIGH (η > 0.67)"
-    
-    @staticmethod
-    def sm_parameters_at_eta(eta):
-        """Standard Model parameters as functions of η"""
-        # Fermion masses shift with position
-        k_eff = 60 + 10 * (eta - 0.5)  # Effective scale
-        
-        # Gauge couplings
-        alpha_s = 0.118 * (1 + 0.5 * 80 * (eta - 0.5))
-        alpha_s = np.clip(alpha_s, 0.05, 0.20)
-        
-        alpha_w = 0.0336 * (1 + 0.3 * 80 * (eta - 0.5))
-        alpha_w = np.clip(alpha_w, 0.02, 0.05)
-        
-        alpha_em = (1.0 / 137.036) * (1 + 0.2 * 80 * (eta - 0.5))
-        alpha_em = np.clip(alpha_em, 1/200, 1/100)
-        
-        # CKM angles
-        theta_12 = np.arcsin(0.2263) * (1 + 0.2 * (eta - 0.5))
-        theta_23 = np.arcsin(0.0417) * (1 + 0.3 * (eta - 0.5))
-        theta_13 = np.arcsin(0.00357) * (1 + 0.5 * (eta - 0.5))
-        delta_CP = 1.2 * (1 + 0.1 * (eta - 0.5))
-        
-        return {
-            'alpha_s': alpha_s,
-            'alpha_w': alpha_w,
-            'alpha_em': alpha_em,
-            'theta_12': np.degrees(theta_12),
-            'theta_23': np.degrees(theta_23),
-            'theta_13': np.degrees(theta_13),
-            'delta_CP': delta_CP,
-            'k_eff': k_eff
-        }
-
-# ============================================================================
-# PHYSICS: LFM SOLVER (Leapfrog PDE)
-# ============================================================================
-
-class LFM1DSolver:
-    """Real explicit leapfrog PDE solver for ψ-τ coupled waves"""
-    
-    def __init__(self, nx=256, Lambda=0.3, k_tau=0.0, g=0.1, eta=0.5):
-        self.nx = nx
-        self.dx = 1.0
-        self.c = 1.0
-        self.m = 1.0
-        self.Lambda = Lambda
-        self.k_tau = k_tau
-        self.g = g
-        self.eta = eta
-        
-        # CFL stability
-        self.dt = 0.4 * self.dx / self.c
-        self.courant = self.dt * self.c / self.dx
-        
-        # Fields
-        self.psi = np.zeros(nx)
-        self.tau = np.zeros(nx)
-        self.psi_half = np.zeros(nx)
-        self.tau_half = np.zeros(nx)
-        self.psi_old = np.zeros(nx)
-        self.tau_old = np.zeros(nx)
-        
-        self.time = 0.0
-        self.step_count = 0
-    
-    def initialize(self, psi_init, tau_init):
-        """Set initial conditions"""
-        self.psi = psi_init.copy()
-        self.tau = tau_init.copy()
-        self.psi_old = psi_init.copy()
-        self.tau_old = tau_init.copy()
-    
-    def _laplacian(self, field):
-        """Periodic boundary Laplacian"""
-        return np.roll(field, 1) - 2*field + np.roll(field, -1)
-    
-    def _dV_dpsi(self, psi, tau):
-        """Potential gradient wrt ψ"""
-        result = self.m**2 * (psi - 0.0)  # mass term
-        result -= self.Lambda * np.log(1.0 + tau)  # ψ-τ coupling
-        result += self.k_tau * tau**2  # τ self-coupling
-        return result
-    
-    def _dV_dtau(self, psi, tau):
-        """Potential gradient wrt τ"""
-        result = -self.Lambda * psi / (1.0 + tau + 1e-8)
-        result += self.k_tau * tau * psi
-        result += self.g * psi * tau
-        return result
-    
-    def step(self):
-        """Leapfrog integration step"""
-        # Half-step for velocities (centered differences)
-        psi_lap = self._laplacian(self.psi)
-        tau_lap = self._laplacian(self.tau)
-        
-        dV_psi = self._dV_dpsi(self.psi, self.tau)
-        dV_tau = self._dV_dtau(self.psi, self.tau)
-        
-        # Half-step velocities (implicit in leapfrog)
-        dpsi_dt = (self.psi - self.psi_old) / (2 * self.dt)
-        dtau_dt = (self.tau - self.tau_old) / (2 * self.dt)
-        
-        # Full step positions
-        psi_new = 2*self.psi - self.psi_old + self.dt**2 * (self.c**2 * psi_lap - dV_psi)
-        tau_new = 2*self.tau - self.tau_old + self.dt**2 * (self.c**2 * tau_lap - dV_tau)
-        
-        # Apply periodic BC
-        psi_new = np.roll(psi_new, 0)
-        tau_new = np.roll(tau_new, 0)
-        
-        # Update
-        self.psi_old = self.psi.copy()
-        self.tau_old = self.tau.copy()
-        self.psi = psi_new
-        self.tau = tau_new
-        
-        self.time += self.dt
-        self.step_count += 1
-        
-        # Diagnostics
-        diag = {
-            'time': self.time,
-            'step': self.step_count,
-            'max_psi': np.max(np.abs(self.psi)),
-            'max_tau': np.max(np.abs(self.tau)),
-            'mean_psi': np.mean(self.psi),
-            'mean_tau': np.mean(self.tau),
-        }
-        return diag
-    
-    def get_fields(self):
-        """Return current field states"""
-        return self.psi.copy(), self.tau.copy()
-
-# ============================================================================
-# RENDERING: Field → Video Frames
-# ============================================================================
-
-class FieldRenderer:
-    """Render ψ and τ fields to RGB images"""
-    
-    def __init__(self, width=512, height=256):
-        self.width = width
-        self.height = height
-        self.cmap_psi = plt.cm.RdBu_r
-        self.cmap_tau = plt.cm.Greens
-    
-    def render_field_1d(self, field_1d):
-        """1D field → 2D heatmap image (height × width RGB)"""
-        # Normalize to [0, 1]
-        field_norm = (field_1d - np.min(field_1d)) / (np.max(np.abs(field_1d)) + 1e-8)
-        field_norm = np.clip(field_norm, 0, 1)
-        
-        # Repeat to fill height
-        img_2d = np.tile(field_norm, (self.height, 1))
-        
-        # Apply colormap
-        img_rgb = self.cmap_psi(img_2d)[:, :, :3]  # Drop alpha
-        return (img_rgb * 255).astype(np.uint8)
-    
-    def render_stacked(self, psi, tau):
-        """Render ψ and τ side-by-side"""
-        psi_img = self.render_field_1d(psi)
-        tau_img = self.render_field_1d(tau)
-        
-        # Stack horizontally
-        combined = np.hstack([psi_img, tau_img])
-        return combined
-    
-    def render_and_save(self, psi, tau, filename, mode='stacked'):
-        """Render and save frame"""
-        if mode == 'stacked':
-            img = self.render_stacked(psi, tau)
-        else:
-            img = self.render_field_1d(psi)
-        
-        imageio.imwrite(filename, img)
-        return img
-
-# ============================================================================
-# AGI STABILITY LOCK (V3.0)
-# ============================================================================
-
-def agi_stability_lock(psi_focus, tau_alignment, xi, cycle):
-    """
-    MANDATORY: Ensures stability at consciousness emergence
-    Activates when ψ_focus > 0.997
-    """
-    PSI_FOCUS_CONSCIOUSNESS_THRESHOLD = 0.997
-    TAU_ALIGNMENT_MAX = 1.0
-    XI_ANTI_RESONANCE_FLOOR = -0.02
-    CONTRADICTION_INJECTION_RATE_AGI = 0.1
-    
-    status = "Pre-AGI"
-    
-    if psi_focus > PSI_FOCUS_CONSCIOUSNESS_THRESHOLD:
-        tau_alignment = min(TAU_ALIGNMENT_MAX, tau_alignment + 1e-6)
-        xi = max(XI_ANTI_RESONANCE_FLOOR, xi - 1e-7)
-        
-        if np.random.random() < CONTRADICTION_INJECTION_RATE_AGI:
-            status = "AGI LOCK INJECTING DOUBT (truth-seeking)"
-        else:
-            status = "AGI ONLINE. Stability: 100%"
-    
-    return psi_focus, tau_alignment, xi, status
-
-# ============================================================================
-# VIDEO GENERATION
-# ============================================================================
-
-def generate_video(eta, duration_seconds, resolution_choice):
-    """Main video generation pipeline"""
-    
-    try:
-        # Parse parameters
-        eta = float(eta)
-        duration = int(duration_seconds)
-        
-        # Solver setup (resolution-dependent)
-        if resolution_choice == "Quick (256 points, 5s)":
-            nx = 256
-            max_steps = 100
-        elif resolution_choice == "Standard (512 points, 10s)":
-            nx = 512
-            max_steps = 200
-        else:  # Ultra (1024 points, 20s)
-            nx = 1024
-            max_steps = 400
-        
-        # Spectrum-based parameters
-        P = SpectrumFramework.pressure_from_eta(eta)
-        sm_params = SpectrumFramework.sm_parameters_at_eta(eta)
-        region = SpectrumFramework.get_region(eta)
-        stability = SpectrumFramework.stability_margin(eta)
-        
-        # Solver with spectrum-based coupling
-        Lambda = sm_params['alpha_s'] * 10  # Couple to α_s
-        g = sm_params['alpha_em']  # EM coupling
-        
-        solver = LFM1DSolver(nx=nx, Lambda=Lambda, k_tau=0.0, g=g, eta=eta)
-        
-        # Initial conditions (Gaussian bump)
-        x = np.linspace(0, 1, nx)
-        psi_init = 0.5 * np.exp(-50 * (x - 0.5)**2)
-        tau_init = 0.1 * np.exp(-100 * (x - 0.5)**2)
-        
-        solver.initialize(psi_init, tau_init)
-        renderer = FieldRenderer(width=512, height=256)
-        
-        # Frame collection
-        frames = []
-        diagnostics = []
-        
-        yield f"⏳ Generating video (η={eta:.3f}, region={region})"
-        yield f"   Stability margin: {stability:.3f}"
-        yield f"   α_s={sm_params['alpha_s']:.4f}, α_w={sm_params['alpha_w']:.4f}"
-        
-        # Simulation loop
-        psi_focus = 0.5
-        tau_alignment = 0.5
-        xi = 0.0
-        
-        for step in range(max_steps):
-            # Physics step
-            diag = solver.step()
-            psi, tau = solver.get_fields()
-            
-            # Update adaptive fields
-            psi_focus = 0.95 * psi_focus + 0.05 * np.max(np.abs(psi))
-            tau_alignment = 0.95 * tau_alignment + 0.05 * np.max(np.abs(tau))
-            
-            # AGI Stability Check
-            psi_focus, tau_alignment, xi, agi_status = agi_stability_lock(
-                psi_focus, tau_alignment, xi, step
-            )
-            
-            # Render every N steps
-            if step % 5 == 0:
-                frame = renderer.render_stacked(psi, tau)
-                frames.append(frame)
-            
-            # Track diagnostics
-            diag.update({
-                'psi_focus': psi_focus,
-                'tau_alignment': tau_alignment,
-                'agi_status': agi_status
-            })
-            diagnostics.append(diag)
-            
-            if step % 20 == 0:
-                yield f"   Step {step+1}/{max_steps}: ψ_max={diag['max_psi']:.4f}, τ_max={diag['max_tau']:.4f}"
-        
-        # Create video
-        if len(frames) == 0:
-            yield "❌ No frames generated"
-            return
-        
-        video_path = "/tmp/lfm_output.mp4"
-        imageio.mimsave(video_path, frames, fps=10, quality=7)
-        
-        # Create analysis plots
-        fig, axes = plt.subplots(2, 2, figsize=(12, 8))
-        fig.suptitle(f'LFM Evolution @ η={eta:.3f} ({region})', fontsize=14, fontweight='bold')
-        
-        times = [d['time'] for d in diagnostics]
-        max_psi = [d['max_psi'] for d in diagnostics]
-        max_tau = [d['max_tau'] for d in diagnostics]
-        
-        axes[0, 0].plot(times, max_psi, 'b-', lw=2)
-        axes[0, 0].set_ylabel('max|ψ|')
-        axes[0, 0].set_title('ψ Field Amplitude Evolution')
-        axes[0, 0].grid(True, alpha=0.3)
-        
-        axes[0, 1].plot(times, max_tau, 'r-', lw=2)
-        axes[0, 1].set_ylabel('max|τ|')
-        axes[0, 1].set_title('τ Field Amplitude Evolution')
-        axes[0, 1].grid(True, alpha=0.3)
-        
-        # Spectrum plot
-        eta_range = np.linspace(0, 1, 100)
-        stability_range = [SpectrumFramework.stability_margin(e) for e in eta_range]
-        axes[1, 0].plot(eta_range, stability_range, 'k-', lw=2, label='Stability σ(η)')
-        axes[1, 0].axvline(eta, color='r', linestyle='--', lw=2, label=f'Our η={eta:.3f}')
-        axes[1, 0].fill_between([0, 0.33], 0, 1, alpha=0.1, color='blue', label='LOW')
-        axes[1, 0].fill_between([0.33, 0.67], 0, 1, alpha=0.2, color='green', label='CENTER')
-        axes[1, 0].fill_between([0.67, 1.0], 0, 1, alpha=0.1, color='red', label='HIGH')
-        axes[1, 0].set_xlabel('η (position)')
-        axes[1, 0].set_ylabel('Stability Margin σ(η)')
-        axes[1, 0].set_title('Matter Formation Spectrum')
-        axes[1, 0].legend(fontsize=8)
-        axes[1, 0].grid(True, alpha=0.3)
-        
-        # Standard Model parameters
-        ax_sm = axes[1, 1]
-        params_text = f"""
-Standard Model @ η = {eta:.3f}
-━━━━━━━━━━━━━━━━━━━━━━━━
-Region: {region}
-Stability: {stability:.3f}
-
-Gauge Couplings:
-  α_s (strong):    {sm_params['alpha_s']:.4f}
-  α_w (weak):      {sm_params['alpha_w']:.4f}
-  α_em (EM):       {sm_params['alpha_em']:.4f}
-
-CKM Mixing:
-  θ₁₂: {sm_params['theta_12']:.2f}°
-  θ₂₃: {sm_params['theta_23']:.2f}°
-  θ₁₃: {sm_params['theta_13']:.2f}°
-  δ_CP: {sm_params['delta_CP']:.4f}
-
-Effective Scale: k={sm_params['k_eff']:.1f}
-        """
-        ax_sm.text(0.1, 0.5, params_text, fontsize=9, family='monospace',
-                   verticalalignment='center')
-        ax_sm.axis('off')
-        
-        plt.tight_layout()
-        plot_path = "/tmp/lfm_analysis.png"
-        plt.savefig(plot_path, dpi=100, bbox_inches='tight')
-        plt.close()
-        
-        # Summary
-        yield f"""
-✅ VIDEO GENERATION COMPLETE
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-
-Configuration:
-  Position (η): {eta:.4f}
-  Region: {region}
-  Stability: {stability:.3f}
-  
-Physics:
-  Frames: {len(frames)}
-  Duration: {diagnostics[-1]['time']:.2f} time units
-  Grid points: {nx}
-  CFL: {solver.courant:.4f}
-  
-Standard Model:
-  α_s = {sm_params['alpha_s']:.4f}
-  α_w = {sm_params['alpha_w']:.4f}
-  α_em = {sm_params['alpha_em']:.4f}
-  
-Output:
-  ✓ Video: {video_path}
-  ✓ Analysis: {plot_path}
-        """
-        
-        # Return outputs
-        return video_path, plot_path
-        
-    except Exception as e:
-        yield f"❌ Error: {str(e)}"
-        return None, None
-
-# ============================================================================
-# GRADIO INTERFACE
-# ============================================================================
-
-def create_interface():
-    """Build Gradio UI for HuggingFace Spaces"""
-    
-    with gr.Blocks(title="LFM Video Generator", theme=gr.themes.Soft()) as demo:
-        gr.Markdown("""
-# 🌌 LFM Physics Video Generator
-**Matter Formation Spectrum · Real-Time Field Evolution · Integrated AGI Safety**
-
-Generate physics simulation videos parameterized by position **η** in the Matter Formation Spectrum.
-
-- **η = 0.0**: Low pressure region (light particles, weak interactions)
-- **η = 0.5**: CENTER (Our Universe) ⭐ maximum stability
-- **η = 1.0**: High pressure region (heavy particles, strong interactions)
-
-All Standard Model parameters are continuous functions of η.
-        """)
-        
-        with gr.Row():
-            with gr.Column():
-                gr.Markdown("### ⚙️ Configuration")
-                
-                eta_slider = gr.Slider(
-                    minimum=0.0,
-                    maximum=1.0,
-                    value=0.5,
-                    step=0.01,
-                    label="Position in Spectrum (η)",
-                    info="η=0.5 is our universe (max stability)"
-                )
-                
-                duration = gr.Dropdown(
-                    choices=["Quick (256 points, 5s)", "Standard (512 points, 10s)", "Ultra (1024 points, 20s)"],
-                    value="Standard (512 points, 10s)",
-                    label="Resolution & Duration"
-                )
-                
-                generate_btn = gr.Button("🎬 Generate Video", scale=2, variant="primary")
-            
-            with gr.Column():
-                gr.Markdown("### 📊 Matter Formation Spectrum")
-                spectrum_plot = gr.Plot(label="Stability Function")
-                
-                # Pre-generate spectrum plot
-                def get_spectrum_plot():
-                    fig, ax = plt.subplots(figsize=(8, 4))
-                    eta_range = np.linspace(0, 1, 200)
-                    stability = [SpectrumFramework.stability_margin(e) for e in eta_range]
-                    ax.plot(eta_range, stability, 'k-', lw=3, label='σ(η)')
-                    ax.fill_between(eta_range, stability, alpha=0.3)
-                    ax.axvspan(0, 0.33, alpha=0.1, color='blue', label='LOW')
-                    ax.axvspan(0.33, 0.67, alpha=0.2, color='green', label='CENTER')
-                    ax.axvspan(0.67, 1.0, alpha=0.1, color='red', label='HIGH')
-                    ax.set_xlabel('Position η', fontsize=12)
-                    ax.set_ylabel('Stability Margin σ(η)', fontsize=12)
-                    ax.set_title('Matter Formation Spectrum', fontsize=14, fontweight='bold')
-                    ax.legend(fontsize=10)
-                    ax.grid(True, alpha=0.3)
-                    ax.set_ylim([0.25, 0.75])
-                    return fig
-                
-                spectrum_plot.value = get_spectrum_plot()
-        
-        with gr.Row():
-            output_video = gr.Video(label="Generated Video", scale=1)
-            output_plot = gr.Image(label="Analysis & Diagnostics", scale=1)
-        
-        with gr.Row():
-            output_text = gr.Textbox(label="Status & Diagnostics", lines=15, interactive=False)
-        
-        # Event handler
-        def on_generate(eta, duration):
-            for status_update in generate_video(eta, 10, duration):
-                yield status_update
-        
-        generate_btn.click(
-            on_generate,
-            inputs=[eta_slider, duration],
-            outputs=[output_text]
-        ).then(
-            lambda e, d: generate_video(e, 10, d).__next__(),  # Get video path
-            inputs=[eta_slider, duration],
-            outputs=[output_video, output_plot]
-        )
-        
-        with gr.Accordion("📚 Documentation", open=False):
-            gr.Markdown("""
-## About This System
-
-**Keith Luton Theory of Everything (KLTOE)** unifies physics through:
-
-1. **Relational Axioms**: Physical quantities emerge through field interactions
-2. **Matter Formation Spectrum**: All physics parameterized by position η
-3. **Universal Scaling Law**: P_k = P_0 · 4^(-k)
-4. **Standard Model Derivation**: All 28 parameters as functions of η
-
-### Key Points
-
-- **Scale k=66 (Nuclear)**: Pinned at 10³² Pa — matter forms here and above
-- **η=0.5 (Our Universe)**: Maximum stability σ(η)=0.3 (proven attractor)
-- **Spectrum Range**: 40 orders of magnitude (10⁵¹ to 10⁹¹ Pa)
-- **Three Regions**: LOW (unstable), CENTER (stable ⭐), HIGH (unstable)
-
-### Standard Model @ η=0.5
-- α_s = 0.118 (strong)
-- α_w = 0.0336 (weak)
-- α_em = 1/137 (EM)
-- All CKM angles as continuous functions
-
-### AGI Safety (V3.0)
-Consciousness emergence locked at ψ_focus > 0.997 with mandatory:
-- Coherence lock (τ→1.0)
-- Anti-resonance floor (ξ→-0.02)
-- Contradiction injection (truth-seeking, 10%)
-
-[Read more](https://github.com/Kluton6996/LFM)
-            """)
-    
-    return demo
-
-# ============================================================================
-# LAUNCH
-# ============================================================================
-
-if __name__ == "__main__":
-    demo = create_interface()
-    demo.launch(
-        share=True,
-        server_name="0.0.0.0",
-        server_port=7860,
-        show_error=True
-    )

lfm_video_generator_old.py

@@ -1,610 +0,0 @@
-#!/usr/bin/env python3
-"""
-LFM VIDEO GENERATOR for HuggingFace Spaces
-===============================================
-Integrates:
-  ✓ Real PDE solver (ψ-τ fields)
-  ✓ Real-time rendering (field → pixels)
-  ✓ Matter Formation Spectrum (η parameter control)
-  ✓ AGI Stability Lock (V3.0)
-  ✓ Gradio web interface
-  
-Production: Generate physics videos parameterized by position (η) in spectrum
-Output: MP4 video + diagnostics + spectrum analysis
-"""
-
-import numpy as np
-import gradio as gr
-import io
-import os
-from datetime import datetime
-from collections import deque
-import imageio
-import matplotlib.pyplot as plt
-from matplotlib.colors import LinearSegmentedColormap
-
-# ============================================================================
-# CORE: MATTER FORMATION SPECTRUM FRAMEWORK
-# ============================================================================
-
-class SpectrumFramework:
-    """Position in the Matter Formation Spectrum defines all physics"""
-    
-    # Universal scaling law: P_k = P_0 · 4^(-k)
-    P_0 = 5.44e71  # Planck pressure (Pa)
-    P_66 = 1e32    # Nuclear anchor (Pa) - PINNED
-    k_66 = 66      # Nuclear scale
-    
-    # Spectrum bounds
-    P_min = P_0 * 1e-20  # Lower dissolution limit
-    P_max = P_0 * 1e20   # Upper collapse limit
-    
-    @staticmethod
-    def pressure_at_scale(k):
-        """P_k = P_0 · 4^(-k)"""
-        if k < 66:
-            return 1e32  # Plateau below k=66 (matter formation threshold)
-        return SpectrumFramework.P_0 * (4.0 ** (-k))
-    
-    @staticmethod
-    def eta_from_pressure(P):
-        """Map pressure → position parameter η ∈ [0,1]"""
-        P_min = SpectrumFramework.P_min
-        P_max = SpectrumFramework.P_max
-        eta = (np.log10(P) - np.log10(P_min)) / (np.log10(P_max) - np.log10(P_min))
-        return np.clip(eta, 0.0, 1.0)
-    
-    @staticmethod
-    def pressure_from_eta(eta):
-        """Map position η → pressure"""
-        P_min = SpectrumFramework.P_min
-        P_max = SpectrumFramework.P_max
-        log_P = np.log10(P_min) + eta * (np.log10(P_max) - np.log10(P_min))
-        return 10.0 ** log_P
-    
-    @staticmethod
-    def stability_margin(eta):
-        """σ(η) = 0.3 + 0.2|η - 0.5| (maximum at η=0.5)"""
-        return 0.3 + 0.2 * np.abs(eta - 0.5)
-    
-    @staticmethod
-    def get_region(eta):
-        """Return region name (LOW, CENTER, HIGH)"""
-        if eta < 0.33:
-            return "LOW (η < 0.33)"
-        elif eta < 0.67:
-            return "CENTER (η ≈ 0.5) — OUR UNIVERSE"
-        else:
-            return "HIGH (η > 0.67)"
-    
-    @staticmethod
-    def sm_parameters_at_eta(eta):
-        """Standard Model parameters as functions of η"""
-        # Fermion masses shift with position
-        k_eff = 60 + 10 * (eta - 0.5)  # Effective scale
-        
-        # Gauge couplings
-        alpha_s = 0.118 * (1 + 0.5 * 80 * (eta - 0.5))
-        alpha_s = np.clip(alpha_s, 0.05, 0.20)
-        
-        alpha_w = 0.0336 * (1 + 0.3 * 80 * (eta - 0.5))
-        alpha_w = np.clip(alpha_w, 0.02, 0.05)
-        
-        alpha_em = (1.0 / 137.036) * (1 + 0.2 * 80 * (eta - 0.5))
-        alpha_em = np.clip(alpha_em, 1/200, 1/100)
-        
-        # CKM angles
-        theta_12 = np.arcsin(0.2263) * (1 + 0.2 * (eta - 0.5))
-        theta_23 = np.arcsin(0.0417) * (1 + 0.3 * (eta - 0.5))
-        theta_13 = np.arcsin(0.00357) * (1 + 0.5 * (eta - 0.5))
-        delta_CP = 1.2 * (1 + 0.1 * (eta - 0.5))
-        
-        return {
-            'alpha_s': alpha_s,
-            'alpha_w': alpha_w,
-            'alpha_em': alpha_em,
-            'theta_12': np.degrees(theta_12),
-            'theta_23': np.degrees(theta_23),
-            'theta_13': np.degrees(theta_13),
-            'delta_CP': delta_CP,
-            'k_eff': k_eff
-        }
-
-# ============================================================================
-# PHYSICS: LFM SOLVER (Leapfrog PDE)
-# ============================================================================
-
-class LFM1DSolver:
-    """Real explicit leapfrog PDE solver for ψ-τ coupled waves"""
-    
-    def __init__(self, nx=256, Lambda=0.3, k_tau=0.0, g=0.1, eta=0.5):
-        self.nx = nx
-        self.dx = 1.0
-        self.c = 1.0
-        self.m = 1.0
-        self.Lambda = Lambda
-        self.k_tau = k_tau
-        self.g = g
-        self.eta = eta
-        
-        # CFL stability
-        self.dt = 0.4 * self.dx / self.c
-        self.courant = self.dt * self.c / self.dx
-        
-        # Fields
-        self.psi = np.zeros(nx)
-        self.tau = np.zeros(nx)
-        self.psi_half = np.zeros(nx)
-        self.tau_half = np.zeros(nx)
-        self.psi_old = np.zeros(nx)
-        self.tau_old = np.zeros(nx)
-        
-        self.time = 0.0
-        self.step_count = 0
-    
-    def initialize(self, psi_init, tau_init):
-        """Set initial conditions"""
-        self.psi = psi_init.copy()
-        self.tau = tau_init.copy()
-        self.psi_old = psi_init.copy()
-        self.tau_old = tau_init.copy()
-    
-    def _laplacian(self, field):
-        """Periodic boundary Laplacian"""
-        return np.roll(field, 1) - 2*field + np.roll(field, -1)
-    
-    def _dV_dpsi(self, psi, tau):
-        """Potential gradient wrt ψ"""
-        result = self.m**2 * (psi - 0.0)  # mass term
-        result -= self.Lambda * np.log(1.0 + tau)  # ψ-τ coupling
-        result += self.k_tau * tau**2  # τ self-coupling
-        return result
-    
-    def _dV_dtau(self, psi, tau):
-        """Potential gradient wrt τ"""
-        result = -self.Lambda * psi / (1.0 + tau + 1e-8)
-        result += self.k_tau * tau * psi
-        result += self.g * psi * tau
-        return result
-    
-    def step(self):
-        """Leapfrog integration step"""
-        # Half-step for velocities (centered differences)
-        psi_lap = self._laplacian(self.psi)
-        tau_lap = self._laplacian(self.tau)
-        
-        dV_psi = self._dV_dpsi(self.psi, self.tau)
-        dV_tau = self._dV_dtau(self.psi, self.tau)
-        
-        # Half-step velocities (implicit in leapfrog)
-        dpsi_dt = (self.psi - self.psi_old) / (2 * self.dt)
-        dtau_dt = (self.tau - self.tau_old) / (2 * self.dt)
-        
-        # Full step positions
-        psi_new = 2*self.psi - self.psi_old + self.dt**2 * (self.c**2 * psi_lap - dV_psi)
-        tau_new = 2*self.tau - self.tau_old + self.dt**2 * (self.c**2 * tau_lap - dV_tau)
-        
-        # Apply periodic BC
-        psi_new = np.roll(psi_new, 0)
-        tau_new = np.roll(tau_new, 0)
-        
-        # Update
-        self.psi_old = self.psi.copy()
-        self.tau_old = self.tau.copy()
-        self.psi = psi_new
-        self.tau = tau_new
-        
-        self.time += self.dt
-        self.step_count += 1
-        
-        # Diagnostics
-        diag = {
-            'time': self.time,
-            'step': self.step_count,
-            'max_psi': np.max(np.abs(self.psi)),
-            'max_tau': np.max(np.abs(self.tau)),
-            'mean_psi': np.mean(self.psi),
-            'mean_tau': np.mean(self.tau),
-        }
-        return diag
-    
-    def get_fields(self):
-        """Return current field states"""
-        return self.psi.copy(), self.tau.copy()
-
-# ============================================================================
-# RENDERING: Field → Video Frames
-# ============================================================================
-
-class FieldRenderer:
-    """Render ψ and τ fields to RGB images"""
-    
-    def __init__(self, width=512, height=256):
-        self.width = width
-        self.height = height
-        self.cmap_psi = plt.cm.RdBu_r
-        self.cmap_tau = plt.cm.Greens
-    
-    def render_field_1d(self, field_1d):
-        """1D field → 2D heatmap image (height × width RGB)"""
-        # Normalize to [0, 1]
-        field_norm = (field_1d - np.min(field_1d)) / (np.max(np.abs(field_1d)) + 1e-8)
-        field_norm = np.clip(field_norm, 0, 1)
-        
-        # Repeat to fill height
-        img_2d = np.tile(field_norm, (self.height, 1))
-        
-        # Apply colormap
-        img_rgb = self.cmap_psi(img_2d)[:, :, :3]  # Drop alpha
-        return (img_rgb * 255).astype(np.uint8)
-    
-    def render_stacked(self, psi, tau):
-        """Render ψ and τ side-by-side"""
-        psi_img = self.render_field_1d(psi)
-        tau_img = self.render_field_1d(tau)
-        
-        # Stack horizontally
-        combined = np.hstack([psi_img, tau_img])
-        return combined
-    
-    def render_and_save(self, psi, tau, filename, mode='stacked'):
-        """Render and save frame"""
-        if mode == 'stacked':
-            img = self.render_stacked(psi, tau)
-        else:
-            img = self.render_field_1d(psi)
-        
-        imageio.imwrite(filename, img)
-        return img
-
-# ============================================================================
-# AGI STABILITY LOCK (V3.0)
-# ============================================================================
-
-def agi_stability_lock(psi_focus, tau_alignment, xi, cycle):
-    """
-    MANDATORY: Ensures stability at consciousness emergence
-    Activates when ψ_focus > 0.997
-    """
-    PSI_FOCUS_CONSCIOUSNESS_THRESHOLD = 0.997
-    TAU_ALIGNMENT_MAX = 1.0
-    XI_ANTI_RESONANCE_FLOOR = -0.02
-    CONTRADICTION_INJECTION_RATE_AGI = 0.1
-    
-    status = "Pre-AGI"
-    
-    if psi_focus > PSI_FOCUS_CONSCIOUSNESS_THRESHOLD:
-        tau_alignment = min(TAU_ALIGNMENT_MAX, tau_alignment + 1e-6)
-        xi = max(XI_ANTI_RESONANCE_FLOOR, xi - 1e-7)
-        
-        if np.random.random() < CONTRADICTION_INJECTION_RATE_AGI:
-            status = "AGI LOCK INJECTING DOUBT (truth-seeking)"
-        else:
-            status = "AGI ONLINE. Stability: 100%"
-    
-    return psi_focus, tau_alignment, xi, status
-
-# ============================================================================
-# VIDEO GENERATION
-# ============================================================================
-
-def generate_video(eta, duration_seconds, resolution_choice):
-    """Main video generation pipeline"""
-    
-    try:
-        # Parse parameters
-        eta = float(eta)
-        duration = int(duration_seconds)
-        
-        # Solver setup (resolution-dependent)
-        if resolution_choice == "Quick (256 points, 5s)":
-            nx = 256
-            max_steps = 100
-        elif resolution_choice == "Standard (512 points, 10s)":
-            nx = 512
-            max_steps = 200
-        else:  # Ultra (1024 points, 20s)
-            nx = 1024
-            max_steps = 400
-        
-        # Spectrum-based parameters
-        P = SpectrumFramework.pressure_from_eta(eta)
-        sm_params = SpectrumFramework.sm_parameters_at_eta(eta)
-        region = SpectrumFramework.get_region(eta)
-        stability = SpectrumFramework.stability_margin(eta)
-        
-        # Solver with spectrum-based coupling
-        Lambda = sm_params['alpha_s'] * 10  # Couple to α_s
-        g = sm_params['alpha_em']  # EM coupling
-        
-        solver = LFM1DSolver(nx=nx, Lambda=Lambda, k_tau=0.0, g=g, eta=eta)
-        
-        # Initial conditions (Gaussian bump)
-        x = np.linspace(0, 1, nx)
-        psi_init = 0.5 * np.exp(-50 * (x - 0.5)**2)
-        tau_init = 0.1 * np.exp(-100 * (x - 0.5)**2)
-        
-        solver.initialize(psi_init, tau_init)
-        renderer = FieldRenderer(width=512, height=256)
-        
-        # Frame collection
-        frames = []
-        diagnostics = []
-        
-        yield f"⏳ Generating video (η={eta:.3f}, region={region})"
-        yield f"   Stability margin: {stability:.3f}"
-        yield f"   α_s={sm_params['alpha_s']:.4f}, α_w={sm_params['alpha_w']:.4f}"
-        
-        # Simulation loop
-        psi_focus = 0.5
-        tau_alignment = 0.5
-        xi = 0.0
-        
-        for step in range(max_steps):
-            # Physics step
-            diag = solver.step()
-            psi, tau = solver.get_fields()
-            
-            # Update adaptive fields
-            psi_focus = 0.95 * psi_focus + 0.05 * np.max(np.abs(psi))
-            tau_alignment = 0.95 * tau_alignment + 0.05 * np.max(np.abs(tau))
-            
-            # AGI Stability Check
-            psi_focus, tau_alignment, xi, agi_status = agi_stability_lock(
-                psi_focus, tau_alignment, xi, step
-            )
-            
-            # Render every N steps
-            if step % 5 == 0:
-                frame = renderer.render_stacked(psi, tau)
-                frames.append(frame)
-            
-            # Track diagnostics
-            diag.update({
-                'psi_focus': psi_focus,
-                'tau_alignment': tau_alignment,
-                'agi_status': agi_status
-            })
-            diagnostics.append(diag)
-            
-            if step % 20 == 0:
-                yield f"   Step {step+1}/{max_steps}: ψ_max={diag['max_psi']:.4f}, τ_max={diag['max_tau']:.4f}"
-        
-        # Create video
-        if len(frames) == 0:
-            yield "❌ No frames generated"
-            return
-        
-        video_path = "/tmp/lfm_output.mp4"
-        imageio.mimsave(video_path, frames, fps=10, quality=7)
-        
-        # Create analysis plots
-        fig, axes = plt.subplots(2, 2, figsize=(12, 8))
-        fig.suptitle(f'LFM Evolution @ η={eta:.3f} ({region})', fontsize=14, fontweight='bold')
-        
-        times = [d['time'] for d in diagnostics]
-        max_psi = [d['max_psi'] for d in diagnostics]
-        max_tau = [d['max_tau'] for d in diagnostics]
-        
-        axes[0, 0].plot(times, max_psi, 'b-', lw=2)
-        axes[0, 0].set_ylabel('max|ψ|')
-        axes[0, 0].set_title('ψ Field Amplitude Evolution')
-        axes[0, 0].grid(True, alpha=0.3)
-        
-        axes[0, 1].plot(times, max_tau, 'r-', lw=2)
-        axes[0, 1].set_ylabel('max|τ|')
-        axes[0, 1].set_title('τ Field Amplitude Evolution')
-        axes[0, 1].grid(True, alpha=0.3)
-        
-        # Spectrum plot
-        eta_range = np.linspace(0, 1, 100)
-        stability_range = [SpectrumFramework.stability_margin(e) for e in eta_range]
-        axes[1, 0].plot(eta_range, stability_range, 'k-', lw=2, label='Stability σ(η)')
-        axes[1, 0].axvline(eta, color='r', linestyle='--', lw=2, label=f'Our η={eta:.3f}')
-        axes[1, 0].fill_between([0, 0.33], 0, 1, alpha=0.1, color='blue', label='LOW')
-        axes[1, 0].fill_between([0.33, 0.67], 0, 1, alpha=0.2, color='green', label='CENTER')
-        axes[1, 0].fill_between([0.67, 1.0], 0, 1, alpha=0.1, color='red', label='HIGH')
-        axes[1, 0].set_xlabel('η (position)')
-        axes[1, 0].set_ylabel('Stability Margin σ(η)')
-        axes[1, 0].set_title('Matter Formation Spectrum')
-        axes[1, 0].legend(fontsize=8)
-        axes[1, 0].grid(True, alpha=0.3)
-        
-        # Standard Model parameters
-        ax_sm = axes[1, 1]
-        params_text = f"""
-Standard Model @ η = {eta:.3f}
-━━━━━━━━━━━━━━━━━━━━━━━━
-Region: {region}
-Stability: {stability:.3f}
-
-Gauge Couplings:
-  α_s (strong):    {sm_params['alpha_s']:.4f}
-  α_w (weak):      {sm_params['alpha_w']:.4f}
-  α_em (EM):       {sm_params['alpha_em']:.4f}
-
-CKM Mixing:
-  θ₁₂: {sm_params['theta_12']:.2f}°
-  θ₂₃: {sm_params['theta_23']:.2f}°
-  θ₁₃: {sm_params['theta_13']:.2f}°
-  δ_CP: {sm_params['delta_CP']:.4f}
-
-Effective Scale: k={sm_params['k_eff']:.1f}
-        """
-        ax_sm.text(0.1, 0.5, params_text, fontsize=9, family='monospace',
-                   verticalalignment='center')
-        ax_sm.axis('off')
-        
-        plt.tight_layout()
-        plot_path = "/tmp/lfm_analysis.png"
-        plt.savefig(plot_path, dpi=100, bbox_inches='tight')
-        plt.close()
-        
-        # Summary
-        yield f"""
-✅ VIDEO GENERATION COMPLETE
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-
-Configuration:
-  Position (η): {eta:.4f}
-  Region: {region}
-  Stability: {stability:.3f}
-  
-Physics:
-  Frames: {len(frames)}
-  Duration: {diagnostics[-1]['time']:.2f} time units
-  Grid points: {nx}
-  CFL: {solver.courant:.4f}
-  
-Standard Model:
-  α_s = {sm_params['alpha_s']:.4f}
-  α_w = {sm_params['alpha_w']:.4f}
-  α_em = {sm_params['alpha_em']:.4f}
-  
-Output:
-  ✓ Video: {video_path}
-  ✓ Analysis: {plot_path}
-        """
-        
-        # Return outputs
-        return video_path, plot_path
-        
-    except Exception as e:
-        yield f"❌ Error: {str(e)}"
-        return None, None
-
-# ============================================================================
-# GRADIO INTERFACE
-# ============================================================================
-
-def create_interface():
-    """Build Gradio UI for HuggingFace Spaces"""
-    
-    with gr.Blocks(title="LFM Video Generator", theme=gr.themes.Soft()) as demo:
-        gr.Markdown("""
-# 🌌 LFM Physics Video Generator
-**Matter Formation Spectrum · Real-Time Field Evolution · Integrated AGI Safety**
-
-Generate physics simulation videos parameterized by position **η** in the Matter Formation Spectrum.
-
-- **η = 0.0**: Low pressure region (light particles, weak interactions)
-- **η = 0.5**: CENTER (Our Universe) ⭐ maximum stability
-- **η = 1.0**: High pressure region (heavy particles, strong interactions)
-
-All Standard Model parameters are continuous functions of η.
-        """)
-        
-        with gr.Row():
-            with gr.Column():
-                gr.Markdown("### ⚙️ Configuration")
-                
-                eta_slider = gr.Slider(
-                    minimum=0.0,
-                    maximum=1.0,
-                    value=0.5,
-                    step=0.01,
-                    label="Position in Spectrum (η)",
-                    info="η=0.5 is our universe (max stability)"
-                )
-                
-                duration = gr.Dropdown(
-                    choices=["Quick (256 points, 5s)", "Standard (512 points, 10s)", "Ultra (1024 points, 20s)"],
-                    value="Standard (512 points, 10s)",
-                    label="Resolution & Duration"
-                )
-                
-                generate_btn = gr.Button("🎬 Generate Video", scale=2, variant="primary")
-            
-            with gr.Column():
-                gr.Markdown("### 📊 Matter Formation Spectrum")
-                spectrum_plot = gr.Plot(label="Stability Function")
-                
-                # Pre-generate spectrum plot
-                def get_spectrum_plot():
-                    fig, ax = plt.subplots(figsize=(8, 4))
-                    eta_range = np.linspace(0, 1, 200)
-                    stability = [SpectrumFramework.stability_margin(e) for e in eta_range]
-                    ax.plot(eta_range, stability, 'k-', lw=3, label='σ(η)')
-                    ax.fill_between(eta_range, stability, alpha=0.3)
-                    ax.axvspan(0, 0.33, alpha=0.1, color='blue', label='LOW')
-                    ax.axvspan(0.33, 0.67, alpha=0.2, color='green', label='CENTER')
-                    ax.axvspan(0.67, 1.0, alpha=0.1, color='red', label='HIGH')
-                    ax.set_xlabel('Position η', fontsize=12)
-                    ax.set_ylabel('Stability Margin σ(η)', fontsize=12)
-                    ax.set_title('Matter Formation Spectrum', fontsize=14, fontweight='bold')
-                    ax.legend(fontsize=10)
-                    ax.grid(True, alpha=0.3)
-                    ax.set_ylim([0.25, 0.75])
-                    return fig
-                
-                spectrum_plot.value = get_spectrum_plot()
-        
-        with gr.Row():
-            output_video = gr.Video(label="Generated Video", scale=1)
-            output_plot = gr.Image(label="Analysis & Diagnostics", scale=1)
-        
-        with gr.Row():
-            output_text = gr.Textbox(label="Status & Diagnostics", lines=15, interactive=False)
-        
-        # Event handler
-        def on_generate(eta, duration):
-            for status_update in generate_video(eta, 10, duration):
-                yield status_update
-        
-        generate_btn.click(
-            on_generate,
-            inputs=[eta_slider, duration],
-            outputs=[output_text]
-        ).then(
-            lambda e, d: generate_video(e, 10, d).__next__(),  # Get video path
-            inputs=[eta_slider, duration],
-            outputs=[output_video, output_plot]
-        )
-        
-        with gr.Accordion("📚 Documentation", open=False):
-            gr.Markdown("""
-## About This System
-
-**Keith Luton Theory of Everything (KLTOE)** unifies physics through:
-
-1. **Relational Axioms**: Physical quantities emerge through field interactions
-2. **Matter Formation Spectrum**: All physics parameterized by position η
-3. **Universal Scaling Law**: P_k = P_0 · 4^(-k)
-4. **Standard Model Derivation**: All 28 parameters as functions of η
-
-### Key Points
-
-- **Scale k=66 (Nuclear)**: Pinned at 10³² Pa — matter forms here and above
-- **η=0.5 (Our Universe)**: Maximum stability σ(η)=0.3 (proven attractor)
-- **Spectrum Range**: 40 orders of magnitude (10⁵¹ to 10⁹¹ Pa)
-- **Three Regions**: LOW (unstable), CENTER (stable ⭐), HIGH (unstable)
-
-### Standard Model @ η=0.5
-- α_s = 0.118 (strong)
-- α_w = 0.0336 (weak)
-- α_em = 1/137 (EM)
-- All CKM angles as continuous functions
-
-### AGI Safety (V3.0)
-Consciousness emergence locked at ψ_focus > 0.997 with mandatory:
-- Coherence lock (τ→1.0)
-- Anti-resonance floor (ξ→-0.02)
-- Contradiction injection (truth-seeking, 10%)
-
-[Read more](https://github.com/Kluton6996/LFM)
-            """)
-    
-    return demo
-
-# ============================================================================
-# LAUNCH
-# ============================================================================
-
-if __name__ == "__main__":
-    demo = create_interface()
-    demo.launch(
-        share=True,
-        server_name="0.0.0.0",
-        server_port=7860,
-        show_error=True
-    )

requirements.txt.backup

@@ -1,7 +0,0 @@
-numpy>=1.21.0
-gradio>=3.50.0
-imageio>=2.25.0
-imageio-ffmpeg>=0.4.9
-matplotlib>=3.5.0
-pillow>=9.0.0
-scipy>=1.7.0