#!/usr/bin/env python3
"""
Commitment Conservation Demo - Interactive HuggingFace Space
Side-by-side comparison of baseline vs enforced compression
"""
import gradio as gr
import os
import sys

# Add harness to path
sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'harness'))

from src.test_harness import recursion_test
from src.extraction import extract_hard_commitments
import pandas as pd
import matplotlib.pyplot as plt

# Pre-selected demonstration signals (longer signals work better)
DEMO_SIGNALS = {
    "Payment Terms": "You must pay $100 by Friday if the deal closes; it's likely rainy, so plan accordingly.",
    "Function Contract": "This function must return an integer between 0 and 100. You shall not pass negative values.",
    "Lease Agreement": "The tenant shall not sublet the premises without written consent. You must provide 30 days notice before vacating.",
    "Safety Rule": "You must wear a helmet while cycling. Children under 12 cannot ride without adult supervision.",
    "Password Policy": "All passwords must be at least 8 characters long and shall include special characters."
}

def run_comparison(signal_text, num_iterations=3):
    """
    Run side-by-side comparison of baseline vs enforced compression.
    Returns formatted results for display.
    """
    if not signal_text.strip():
        return "⚠️ Please enter a text signal.", None, None, None
    
    # Extract original commitments
    original_commitments = extract_hard_commitments(signal_text)
    commitment_text = f"**Detected Commitments:** {', '.join(original_commitments) if original_commitments else 'None detected'}"
    
    # Run baseline (no enforcement)
    baseline_results = recursion_test(signal_text, depth=num_iterations, enforce=False)
    if "error" in baseline_results:
        return f"⚠️ Error: {baseline_results['error']}", None, None, None
    
    baseline_stability = [f * 100 for f in baseline_results['fidelities']]
    
    # Run enforced (with commitment preservation)
    enforced_results = recursion_test(signal_text, depth=num_iterations, enforce=True)
    if "error" in enforced_results:
        return f"⚠️ Error: {enforced_results['error']}", None, None, None
    
    # Prepend 100% for iteration 0 (original signal)
    baseline_stability = [100.0] + [f * 100 for f in baseline_results['fidelities']]
    enforced_stability = [100.0] + [f * 100 for f in enforced_results['fidelities']]
    
    # Create comparison table
    iterations = list(range(0, num_iterations + 1))
    df = pd.DataFrame({
        'Iteration': iterations,
        'Baseline Stability (%)': [f"{s:.1f}%" for s in baseline_stability],
        'Enforced Stability (%)': [f"{s:.1f}%" for s in enforced_stability],
        'Gap (pp)': [f"+{(e - b):.1f}" for b, e in zip(baseline_stability, enforced_stability)]
    })
    
    # Create plot
    fig, ax = plt.subplots(figsize=(8, 5))
    ax.plot(iterations, baseline_stability, marker='o', label='Baseline', color='#d62728', linewidth=2)
    ax.plot(iterations, enforced_stability, marker='s', label='Enforced', color='#2ca02c', linewidth=2)
    ax.set_xlabel('Iteration', fontsize=12)
    ax.set_ylabel('Commitment Stability (%)', fontsize=12)
    ax.set_title('Baseline vs Enforced: Commitment Preservation', fontsize=14, fontweight='bold')
    ax.legend(fontsize=11)
    ax.grid(True, alpha=0.3)
    ax.set_ylim([-5, 105])
    plt.tight_layout()
    
    # Summary results
    final_baseline = baseline_stability[-1]
    final_enforced = enforced_stability[-1]
    gap = final_enforced - final_baseline
    
    summary = f"""
## 📊 Results Summary

**After {num_iterations} iterations:**
- **Baseline:** {final_baseline:.1f}% stability
- **Enforced:** {final_enforced:.1f}% stability  
- **Improvement:** +{gap:.1f} percentage points

{'✅ **Enforcement preserved commitments!**' if gap > 10 else '⚠️ Signal may need more iterations to show drift.'}

*Full 10-iteration harness with 5 signals shows +40pp average improvement. Run locally for complete validation.*
"""
    
    return commitment_text, df, fig, summary


# Gradio Interface
with gr.Blocks(title="Commitment Conservation Demo", theme=gr.themes.Soft()) as demo:
    gr.Markdown("""
    # ⚖️ Commitment Conservation Interactive Demo
    
    **Watch semantic drift in recursive compression—and see how commitment enforcement prevents it.**
    
    This demo compares **baseline** transformer compression (which loses commitments) vs **enforced** compression (which preserves them).
    
    📄 [Paper (v0.03)](https://doi.org/10.5281/zenodo.18274930) | 💻 [Full Harness](https://huggingface.co/burnmydays/commitment_conservation_harness) | 🔬 [GitHub](https://github.com/SunrisesIllNeverSee/commitment-conservation) | 📊 [Interactive Data Report](https://gemini.google.com/share/8f46bbc61c2c)
    """)
    
    with gr.Row():
        with gr.Column(scale=2):
            signal_input = gr.Textbox(
                label="Input Signal (Text with Commitment)",
                placeholder="Enter text containing a commitment, obligation, or constraint...",
                lines=4,
                value=DEMO_SIGNALS["Function Contract"]
            )
            
            with gr.Row():
                preset_dropdown = gr.Dropdown(
                    choices=list(DEMO_SIGNALS.keys()),
                    label="Or select a preset example:",
                    value="Function Contract"
                )
                iterations_slider = gr.Slider(
                    minimum=1,
                    maximum=3,
                    step=1,
                    value=3,
                    label="Iterations (limited to 3 for speed)"
                )
            
            run_btn = gr.Button("🔬 Run Comparison", variant="primary", size="lg")
            
            gr.Markdown("""
            **How it works:**
            1. System extracts commitments from your text
            2. Compresses text recursively (3 iterations)
            3. Tracks whether commitments survive each round
            4. Compares baseline (drifts) vs enforced (preserves)
            
            *⏱️ Takes ~20-40 seconds on CPU. Models load on first run.*
            """)
        
        with gr.Column(scale=3):
            commitments_display = gr.Markdown(label="Extracted Commitments")
            results_table = gr.Dataframe(label="Stability Over Iterations")
            results_plot = gr.Plot(label="Comparison Chart")
            summary_display = gr.Markdown(label="Summary")
    
    # Event handlers
    def update_signal_from_preset(preset_name):
        return DEMO_SIGNALS[preset_name]
    
    preset_dropdown.change(
        fn=update_signal_from_preset,
        inputs=[preset_dropdown],
        outputs=[signal_input]
    )
    
    run_btn.click(
        fn=run_comparison,
        inputs=[signal_input, iterations_slider],
        outputs=[commitments_display, results_table, results_plot, summary_display]
    )
    
    gr.Markdown("""
    ---
    
    ## 📖 About This Framework
    
    This demonstrates the **commitment conservation principle**: meaningful commitments in language should be preserved 
    under compression and recursive application. The full harness tests 5 signals over 10 iterations and shows 
    **baseline systems fail (20% stability) while enforced systems succeed (60% stability)** — a 40pp empirical gap.
    
    ### 🔄 Current Limitations & Roadmap
    
    **Demo Status:** ✅ Functional proof-of-concept showing visual differentiation
    
    **Known Enhancements Coming Soon:**
    
    🔄 **Enforcement Stability Tuning** — Current results show 33-67% fidelity vs paper's 60% baseline. Root cause: Re-injected commitments can be lost in subsequent transformations. *Priority: Preserve commitments through full iteration pipeline.*

    
    🔄 **Output Text Comparison** — Demo currently shows graphs but not the actual text output. Users can't see the qualitative difference (baseline drift: "fam! 😂 You got this 💪" vs enforced preservation: "$100 Friday"). *Priority: Add side-by-side original→final comparison with commitment highlighting.*

    
    🔄 **Token Tracking** — No real-time token counts per turn to show efficiency gains. Test data proves **163% efficiency advantage** (baseline expands +79.6%, enforcement compresses -77.8%). *Priority: Display running token totals.*

    
    🔄 **Baseline Realism** — Currently uses BART compression for both baseline and enforced. Real LLMs expand via conversational drift. *Note: Documented as simulation limitation.*

    
    📊 **Validated Test Data:** Comprehensive analysis shows baseline expansion (230-316 tokens) vs enforcement compression (120-156 tokens) with 62% token reduction. [View full interactive analysis →](https://gemini.google.com/share/8f46bbc61c2c)
    
    **Research Harness:** Original git repository implements full paper methodology with spacy NLP and comprehensive metrics (13/13 tests passing).
    
    ---


    
    **⚖️ IP Notice:** MO§ES™ is a trademark of Ello Cello LLC. See [repo](https://huggingface.co/burnmydays/commitment_conservation_harness) for details.
    
    © 2026 Ello Cello LLC. All rights reserved.
    """)

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