Create app.py

app.py

@@ -0,0 +1,178 @@
+"""
+YSenseAI Wisdom Canvas Demo v4.5-Beta
+A demonstration of the Story-First UX for ethical AI training data collection.
+"""
+
+import gradio as gr
+import hashlib
+import uuid
+import json
+from datetime import datetime
+
+def generate_attribution(story, author_name):
+    timestamp = datetime.utcnow().isoformat() + "Z"
+    content_hash = hashlib.sha256(story.encode('utf-8')).hexdigest()
+    did = f"did:ysense:{uuid.uuid4().hex[:16]}"
+    return {
+        "did": did,
+        "content_hash": content_hash,
+        "author": author_name or "Anonymous",
+        "timestamp": timestamp,
+        "version": "v4.5-beta",
+        "protocol": "Z-Protocol v2.0"
+    }
+
+def analyze_perception_layers(story):
+    word_count = len(story.split())
+    sentences = story.split('.')
+    return {
+        "narrative": f"A personal story with {len(sentences)} key moments, exploring meaningful experiences.",
+        "somatic": "Physical presence and embodied experience are woven throughout the narrative.",
+        "attention": "The author's focus reveals what matters most in this moment of reflection.",
+        "synesthetic": "Sensory details paint a vivid picture of the experience.",
+        "temporal": f"The story unfolds across time, with {word_count} words capturing the journey."
+    }
+
+def distill_essence(story, layers):
+    words = story.lower().split()
+    common = {'about', 'there', 'would', 'could', 'should', 'their', 'these', 'those', 'which', 'where', 'being'}
+    meaningful = [w.strip('.,!?') for w in words if len(w) > 5 and w not in common][:3]
+    if len(meaningful) < 3:
+        meaningful = ["wisdom", "growth", "journey"]
+    return {
+        "essence": " ".join(meaningful[:3]).title(),
+        "meaning": "These words capture the core themes of your story.",
+        "wisdom_type": "personal growth"
+    }
+
+def calculate_quality_metrics(story, layers, essence):
+    word_count = len(story.split())
+    sentence_count = len([s for s in story.split('.') if s.strip()])
+    metrics = {
+        "authenticity": min(100, 50 + (word_count // 10)),
+        "emotional_depth": min(100, 60 + len(layers.get("somatic", "")) // 2),
+        "narrative_clarity": min(100, 70 if sentence_count > 3 else 50),
+        "cultural_richness": min(100, 55 + (word_count // 20)),
+        "wisdom_density": min(100, 65 if essence.get("wisdom_type") else 45),
+        "training_value": 0
+    }
+    metrics["training_value"] = sum(list(metrics.values())[:-1]) // 5
+    return metrics
+
+def process_story(story, author_name, consent_research, consent_commercial, consent_derivative, consent_attribution):
+    if not story or len(story.strip()) < 50:
+        return "Please write at least 50 characters to analyze.", "", "", "", "", ""
+    
+    attribution = generate_attribution(story, author_name)
+    attribution_display = f"""### Attribution Record
+| Field | Value |
+|-------|-------|
+| **DID** | `{attribution['did']}` |
+| **Content Hash** | `{attribution['content_hash'][:32]}...` |
+| **Author** | {attribution['author']} |
+| **Timestamp** | {attribution['timestamp']} |
+| **Protocol** | {attribution['protocol']} |"""
+    
+    layers = analyze_perception_layers(story)
+    layers_display = f"""### 5-Layer Perception Analysis
+**Narrative Layer**: {layers.get('narrative', 'N/A')}
+
+**Somatic Layer**: {layers.get('somatic', 'N/A')}
+
+**Attention Layer**: {layers.get('attention', 'N/A')}
+
+**Synesthetic Layer**: {layers.get('synesthetic', 'N/A')}
+
+**Temporal Layer**: {layers.get('temporal', 'N/A')}"""
+    
+    essence = distill_essence(story, layers)
+    essence_display = f"""### 3-Word Essence
+# {essence.get('essence', 'Wisdom Awaits You')}
+**Meaning:** {essence.get('meaning', 'Your story holds unique wisdom.')}
+**Wisdom Type:** {essence.get('wisdom_type', 'personal growth').title()}"""
+    
+    metrics = calculate_quality_metrics(story, layers, essence)
+    metrics_display = f"""### Quality Metrics
+| Metric | Score |
+|--------|-------|
+| Authenticity | {metrics['authenticity']}% |
+| Emotional Depth | {metrics['emotional_depth']}% |
+| Narrative Clarity | {metrics['narrative_clarity']}% |
+| Cultural Richness | {metrics['cultural_richness']}% |
+| Wisdom Density | {metrics['wisdom_density']}% |
+| **Training Value** | **{metrics['training_value']}%** |"""
+    
+    consent_types = []
+    if consent_research: consent_types.append("Research Use")
+    if consent_commercial: consent_types.append("Commercial Training")
+    if consent_derivative: consent_types.append("Derivative Works")
+    if consent_attribution: consent_types.append("Public Attribution")
+    if not consent_types: consent_types.append("No consent granted")
+    
+    consent_display = f"""### Z-Protocol v2.0 Consent
+{chr(10).join(['- ' + c for c in consent_types])}
+**Consent Hash:** `{hashlib.sha256(str(consent_types).encode()).hexdigest()[:16]}`
+**Revocable:** Yes"""
+    
+    export_data = {
+        "attribution": attribution,
+        "layers": layers,
+        "essence": essence,
+        "metrics": metrics,
+        "consent": {"research": consent_research, "commercial": consent_commercial, "derivative": consent_derivative, "attribution": consent_attribution}
+    }
+    export_display = f"""### Export Preview
+```json
+{json.dumps(export_data, indent=2)}
+```"""
+    
+    return attribution_display, layers_display, essence_display, metrics_display, consent_display, export_display
+
+example_stories = [
+    ["My grandmother taught me to make dumplings when I was seven. Her hands, weathered from decades of work, moved with a grace I couldn't understand then. She never measured anything - a pinch of this, a handful of that. Years later, standing in my own kitchen, I finally understood: she wasn't teaching me to cook. She was passing down a language of love that words could never capture.", "Anonymous"],
+    ["The day I failed my first exam, my father didn't say a word. He just took me fishing. We sat by the lake for hours in silence. When the sun set, he finally spoke: 'The fish don't care about your grades. Neither do I. I care about who you become.' That silence taught me more than any lecture ever could.", "Anonymous"]
+]
+
+with gr.Blocks(title="YSenseAI Wisdom Canvas") as demo:
+    gr.HTML("""<div style="text-align:center;padding:20px;background:linear-gradient(135deg,#0f172a,#1e293b);border-radius:12px;margin-bottom:20px;">
+        <h1 style="color:#f59e0b;font-size:2.5em;margin-bottom:10px;">YSenseAI Wisdom Canvas</h1>
+        <p style="color:#94a3b8;font-size:1.1em;">v4.5-Beta | Share your stories. Preserve your wisdom. Shape ethical AI.</p>
+        <p style="margin-top:10px;"><a href="https://ysenseai.org" target="_blank" style="color:#22d3ee;margin-right:15px;">Website</a>
+        <a href="https://github.com/creator35lwb-web/YSense-AI-Attribution-Infrastructure" target="_blank" style="color:#22d3ee;margin-right:15px;">GitHub</a>
+        <a href="https://doi.org/10.5281/zenodo.17737995" target="_blank" style="color:#22d3ee;">White Paper</a></p></div>""")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("## Story Canvas")
+            story_input = gr.Textbox(label="Your Story", placeholder="Share a meaningful moment...", lines=8)
+            author_input = gr.Textbox(label="Author Name (optional)", placeholder="Anonymous", lines=1)
+            gr.Markdown("### Z-Protocol Consent")
+            with gr.Row():
+                consent_research = gr.Checkbox(label="Research Use", value=True)
+                consent_commercial = gr.Checkbox(label="Commercial Training", value=False)
+            with gr.Row():
+                consent_derivative = gr.Checkbox(label="Derivative Works", value=True)
+                consent_attribution = gr.Checkbox(label="Public Attribution", value=False)
+            analyze_btn = gr.Button("Analyze & Distill", variant="primary")
+            gr.Examples(examples=example_stories, inputs=[story_input, author_input])
+        
+        with gr.Column(scale=1):
+            gr.Markdown("## Analysis Results")
+            with gr.Accordion("Attribution", open=True):
+                attribution_output = gr.Markdown()
+            with gr.Accordion("5-Layer Analysis", open=True):
+                layers_output = gr.Markdown()
+            with gr.Accordion("3-Word Essence", open=True):
+                essence_output = gr.Markdown()
+            with gr.Accordion("Quality Metrics", open=False):
+                metrics_output = gr.Markdown()
+            with gr.Accordion("Consent Record", open=False):
+                consent_output = gr.Markdown()
+            with gr.Accordion("Export Preview", open=False):
+                export_output = gr.Markdown()
+    
+    analyze_btn.click(fn=process_story, inputs=[story_input, author_input, consent_research, consent_commercial, consent_derivative, consent_attribution], outputs=[attribution_output, layers_output, essence_output, metrics_output, consent_output, export_output])
+    gr.HTML("""<footer style="text-align:center;padding:20px;color:#64748b;"><p>2025 YSenseAI | MIT License | <a href="https://doi.org/10.5281/zenodo.17737995" target="_blank">DOI: 10.5281/zenodo.17737995</a></p></footer>""")
+
+if __name__ == "__main__":
+    demo.launch()