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dvnc_ai_hf/README.md

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+# DVNC.AI Hugging Face Space
+
+A premium Gradio app for scientific discovery workflows with:
+- cinematic chat interface inspired by modern editorial AI products
+- 3D-style connectome view with active path illumination
+- 7-agent reasoning timeline
+- flippable candidate cards for alternative discovery paths
+- model switching UI for orchestration tiers
+
+## Files
+- `app.py` — entrypoint for Hugging Face Spaces
+- `requirements.txt` — Python dependencies
+- `assets/` — reserved for local assets if needed
+
+## Deploy
+1. Create a new **Gradio** Space on Hugging Face.
+2. Upload the full folder contents.
+3. Ensure the entry file is `app.py`.
+4. Launch.
+
+## Notes
+This package includes a working front-end prototype and demo orchestration logic. Replace the mock `run_discovery()` function with your production Claude / routing / graph backend.

dvnc_ai_hf/app.py

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+import json
+import math
+import random
+import gradio as gr
+
+MODELS = [
+    {"name": "DVNC Sovereign", "tag": "flagship", "desc": "Maximum depth orchestration for frontier discovery"},
+    {"name": "DVNC Atlas", "tag": "research", "desc": "Balanced reasoning, graph traversal, and synthesis"},
+    {"name": "DVNC Curie", "tag": "lab", "desc": "Experimental hypothesis generation for anomalous signals"},
+]
+
+AGENTS = [
+    "Query Interpreter",
+    "Graph Divergence Mapper",
+    "Evidence Harvester",
+    "Analogy Engine",
+    "Hypothesis Composer",
+    "Adversarial Critic",
+    "Experimental Program Designer",
+]
+
+NODES = [
+    {"id": "seed", "label": "Seed Query", "group": "core", "x": 0, "y": 0, "z": 0},
+    {"id": "bio", "label": "Biomaterials", "group": "domain", "x": 18, "y": 12, "z": -8},
+    {"id": "card", "label": "Cardiac Repair", "group": "domain", "x": 34, "y": 2, "z": 14},
+    {"id": "nano", "label": "Nanostructure", "group": "bridge", "x": 14, "y": -18, "z": 16},
+    {"id": "selfasm", "label": "Self-Assembly", "group": "bridge", "x": 30, "y": -16, "z": -16},
+    {"id": "electro", "label": "Electro-signalling", "group": "mechanism", "x": 48, "y": 12, "z": -10},
+    {"id": "immune", "label": "Immune Modulation", "group": "mechanism", "x": 54, "y": -8, "z": 10},
+    {"id": "trial", "label": "Validation Path", "group": "outcome", "x": 70, "y": 0, "z": 0},
+    {"id": "alt1", "label": "Piezoelectric Scaffold", "group": "candidate", "x": 44, "y": 28, "z": 14},
+    {"id": "alt2", "label": "Peptide Mesh", "group": "candidate", "x": 42, "y": -28, "z": -14},
+]
+
+EDGES = [
+    ("seed", "bio"), ("seed", "nano"), ("bio", "card"), ("nano", "selfasm"),
+    ("selfasm", "electro"), ("card", "immune"), ("electro", "trial"), ("immune", "trial"),
+    ("card", "alt1"), ("selfasm", "alt2"), ("alt1", "trial"), ("alt2", "trial")
+]
+
+DEFAULT_PATH = ["seed", "nano", "selfasm", "electro", "trial"]
+
+CANDIDATES = [
+    {
+        "title": "Piezoelectric Scaffold Cascade",
+        "front": "Use mechano-electric scaffolds to convert cardiac strain into micro-current signalling.",
+        "back": "Discovery path: anomalous healing signal -> piezoelectric analog -> ion-channel entrainment -> tissue regeneration. Risk: power density and fibrosis coupling.",
+        "score": 92,
+        "novelty": "High",
+        "agent": "Hypothesis Composer"
+    },
+    {
+        "title": "Peptide Self-Assembly Mesh",
+        "front": "Deploy dynamic peptide meshes that self-assemble around damaged myocardium and guide repair.",
+        "back": "Discovery path: self-assembly -> local immune choreography -> regenerative substrate formation. Risk: degradation timing and targeting specificity.",
+        "score": 88,
+        "novelty": "High",
+        "agent": "Analogy Engine"
+    },
+    {
+        "title": "Immune-Tuned Conductive Hydrogel",
+        "front": "Blend conductivity with macrophage-state modulation to reduce scarring and restore conduction.",
+        "back": "Discovery path: inflammation mismatch -> conductive medium -> macrophage polarization -> synchronized healing. Risk: persistence and biocompatibility.",
+        "score": 85,
+        "novelty": "Medium-High",
+        "agent": "Adversarial Critic"
+    }
+]
+
+
+def build_connectome_html(path_ids):
+    active = set(path_ids)
+    node_map = {n['id']: n for n in NODES}
+    lines = []
+    for a, b in EDGES:
+        na, nb = node_map[a], node_map[b]
+        active_edge = a in active and b in active and abs(path_ids.index(a) - path_ids.index(b)) == 1 if a in path_ids and b in path_ids else False
+        cls = "edge active" if active_edge else "edge"
+        lines.append(f'<line class="{cls}" x1="{na["x"]*6+420:.1f}" y1="{na["y"]*6+280:.1f}" x2="{nb["x"]*6+420:.1f}" y2="{nb["y"]*6+280:.1f}" />')
+    circles = []
+    labels = []
+    for n in NODES:
+        cx = n['x']*6 + 420
+        cy = n['y']*6 + 280
+        cls = f"node {n['group']} {'active' if n['id'] in active else ''}"
+        circles.append(f'<g class="node-wrap"><circle class="{cls}" cx="{cx:.1f}" cy="{cy:.1f}" r="{17 if n["id"] in active else 12}" /><circle class="halo {'active' if n['id'] in active else ''}" cx="{cx:.1f}" cy="{cy:.1f}" r="{28 if n["id"] in active else 0}" /></g>')
+        labels.append(f'<text class="label {'active' if n["id"] in active else ''}" x="{cx+16:.1f}" y="{cy-16:.1f}">{n["label"]}</text>')
+    return f'''
+    <div class="brain-shell">
+      <div class="brain-header">
+        <div>
+          <p class="eyebrow">Connectome</p>
+          <h3>Concept Brain</h3>
+        </div>
+        <div class="brain-legend">
+          <span><i class="dot dot-live"></i> active route</span>
+          <span><i class="dot dot-node"></i> concept nodes</span>
+        </div>
+      </div>
+      <div class="brain-stage">
+        <div class="orb orb-a"></div><div class="orb orb-b"></div>
+        <svg viewBox="0 0 840 560" class="brain-svg" role="img" aria-label="DVNC connectome visualisation">
+          <defs>
+            <filter id="glow"><feGaussianBlur stdDeviation="4" result="c"/><feMerge><feMergeNode in="c"/><feMergeNode in="SourceGraphic"/></feMerge></filter>
+          </defs>
+          {''.join(lines)}
+          {''.join(circles)}
+          {''.join(labels)}
+        </svg>
+      </div>
+    </div>
+    '''
+
+
+def build_cards_html(cards):
+    items = []
+    for i, c in enumerate(cards):
+        items.append(f'''
+        <article class="candidate-card" tabindex="0">
+          <div class="candidate-card-inner">
+            <div class="candidate-face candidate-front">
+              <div class="candidate-top"><span class="chip">{c['agent']}</span><span class="score">{c['score']}</span></div>
+              <h4>{c['title']}</h4>
+              <p>{c['front']}</p>
+              <div class="meta-row"><span>Novelty</span><strong>{c['novelty']}</strong></div>
+              <button class="mini">Flip insight</button>
+            </div>
+            <div class="candidate-face candidate-back">
+              <div class="candidate-top"><span class="chip alt">Alternative path</span><span class="score">{c['score']}</span></div>
+              <h4>{c['title']}</h4>
+              <p>{c['back']}</p>
+              <div class="meta-row"><span>Swap into route</span><strong>Enabled</strong></div>
+              <button class="mini">Return</button>
+            </div>
+          </div>
+        </article>
+        ''')
+    return '<div class="candidate-grid">' + ''.join(items) + '</div>'
+
+
+def build_agent_timeline(reasoning):
+    rows = []
+    for r in reasoning:
+        rows.append(f'''
+        <div class="agent-step">
+          <div class="agent-index">{r['step']}</div>
+          <div class="agent-copy">
+            <div class="agent-head"><h4>{r['agent']}</h4><span>{r['tag']}</span></div>
+            <p>{r['summary']}</p>
+          </div>
+        </div>
+        ''')
+    return '<div class="timeline">' + ''.join(rows) + '</div>'
+
+
+def build_chat_html(query, result):
+    bubbles = f'''
+    <div class="chat-thread">
+      <div class="bubble bubble-user">
+        <span class="role">You</span>
+        <p>{query}</p>
+      </div>
+      <div class="bubble bubble-ai">
+        <span class="role">DVNC Sovereign</span>
+        <p>{result['summary']}</p>
+      </div>
+      <div class="bubble bubble-system">
+        <span class="role">Discovery Signal</span>
+        <p><strong>Primary hypothesis:</strong> {result['primary_hypothesis']}</p>
+      </div>
+    </div>
+    '''
+    return bubbles
+
+
+def build_models_html(selected):
+    items = []
+    for m in MODELS:
+        active = 'active' if m['name'] == selected else ''
+        items.append(f'''<div class="model-pill {active}"><span class="model-name">{m['name']}</span><span class="model-tag">{m['tag']}</span><small>{m['desc']}</small></div>''')
+    return '<div class="model-switcher">' + ''.join(items) + '</div>'
+
+
+def run_discovery(query, model_name):
+    random.seed(len(query) + len(model_name))
+    if "curie" in query.lower() or "einstein" in query.lower():
+        primary = "Map the anomaly first, then force a distant analogy before composing the experimental programme."
+        path = ["seed", "bio", "card", "immune", "trial"]
+    else:
+        primary = "Use a self-assembling conductive scaffold that transforms mechanical strain into local regenerative signalling."
+        path = DEFAULT_PATH
+    summaries = [
+        "Normalises the user prompt into a graph-searchable seed and isolates the tension inside the question.",
+        "Finds remote conceptual bridges instead of staying near the starting domain cluster.",
+        "Pulls evidence packets and conflict signals required for grounded hypothesis formation.",
+        "Generates cross-domain analogies with a bias toward mechanism transfer rather than keyword similarity.",
+        "Composes the lead hypothesis and two structurally different variants.",
+        "Attacks weak assumptions, hidden confounders, and feasibility gaps.",
+        "Produces a staged validation plan with measurable falsification criteria."
+    ]
+    reasoning = [{"step": i+1, "agent": AGENTS[i], "tag": ["input","graph","evidence","analogy","compose","critique","experiment"][i], "summary": summaries[i]} for i in range(7)]
+    result = {
+        "summary": "A deeper route was chosen through the concept graph, with live alternatives preserved as candidate cards so the reasoning path can be swapped rather than hidden.",
+        "primary_hypothesis": primary,
+        "reasoning": reasoning,
+        "cards": CANDIDATES,
+        "path": path,
+        "metrics": {
+            "Novelty": 93,
+            "Mechanistic clarity": 89,
+            "Experimental tractability": 82,
+            "Cross-domain distance": 91
+        }
+    }
+    chat_html = build_chat_html(query, result)
+    connectome_html = build_connectome_html(path)
+    cards_html = build_cards_html(CANDIDATES)
+    timeline_html = build_agent_timeline(reasoning)
+    metrics = "\n".join([f"- {k}: {v}/100" for k, v in result["metrics"].items()])
+    hypothesis = f"""# Discovery Output\n\n**Model:** {model_name}\n\n**Primary hypothesis:** {result['primary_hypothesis']}\n\n## Scoring\n{metrics}\n\n## Experimental outline\n1. Construct the candidate material or protocol.\n2. Test mechanistic signal expression under controlled conditions.\n3. Compare against baseline and nearest-neighbour alternatives.\n4. Falsify using the adversarial risk criteria surfaced in the reasoning path.\n"""
+    return chat_html, connectome_html, timeline_html, cards_html, hypothesis, build_models_html(model_name)
+
+CSS = r'''
+:root {
+  --bg: #08090c;
+  --bg2: #0d1015;
+  --panel: rgba(17,20,27,.78);
+  --panel-solid: #0f131a;
+  --panel-2: rgba(21,26,35,.86);
+  --line: rgba(255,255,255,.08);
+  --soft: rgba(255,255,255,.55);
+  --text: #f5f7fb;
+  --muted: #8f99ab;
+  --gold: #f2d293;
+  --teal: #7be7dd;
+  --blue: #7ea9ff;
+  --shadow: 0 20px 60px rgba(0,0,0,.38);
+  --radius: 22px;
+}
+html, body, .gradio-container {
+  background: radial-gradient(circle at top left, rgba(120,144,255,.14), transparent 26%),
+              radial-gradient(circle at 90% 10%, rgba(242,210,147,.11), transparent 22%),
+              linear-gradient(180deg, #050608 0%, #090b10 50%, #07080c 100%) !important;
+  color: var(--text) !important;
+  font-family: Inter, ui-sans-serif, system-ui, sans-serif;
+}
+.gradio-container {max-width: 1550px !important; padding: 22px !important;}
+#dvnc-shell {border: 1px solid var(--line); border-radius: 28px; overflow: hidden; background: linear-gradient(180deg, rgba(14,17,24,.86), rgba(7,8,12,.9)); box-shadow: var(--shadow); backdrop-filter: blur(18px);}
+#dvnc-shell .wrap {display:grid; grid-template-columns: 1.12fr .88fr; min-height: 84vh;}
+#dvnc-shell .left, #dvnc-shell .right {padding: 24px;}
+#dvnc-shell .left {border-right: 1px solid var(--line); display:flex; flex-direction:column; gap:18px;}
+#dvnc-shell .right {display:grid; grid-template-rows:auto auto 1fr auto; gap:18px;}
+.hero-bar {display:flex; justify-content:space-between; align-items:center; gap:16px; padding-bottom:8px;}
+.brand {display:flex; align-items:center; gap:14px;}
+.logo {width:42px; height:42px; border-radius:14px; background: linear-gradient(135deg, rgba(242,210,147,.28), rgba(123,231,221,.16)); border:1px solid rgba(255,255,255,.1); display:grid; place-items:center; box-shadow: inset 0 1px 0 rgba(255,255,255,.08);}
+.logo svg {width:24px; height:24px; color: var(--gold);}
+.brand h1 {font-size: 1.05rem; margin:0; font-weight: 650; letter-spacing:.02em;}
+.brand p {margin:3px 0 0; color:var(--muted); font-size:.84rem;}
+.status {display:flex; gap:10px; align-items:center; color:var(--soft); font-size:.85rem;}
+.status-dot {width:10px; height:10px; border-radius:50%; background:var(--teal); box-shadow:0 0 0 6px rgba(123,231,221,.08), 0 0 18px rgba(123,231,221,.4);}
+.panel {background: linear-gradient(180deg, rgba(17,20,27,.82), rgba(12,14,19,.76)); border:1px solid var(--line); border-radius: 22px; box-shadow: inset 0 1px 0 rgba(255,255,255,.04);}
+.chat-panel {padding:18px; display:flex; flex-direction:column; gap:14px; min-height: 340px;}
+.querybox textarea, .querybox input {background: transparent !important; color: var(--text) !important;}
+.querybox, .querybox > div {background: rgba(255,255,255,.02) !important; border-radius: 18px !important; border-color: var(--line) !important;}
+.chat-thread {display:flex; flex-direction:column; gap:14px;}
+.bubble {max-width: 86%; padding:16px 18px; border-radius: 22px; position:relative; border:1px solid var(--line);}
+.bubble p {margin:8px 0 0; line-height:1.6; font-size:.96rem;}
+.bubble .role {font-size:.72rem; letter-spacing:.12em; text-transform:uppercase; color:var(--muted);}
+.bubble-user {align-self:flex-end; background: linear-gradient(135deg, rgba(126,169,255,.18), rgba(126,169,255,.08));}
+.bubble-ai {align-self:flex-start; background: linear-gradient(135deg, rgba(255,255,255,.06), rgba(255,255,255,.03));}
+.bubble-system {align-self:flex-start; background: linear-gradient(135deg, rgba(242,210,147,.12), rgba(242,210,147,.04));}
+.model-switcher {display:grid; grid-template-columns:repeat(3,1fr); gap:12px;}
+.model-pill {padding:14px; border:1px solid var(--line); border-radius:18px; background:rgba(255,255,255,.02); display:flex; flex-direction:column; gap:4px; min-height: 96px;}
+.model-pill.active {border-color: rgba(242,210,147,.45); background: linear-gradient(135deg, rgba(242,210,147,.14), rgba(255,255,255,.03)); box-shadow: inset 0 0 0 1px rgba(242,210,147,.08);}
+.model-name {font-weight:650;}
+.model-tag {font-size:.76rem; text-transform:uppercase; letter-spacing:.12em; color:var(--gold);}
+.model-pill small {color:var(--muted); line-height:1.45;}
+.brain-shell {padding:18px; height:100%;}
+.brain-header {display:flex; justify-content:space-between; align-items:flex-end; gap:16px; margin-bottom:10px;}
+.eyebrow {font-size:.72rem; letter-spacing:.16em; text-transform:uppercase; color:var(--gold); margin:0 0 4px;}
+.brain-header h3 {margin:0; font-size:1.12rem;}
+.brain-legend {display:flex; gap:14px; color:var(--muted); font-size:.8rem; flex-wrap:wrap;}
+.dot {width:10px; height:10px; display:inline-block; border-radius:50%; margin-right:6px;}
+.dot-live {background:var(--gold); box-shadow:0 0 12px rgba(242,210,147,.65);} .dot-node {background:var(--teal);}
+.brain-stage {position:relative; min-height: 360px; border-radius:20px; overflow:hidden; background: radial-gradient(circle at center, rgba(126,169,255,.08), transparent 35%), linear-gradient(180deg, rgba(255,255,255,.02), rgba(255,255,255,.01)); border:1px solid rgba(255,255,255,.05);}
+.orb {position:absolute; border-radius:50%; filter: blur(40px); opacity:.28; pointer-events:none;}
+.orb-a {width:180px; height:180px; background:rgba(123,231,221,.16); left:10%; top:18%;}
+.orb-b {width:220px; height:220px; background:rgba(242,210,147,.12); right:8%; bottom:12%;}
+.brain-svg {width:100%; height:100%; min-height:360px; transform: perspective(1200px) rotateX(10deg) rotateY(-8deg) scale(1.02);}
+.edge {stroke: rgba(132,153,184,.18); stroke-width:2.2;}
+.edge.active {stroke: var(--gold); stroke-width:3.4; filter:url(#glow); stroke-dasharray: 8 8; animation: pulseEdge 2.8s linear infinite;}
+.node {fill:#7ea9ff; opacity:.88;}
+.node.core {fill:#f5f7fb;} .node.domain {fill:#7be7dd;} .node.bridge {fill:#8ea4ff;} .node.mechanism {fill:#f2d293;} .node.outcome {fill:#ffd9a6;} .node.candidate {fill:#ddadff;}
+.node.active {stroke:#fff7df; stroke-width:2.4; filter:url(#glow);}
+.halo {fill:none;} .halo.active {stroke: rgba(242,210,147,.22); stroke-width:10;}
+.label {fill: rgba(245,247,251,.68); font-size: 12px; letter-spacing: .02em;} .label.active {fill: #fff8e8;}
+.timeline {display:flex; flex-direction:column; gap:10px;}
+.agent-step {display:grid; grid-template-columns:42px 1fr; gap:12px; padding:12px; border:1px solid var(--line); border-radius:18px; background:rgba(255,255,255,.02);}
+.agent-index {width:42px; height:42px; border-radius:14px; display:grid; place-items:center; font-weight:700; color:var(--gold); background:rgba(242,210,147,.08); border:1px solid rgba(242,210,147,.16);}
+.agent-head {display:flex; justify-content:space-between; gap:12px; align-items:center; margin-bottom:4px;}
+.agent-head h4 {margin:0; font-size:.98rem;} .agent-head span {font-size:.72rem; letter-spacing:.12em; text-transform:uppercase; color:var(--muted);} .agent-copy p {margin:0; color:#cbd3e2; font-size:.9rem; line-height:1.55;}
+.candidate-grid {display:grid; grid-template-columns: repeat(3, minmax(0, 1fr)); gap:14px;}
+.candidate-card {background:none; perspective:1200px; min-height: 250px;}
+.candidate-card-inner {position:relative; width:100%; height:100%; min-height:250px; transition: transform .8s cubic-bezier(.2,.7,.1,1); transform-style:preserve-3d;}
+.candidate-card:hover .candidate-card-inner, .candidate-card:focus .candidate-card-inner, .candidate-card:focus-within .candidate-card-inner {transform: rotateY(180deg) translateY(-4px);}
+.candidate-face {position:absolute; inset:0; padding:18px; border-radius:22px; border:1px solid var(--line); background: linear-gradient(180deg, rgba(19,22,29,.96), rgba(11,13,18,.96)); backface-visibility:hidden; box-shadow: inset 0 1px 0 rgba(255,255,255,.04), var(--shadow); display:flex; flex-direction:column; gap:12px;}
+.candidate-back {transform: rotateY(180deg); background: linear-gradient(180deg, rgba(29,23,16,.96), rgba(12,11,10,.96));}
+.candidate-top {display:flex; justify-content:space-between; align-items:center; gap:8px;}
+.chip {font-size:.72rem; text-transform:uppercase; letter-spacing:.12em; color:var(--teal); padding:7px 10px; border-radius:999px; background:rgba(123,231,221,.08); border:1px solid rgba(123,231,221,.18);} .chip.alt {color:var(--gold); background:rgba(242,210,147,.08); border-color: rgba(242,210,147,.18);}
+.score {font-weight:700; color:var(--gold);}
+.candidate-face h4 {margin:0; font-size:1rem; line-height:1.3;}
+.candidate-face p {margin:0; color:#cbd3e2; line-height:1.6; font-size:.92rem;}
+.meta-row {margin-top:auto; display:flex; justify-content:space-between; color:var(--muted); font-size:.84rem;}
+.mini {margin-top:8px; align-self:flex-start; color:var(--text); padding:10px 12px; border-radius:14px; border:1px solid var(--line); background:rgba(255,255,255,.03);}
+.prosebox {padding:18px; white-space:pre-wrap; font-family: ui-monospace, SFMono-Regular, Menlo, monospace; line-height:1.55; color:#dfe6f3;}
+.gr-button-primary {background: linear-gradient(135deg, rgba(242,210,147,.92), rgba(224,178,92,.92)) !important; color:#1b1408 !important; border: none !important;}
+.gr-button-secondary {background: rgba(255,255,255,.05) !important; color: var(--text) !important; border: 1px solid var(--line) !important;}
+footer {display:none !important;}
+@keyframes pulseEdge {to {stroke-dashoffset: -32;}}
+@media (max-width: 1180px){#dvnc-shell .wrap{grid-template-columns:1fr;}#dvnc-shell .left{border-right:0;border-bottom:1px solid var(--line)}.candidate-grid,.model-switcher{grid-template-columns:1fr;} }
+'''
+
+HEAD = '''
+<link rel="preconnect" href="https://fonts.googleapis.com">
+<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&display=swap" rel="stylesheet">
+'''
+
+with gr.Blocks(css=CSS, head=HEAD, theme=gr.themes.Base(), fill_height=True) as demo:
+    gr.HTML('''
+    <div id="dvnc-shell">
+      <div class="wrap">
+        <section class="left">
+          <div class="hero-bar">
+            <div class="brand">
+              <div class="logo" aria-hidden="true">
+                <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.7"><path d="M5 17L12 4l7 13"/><path d="M8.5 12.5h7"/><circle cx="12" cy="12" r="1.8" fill="currentColor" stroke="none"/></svg>
+              </div>
+              <div>
+                <h1>DVNC.AI</h1>
+                <p>Sovereign discovery interface · connectome-native reasoning</p>
+              </div>
+            </div>
+            <div class="status"><span class="status-dot"></span><span>Live orchestration</span></div>
+          </div>
+    ''')
+    model_html = gr.HTML(build_models_html("DVNC Sovereign"))
+    with gr.Row():
+        model = gr.Dropdown(choices=[m["name"] for m in MODELS], value="DVNC Sovereign", label="Model tier")
+    query = gr.Textbox(label="Discovery query", elem_classes=["querybox"], placeholder="Enter a scientific question, anomaly, or breakthrough direction…", lines=4)
+    with gr.Row():
+        run_btn = gr.Button("Run discovery", variant="primary")
+        example_btn = gr.Button("Load example", variant="secondary")
+    chat = gr.HTML('<div class="panel chat-panel"><div class="chat-thread"><div class="bubble bubble-ai"><span class="role">DVNC</span><p>Enter a query to activate the 7-agent discovery stack and illuminate the chosen path through the concept brain.</p></div></div></div>')
+    gr.HTML('</section><section class="right">')
+    connectome = gr.HTML(build_connectome_html(DEFAULT_PATH))
+    timeline = gr.HTML('<div class="panel" style="padding:18px"><div class="timeline"></div></div>')
+    cards = gr.HTML('<div class="panel" style="padding:18px"><div class="candidate-grid"></div></div>')
+    output = gr.Markdown("# Discovery Output\n\nAwaiting query.")
+    gr.HTML('</section></div></div>')
+
+    def load_example():
+        return "How could a self-assembling conductive biomaterial improve cardiac tissue regeneration by converting mechanical strain into repair signalling?"
+
+    example_btn.click(fn=load_example, outputs=query)
+    run_btn.click(fn=run_discovery, inputs=[query, model], outputs=[chat, connectome, timeline, cards, output, model_html])
+
+if __name__ == "__main__":
+    demo.launch()

dvnc_ai_hf/requirements.txt

@@ -0,0 +1,4 @@
+gradio>=5.29.0
+networkx>=3.2
+numpy>=1.26.0
+plotly>=5.24.0