Upload app (2).py

dvnc_ai_v2_hf/app (2).py

@@ -0,0 +1,817 @@
+import json
+import math
+import random
+import html
+import urllib.parse
+import xml.etree.ElementTree as ET
+from pathlib import Path
+
+import gradio as gr
+import requests
+
+
+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": 10, "y": 0, "z": 0},
+    {"id": "bio", "label": "Biomaterials", "group": "domain", "x": 24, "y": 12, "z": -8},
+    {"id": "card", "label": "Cardiac Repair", "group": "domain", "x": 38, "y": 3, "z": 14},
+    {"id": "nano", "label": "Nanostructure", "group": "bridge", "x": 24, "y": -18, "z": 16},
+    {"id": "selfasm", "label": "Self-Assembly", "group": "bridge", "x": 40, "y": -16, "z": -16},
+    {"id": "electro", "label": "Electro-signalling", "group": "mechanism", "x": 58, "y": 10, "z": -10},
+    {"id": "immune", "label": "Immune Modulation", "group": "mechanism", "x": 64, "y": -8, "z": 10},
+    {"id": "trial", "label": "Validation Path", "group": "outcome", "x": 80, "y": 0, "z": 0},
+    {"id": "alt1", "label": "Piezoelectric Scaffold", "group": "candidate", "x": 56, "y": 26, "z": 14},
+    {"id": "alt2", "label": "Peptide Mesh", "group": "candidate", "x": 54, "y": -27, "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"
+    }
+]
+
+
+JOURNALS = [
+    {"name": "Nature", "url": "https://www.nature.com/search", "desc": "Flagship multidisciplinary research journal."},
+    {"name": "Science", "url": "https://www.science.org/search", "desc": "High-impact science journal and family."},
+    {"name": "Cell", "url": "https://www.cell.com/search", "desc": "Life sciences and translational biology."},
+    {"name": "The Lancet", "url": "https://www.thelancet.com/search", "desc": "Clinical and medical research."},
+    {"name": "IEEE Xplore", "url": "https://ieeexplore.ieee.org/search/searchresult.jsp", "desc": "Engineering, AI, signal processing, and systems."},
+]
+
+
+def build_connectome_html(path_ids):
+    active = set(path_ids)
+    node_map = {n["id"]: n for n in NODES}
+    path_pairs = set()
+    for i in range(len(path_ids) - 1):
+        path_pairs.add((path_ids[i], path_ids[i + 1]))
+        path_pairs.add((path_ids[i + 1], path_ids[i]))
+
+    lines = []
+    active_lines = []
+    for a, b in EDGES:
+        na, nb = node_map[a], node_map[b]
+        x1, y1 = na["x"] * 8 + 80, na["y"] * 6 + 280
+        x2, y2 = nb["x"] * 8 + 80, nb["y"] * 6 + 280
+        lines.append(f'<line class="edge" x1="{x1:.1f}" y1="{y1:.1f}" x2="{x2:.1f}" y2="{y2:.1f}" />')
+        if (a, b) in path_pairs:
+            active_lines.append(f'<line class="edge active" x1="{x1:.1f}" y1="{y1:.1f}" x2="{x2:.1f}" y2="{y2:.1f}" />')
+
+    circles = []
+    labels = []
+    for n in NODES:
+        cx = n["x"] * 8 + 80
+        cy = n["y"] * 6 + 280
+        is_active = n["id"] in active
+        cls = f'node {n["group"]} {'chosen' if is_active else 'idle'}'
+        halo_cls = f'halo {'active' if is_active else ''}'
+        circles.append(
+            f'<g class="node-wrap">'
+            f'<circle class="{halo_cls}" cx="{cx:.1f}" cy="{cy:.1f}" r="{30 if is_active else 0}" />'
+            f'<circle class="{cls}" cx="{cx:.1f}" cy="{cy:.1f}" r="{18 if is_active else 13}" />'
+            f'</g>'
+        )
+        labels.append(f'<text class="label {'active' if is_active else ''}" x="{cx + 18:.1f}" y="{cy - 16:.1f}">{n["label"]}</text>')
+
+    return f"""
+    <div class="panel brain-shell">
+      <div class="brain-header">
+        <div>
+          <p class="eyebrow">Connectome</p>
+          <h3>3D Connectome</h3>
+        </div>
+        <div class="brain-legend">
+          <span><i class="dot dot-live"></i> lit path</span>
+          <span><i class="dot dot-chosen"></i> chosen node</span>
+          <span><i class="dot dot-idle"></i> available node</span>
+        </div>
+      </div>
+      <div class="brain-stage">
+        <svg viewBox="0 0 780 560" class="brain-svg" role="img" aria-label="DVNC 3D connectome visualisation">
+          {''.join(lines)}
+          {''.join(active_lines)}
+          {''.join(circles)}
+          {''.join(labels)}
+        </svg>
+      </div>
+    </div>
+    """
+
+
+def build_learning_graph_html(nodes, edges, title="Self-Learning Knowledge Graph"):
+    if not nodes:
+        return """
+        <div class="panel brain-shell">
+          <div class="brain-header">
+            <div>
+              <p class="eyebrow">Learning Graph</p>
+              <h3>Self-Learning Knowledge Graph</h3>
+            </div>
+          </div>
+          <div class="brain-stage learning-empty">
+            <div class="empty-graph-copy">
+              <h4>No papers mapped yet</h4>
+              <p>Search papers, pick a topic, or upload a PDF to grow the graph in real time.</p>
+            </div>
+          </div>
+        </div>
+        """
+
+    node_items = []
+    label_items = []
+    edge_items = []
+    coords = [(110, 110), (320, 80), (540, 130), (660, 270), (550, 410), (300, 450), (110, 340), (380, 260)]
+
+    for i, node in enumerate(nodes[:8]):
+        x, y = coords[i]
+        node["sx"] = x
+        node["sy"] = y
+
+    node_map = {n["id"]: n for n in nodes[:8]}
+    for a, b in edges:
+        if a in node_map and b in node_map:
+            na = node_map[a]
+            nb = node_map[b]
+            edge_items.append(
+                f'<line class="learn-edge" x1="{na["sx"]}" y1="{na["sy"]}" x2="{nb["sx"]}" y2="{nb["sy"]}" />'
+            )
+
+    for node in nodes[:8]:
+        klass = f'learn-node {node.get("kind", "paper")}'
+        node_items.append(
+            f'<circle class="{klass}" cx="{node["sx"]}" cy="{node["sy"]}" r="{24 if node.get("kind") == "query" else 20}" />'
+        )
+        label_items.append(
+            f'<text class="learn-label" x="{node["sx"] + 28}" y="{node["sy"] - 10}">{html.escape(node["label"][:44])}</text>'
+        )
+
+    return f"""
+    <div class="panel brain-shell">
+      <div class="brain-header">
+        <div>
+          <p class="eyebrow">Learning Graph</p>
+          <h3>{html.escape(title)}</h3>
+        </div>
+        <div class="brain-legend">
+          <span><i class="dot dot-query"></i> query</span>
+          <span><i class="dot dot-paper"></i> paper</span>
+          <span><i class="dot dot-upload"></i> uploaded PDF</span>
+        </div>
+      </div>
+      <div class="brain-stage">
+        <svg viewBox="0 0 760 520" class="brain-svg" role="img" aria-label="Self-learning knowledge graph">
+          {''.join(edge_items)}
+          {''.join(node_items)}
+          {''.join(label_items)}
+        </svg>
+      </div>
+    </div>
+    """
+
+
+def build_cards_html(cards):
+    items = []
+    for c in 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" type="button">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" type="button">Return</button>
+            </div>
+          </div>
+        </article>
+        """
+        )
+    return '<div class="panel" style="padding:20px;"><div class="candidate-grid">' + ''.join(items) + '</div></div>'
+
+
+def build_agent_timeline(reasoning):
+    rows = []
+    for r in reasoning:
+        rows.append(
+            f"""
+        <details class="agent-step" {'open' if r['step'] == 1 else ''}>
+          <summary class="agent-summary">
+            <div class="agent-index">{r['step']}</div>
+            <div class="agent-head">
+              <h4>{r['agent']}</h4>
+              <span>{r['tag']}</span>
+            </div>
+          </summary>
+          <div class="agent-copy">
+            <p>{r['summary']}</p>
+          </div>
+        </details>
+        """
+        )
+    return '<div class="panel" style="padding:18px;"><div class="timeline">' + ''.join(rows) + '</div></div>'
+
+
+def build_chat_html(query, result):
+    return f"""
+    <div class="panel chat-panel">
+      <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>
+    </div>
+    """
+
+
+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="panel" style="padding:18px;"><div class="model-switcher">' + ''.join(items) + '</div></div>'
+
+
+def build_journal_html(query):
+    q = urllib.parse.quote_plus(query or "biomaterials cardiac repair")
+    rows = []
+    for journal in JOURNALS:
+        url = f"{journal['url']}?q={q}" if "?" not in journal["url"] else f"{journal['url']}&q={q}"
+        if "ieeexplore" in journal["url"]:
+            url = f"https://ieeexplore.ieee.org/search/searchresult.jsp?queryText={q}"
+        rows.append(
+            f"""
+            <a class="journal-card" href="{url}" target="_blank" rel="noopener noreferrer">
+              <div>
+                <h4>{journal['name']}</h4>
+                <p>{journal['desc']}</p>
+              </div>
+              <span>Open</span>
+            </a>
+            """
+        )
+    return '<div class="journal-grid">' + ''.join(rows) + '</div>'
+
+
+def search_arxiv(query, max_results=5):
+    encoded = urllib.parse.quote(query)
+    url = (
+        "http://export.arxiv.org/api/query?search_query=all:"
+        f"{encoded}&start=0&max_results={max_results}&sortBy=relevance&sortOrder=descending"
+    )
+    response = requests.get(url, timeout=20)
+    response.raise_for_status()
+    root = ET.fromstring(response.text)
+    ns = {"atom": "http://www.w3.org/2005/Atom"}
+    papers = []
+    for entry in root.findall("atom:entry", ns):
+        title = " ".join((entry.findtext("atom:title", default="", namespaces=ns) or "").split())
+        summary = " ".join((entry.findtext("atom:summary", default="", namespaces=ns) or "").split())
+        published = entry.findtext("atom:published", default="", namespaces=ns)
+        paper_id = entry.findtext("atom:id", default="", namespaces=ns)
+        authors = [a.findtext("atom:name", default="", namespaces=ns) for a in entry.findall("atom:author", ns)]
+        pdf_url = ""
+        for link in entry.findall("atom:link", ns):
+            if link.attrib.get("title") == "pdf":
+                pdf_url = link.attrib.get("href", "")
+                break
+        papers.append(
+            {
+                "id": paper_id or title,
+                "title": title,
+                "summary": summary,
+                "published": published[:10],
+                "authors": ", ".join([a for a in authors[:4] if a]),
+                "url": paper_id,
+                "pdf": pdf_url,
+            }
+        )
+    return papers
+
+
+def format_papers_html(papers):
+    if not papers:
+        return '<div class="panel papers-panel"><p>No papers found yet.</p></div>'
+
+    items = []
+    for paper in papers:
+        summary = html.escape((paper.get("summary") or "")[:280])
+        items.append(
+            f"""
+            <article class="paper-card">
+              <div class="paper-topline">
+                <span class="paper-badge">{html.escape(paper.get('published', '')) or 'Paper'}</span>
+                <span class="paper-badge alt">{html.escape(paper.get('authors', 'Unknown authors'))}</span>
+              </div>
+              <h4>{html.escape(paper.get('title', 'Untitled'))}</h4>
+              <p>{summary}</p>
+              <div class="paper-links">
+                <a href="{html.escape(paper.get('url', '#'))}" target="_blank" rel="noopener noreferrer">Abstract</a>
+                <a href="{html.escape(paper.get('pdf', '#'))}" target="_blank" rel="noopener noreferrer">PDF</a>
+              </div>
+            </article>
+            """
+        )
+    return '<div class="papers-grid">' + ''.join(items) + '</div>'
+
+
+def uploaded_pdf_summary(file_obj):
+    if not file_obj:
+        return "No PDF uploaded yet."
+    path = getattr(file_obj, "name", None) or str(file_obj)
+    p = Path(path)
+    return f"Uploaded PDF ready for ingestion: {p.name}. You can parse this next with a paper reader, chunker, or citation extractor."
+
+
+def build_learning_graph_state(query, papers, uploaded_name=None):
+    nodes = [{"id": "query", "label": query or "Research topic", "kind": "query"}]
+    edges = []
+    for i, paper in enumerate(papers[:5], start=1):
+        pid = f"paper_{i}"
+        nodes.append({"id": pid, "label": paper["title"], "kind": "paper"})
+        edges.append(("query", pid))
+    if uploaded_name:
+        nodes.append({"id": "upload", "label": uploaded_name, "kind": "upload"})
+        edges.append(("query", "upload"))
+        if len(nodes) > 2:
+            edges.append(("upload", "paper_1"))
+    return nodes, edges
+
+
+def run_paper_search(query, pdf_file):
+    if not query.strip() and not pdf_file:
+        empty_graph = build_learning_graph_html([], [], "Self-Learning Knowledge Graph")
+        return (
+            empty_graph,
+            '<div class="panel papers-panel"><p>Enter a topic or upload a PDF to start learning.</p></div>',
+            build_journal_html("biomaterials cardiac repair"),
+            "No PDF uploaded yet.",
+        )
+
+    papers = []
+    if query.strip():
+        try:
+            papers = search_arxiv(query.strip(), max_results=5)
+        except Exception as e:
+            papers = []
+            error_html = f'<div class="panel papers-panel"><p>Paper search failed: {html.escape(str(e))}</p></div>'
+            graph_nodes, graph_edges = build_learning_graph_state(query.strip(), [], Path(getattr(pdf_file, "name", "uploaded.pdf")).name if pdf_file else None)
+            return (
+                build_learning_graph_html(graph_nodes, graph_edges),
+                error_html,
+                build_journal_html(query.strip()),
+                uploaded_pdf_summary(pdf_file),
+            )
+
+    uploaded_name = None
+    if pdf_file:
+        uploaded_name = Path(getattr(pdf_file, "name", str(pdf_file))).name
+
+    graph_nodes, graph_edges = build_learning_graph_state(query.strip(), papers, uploaded_name)
+    graph_html = build_learning_graph_html(graph_nodes, graph_edges)
+    papers_html = format_papers_html(papers)
+    journals_html = build_journal_html(query.strip())
+    pdf_summary = uploaded_pdf_summary(pdf_file)
+    return graph_html, papers_html, journals_html, pdf_summary
+
+
+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 connectome, with live alternatives preserved as swappable cards so the reasoning path can be inspected 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 = (
+        "# Discovery Output\n\n"
+        f"**Model:** {model_name}\n\n"
+        f"**Primary hypothesis:** {result['primary_hypothesis']}\n\n"
+        "## Scoring\n"
+        f"{metrics}\n\n"
+        "## Experimental outline\n"
+        "1. Construct the candidate material or protocol.\n"
+        "2. Test mechanistic signal expression under controlled conditions.\n"
+        "3. Compare against baseline and nearest-neighbour alternatives.\n"
+        "4. 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: #ffffff;
+  --panel: #ffffff;
+  --line: rgba(0, 0, 0, 0.12);
+  --text: #111111;
+  --muted: #5b5b5b;
+  --soft: rgba(0, 0, 0, 0.62);
+  --gold: #ff6600;
+  --teal: #17b8a6;
+  --blue: #628dff;
+  --chosen: #ff7a1a;
+  --idle: #b8d8ff;
+  --idle-stroke: #5e8fe6;
+  --query-node: #ffd8b3;
+  --paper-node: #d7f6f2;
+  --upload-node: #e7defe;
+  --shadow: 0 16px 40px rgba(0, 0, 0, 0.12);
+}
+html, body, .gradio-container {
+  background: #ffffff !important;
+  font-family: Inter, ui-sans-serif, system-ui, sans-serif;
+}
+.gradio-container {
+  max-width: 1640px !important;
+  padding: 20px !important;
+}
+#dvnc-shell {
+  border: 1px solid var(--line);
+  border-radius: 28px;
+  overflow: hidden;
+  background: #ffffff;
+  box-shadow: var(--shadow);
+  padding: 20px 22px 22px;
+}
+.hero-bar {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  gap: 16px;
+  padding-bottom: 12px;
+  border-bottom: 1px solid rgba(0, 0, 0, 0.06);
+  margin-bottom: 16px;
+}
+.brand { display: flex; align-items: center; gap: 14px; }
+.logo {
+  width: 42px; height: 42px; border-radius: 14px; display: grid; place-items: center; color: var(--gold);
+  background: linear-gradient(135deg, rgba(255, 122, 26, 0.12), rgba(23, 184, 166, 0.10));
+  border: 1px solid rgba(0, 0, 0, 0.08);
+}
+.logo svg { width: 24px; height: 24px; }
+.brand h1 { font-size: 1.05rem; margin: 0; font-weight: 700; letter-spacing: .12em; text-transform: uppercase; }
+.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(23, 184, 166, 0.10), 0 0 14px rgba(23, 184, 166, 0.25);
+}
+.panel {
+  background: #ffffff; border: 1px solid var(--line); border-radius: 22px; box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.8);
+}
+.querybox textarea, .querybox input { background: transparent !important; color: var(--text) !important; }
+.querybox, .querybox > div { background: #ffffff !important; border-radius: 18px !important; border-color: var(--line) !important; }
+.chat-panel { padding: 18px; min-height: 280px; }
+.chat-thread { display: flex; flex-direction: column; gap: 14px; }
+.bubble { max-width: 88%; padding: 16px 18px; border-radius: 22px; border: 1px solid var(--line); }
+.bubble p { margin: 8px 0 0; line-height: 1.6; font-size: .96rem; color: var(--text); }
+.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(98, 141, 255, 0.16), rgba(98, 141, 255, 0.08)); }
+.bubble-ai { align-self: flex-start; background: #ffffff; }
+.bubble-system { align-self: flex-start; background: linear-gradient(135deg, rgba(255, 122, 26, 0.10), rgba(255, 122, 26, 0.04)); }
+.model-switcher { display: grid; grid-template-columns: repeat(3, 1fr); gap: 12px; }
+.model-pill {
+  padding: 14px; border: 1px solid var(--line); border-radius: 18px; display: flex; flex-direction: column; gap: 4px; min-height: 98px; background: #ffffff;
+}
+.model-pill.active {
+  border-color: rgba(255, 122, 26, 0.40);
+  background: linear-gradient(135deg, rgba(255, 122, 26, 0.10), rgba(255, 255, 255, 0.96));
+}
+.model-name { font-weight: 650; color: var(--text); }
+.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; }
+.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; color: var(--text); }
+.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(--chosen); box-shadow: 0 0 10px rgba(255, 122, 26, 0.35); }
+.dot-chosen { background: var(--chosen); }
+.dot-idle { background: var(--idle); border: 1px solid var(--idle-stroke); }
+.dot-query { background: var(--query-node); border: 1px solid #de9e58; }
+.dot-paper { background: var(--paper-node); border: 1px solid #4fb3a5; }
+.dot-upload { background: var(--upload-node); border: 1px solid #8f73d9; }
+.brain-stage {
+  position: relative; min-height: 420px; overflow: hidden; background: linear-gradient(180deg, rgba(250, 250, 250, 1), rgba(255, 255, 255, 1));
+  border: 1px solid rgba(0, 0, 0, 0.05); border-radius: 20px;
+}
+.brain-svg { width: 100%; height: 520px; display: block; }
+.edge { stroke: rgba(0, 0, 0, 0.12); stroke-width: 2.4; }
+.edge.active {
+  stroke: var(--chosen); stroke-width: 4.2; stroke-linecap: round; filter: drop-shadow(0 0 6px rgba(255, 122, 26, 0.45)); stroke-dasharray: 8 12; animation: pulseEdge 1.5s linear infinite;
+}
+.node { stroke-width: 2.2; transition: all .25s ease; }
+.node.idle { fill: var(--idle); stroke: var(--idle-stroke); }
+.node.chosen { fill: var(--chosen); stroke: #ffb16d; }
+.halo { fill: none; }
+.halo.active { stroke: rgba(255, 122, 26, 0.18); stroke-width: 12; }
+.label { fill: #2c2c2c; font-size: 13px; font-weight: 500; letter-spacing: .01em; }
+.label.active { fill: #111111; font-weight: 700; }
+.learn-edge { stroke: rgba(0, 0, 0, 0.18); stroke-width: 2.2; stroke-linecap: round; }
+.learn-node { stroke-width: 2.2; }
+.learn-node.query { fill: var(--query-node); stroke: #de9e58; }
+.learn-node.paper { fill: var(--paper-node); stroke: #36a091; }
+.learn-node.upload { fill: var(--upload-node); stroke: #7e63cb; }
+.learn-label { fill: #1e1e1e; font-size: 12px; font-weight: 600; }
+.learning-empty { display: grid; place-items: center; }
+.empty-graph-copy { text-align: center; max-width: 440px; padding: 40px 20px; }
+.empty-graph-copy h4 { margin: 0 0 10px; font-size: 1.05rem; }
+.empty-graph-copy p { margin: 0; color: var(--muted); line-height: 1.6; }
+.timeline { display: flex; flex-direction: column; gap: 10px; }
+.agent-step { border: 1px solid var(--line); border-radius: 18px; background: #ffffff; overflow: hidden; }
+.agent-summary { list-style: none; display: grid; grid-template-columns: 42px 1fr; gap: 12px; align-items: center; padding: 12px; cursor: pointer; }
+.agent-summary::-webkit-details-marker { display: none; }
+.agent-index {
+  width: 42px; height: 42px; border-radius: 14px; display: grid; place-items: center; font-weight: 700; color: var(--gold);
+  background: rgba(255, 122, 26, 0.08); border: 1px solid rgba(255, 122, 26, 0.18);
+}
+.agent-head { display: flex; justify-content: space-between; gap: 12px; align-items: center; }
+.agent-head h4 { margin: 0; font-size: .98rem; color: var(--text); }
+.agent-head span { font-size: .72rem; letter-spacing: .12em; text-transform: uppercase; color: var(--muted); }
+.agent-copy { padding: 0 14px 16px 66px; }
+.agent-copy p { margin: 0; color: #2d2d2d; font-size: .93rem; line-height: 1.6; }
+.candidate-grid { display: grid; grid-template-columns: repeat(3, minmax(0, 1fr)); gap: 18px; }
+.candidate-card { background: none; perspective: 1400px; min-height: 330px; }
+.candidate-card-inner { position: relative; width: 100%; min-height: 330px; 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); }
+.candidate-face {
+  position: absolute; inset: 0; padding: 20px; border-radius: 22px; border: 1px solid var(--line); background: #ffffff; color: var(--text);
+  backface-visibility: hidden; box-shadow: 0 12px 24px rgba(0, 0, 0, 0.06); display: flex; flex-direction: column; gap: 14px;
+}
+.candidate-back { transform: rotateY(180deg); background: #ffffff; }
+.candidate-top { display: flex; justify-content: space-between; align-items: center; gap: 8px; }
+.chip {
+  font-size: .72rem; text-transform: uppercase; letter-spacing: .12em; color: #0b6f66; padding: 7px 10px; border-radius: 999px;
+  background: rgba(23, 184, 166, 0.08); border: 1px solid rgba(23, 184, 166, 0.18);
+}
+.chip.alt { color: var(--gold); background: rgba(255, 122, 26, 0.08); border-color: rgba(255, 122, 26, 0.18); }
+.score { font-weight: 700; color: var(--gold); }
+.candidate-face h4 { margin: 0; font-size: 1.08rem; line-height: 1.35; color: var(--text); }
+.candidate-face p { margin: 0; color: #1e1e1e; line-height: 1.65; font-size: .96rem; overflow-wrap: anywhere; }
+.meta-row { margin-top: auto; display: flex; justify-content: space-between; color: var(--muted); font-size: .88rem; gap: 14px; }
+.mini { margin-top: 8px; align-self: flex-start; color: var(--text); padding: 10px 12px; border-radius: 14px; border: 1px solid var(--line); background: #ffffff; }
+.papers-grid { display: grid; grid-template-columns: repeat(2, minmax(0, 1fr)); gap: 14px; }
+.paper-card { border: 1px solid var(--line); border-radius: 18px; padding: 16px; background: #ffffff; }
+.paper-topline { display: flex; gap: 8px; flex-wrap: wrap; margin-bottom: 10px; }
+.paper-badge { font-size: .72rem; padding: 6px 10px; border-radius: 999px; background: rgba(98, 141, 255, 0.08); color: #3456b5; border: 1px solid rgba(98, 141, 255, 0.18); }
+.paper-badge.alt { background: rgba(0, 0, 0, 0.04); color: #444; border-color: rgba(0, 0, 0, 0.08); }
+.paper-card h4 { margin: 0 0 10px; line-height: 1.35; font-size: 1rem; }
+.paper-card p { margin: 0 0 12px; line-height: 1.6; color: #222; }
+.paper-links { display: flex; gap: 12px; flex-wrap: wrap; }
+.paper-links a, .journal-card, .upload-note a { color: #0b63ce; text-decoration: none; }
+.journal-grid { display: grid; grid-template-columns: repeat(2, minmax(0, 1fr)); gap: 14px; }
+.journal-card {
+  border: 1px solid var(--line); border-radius: 18px; padding: 16px; display: flex; justify-content: space-between; gap: 14px; align-items: center; background: #ffffff;
+}
+.journal-card h4 { margin: 0 0 6px; color: var(--text); }
+.journal-card p { margin: 0; color: var(--muted); line-height: 1.5; }
+.upload-note {
+  border: 1px dashed rgba(0,0,0,.16); border-radius: 18px; padding: 16px; background: rgba(0,0,0,.015); color: #1f1f1f; line-height: 1.6;
+}
+.prosebox { padding: 18px; white-space: pre-wrap; font-family: ui-monospace, SFMono-Regular, Menlo, monospace; line-height: 1.55; color: #1b1b1b; }
+.gr-button-primary { background: linear-gradient(135deg, rgba(255, 122, 26, 0.92), rgba(240, 108, 22, 0.92)) !important; color: #ffffff !important; border: none !important; }
+.gr-button-secondary { background: #ffffff !important; color: var(--text) !important; border: 1px solid var(--line) !important; }
+footer { display: none !important; }
+@keyframes pulseEdge { to { stroke-dashoffset: -40; } }
+@media (max-width: 1180px) {
+  .model-switcher, .candidate-grid, .papers-grid, .journal-grid { grid-template-columns: 1fr; }
+  .brain-svg { height: 460px; }
+}
+"""
+
+
+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=\"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 instrument · connectome-native reasoning</p>
+              </div>
+            </div>
+            <div class=\"status\"><span class=\"status-dot\"></span><span>Live orchestration</span></div>
+          </div>
+        </div>
+        """
+    )
+
+    with gr.Tabs():
+        with gr.Tab("Discovery Engine"):
+            model_html = gr.HTML(build_models_html("DVNC Sovereign"))
+            with gr.Row():
+                with gr.Column(scale=2):
+                    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 3D connectome.</p>
+                            </div>
+                          </div>
+                        </div>
+                        """
+                    )
+                    timeline = gr.HTML('<div class="panel" style="padding:18px"><div class="timeline"></div></div>')
+
+                with gr.Column(scale=3):
+                    connectome = gr.HTML(build_connectome_html(DEFAULT_PATH))
+                    cards = gr.HTML('<div class="panel" style="padding:20px"><div class="candidate-grid"></div></div>')
+
+            output = gr.Markdown("# Discovery Output\n\nAwaiting query.")
+
+        with gr.Tab("Self-Learning Graph"):
+            with gr.Row():
+                with gr.Column(scale=2):
+                    paper_query = gr.Textbox(
+                        label="Research topic",
+                        elem_classes=["querybox"],
+                        placeholder="e.g. self-assembling conductive biomaterials for cardiac repair",
+                        lines=3,
+                    )
+                    pdf_upload = gr.File(label="Upload PDF papers", file_types=[".pdf"], file_count="single")
+                    with gr.Row():
+                        learn_btn = gr.Button("Grow knowledge graph", variant="primary")
+                        load_topic_btn = gr.Button("Load example topic", variant="secondary")
+                    upload_status = gr.Markdown("No PDF uploaded yet.")
+                    journal_panel = gr.HTML(build_journal_html("biomaterials cardiac repair"))
+                with gr.Column(scale=3):
+                    learning_graph = gr.HTML(build_learning_graph_html([], []))
+                    papers_panel = gr.HTML('<div class="panel papers-panel" style="padding:18px"><p>Search arXiv, pull linked PDFs, and grow the graph from retrieved papers.</p></div>')
+
+    def load_example():
+        return "How could a self-assembling conductive biomaterial improve cardiac tissue regeneration by converting mechanical strain into repair signalling?"
+
+    def load_paper_topic():
+        return "self-assembling conductive biomaterials for cardiac repair"
+
+    example_btn.click(fn=load_example, outputs=query)
+    load_topic_btn.click(fn=load_paper_topic, outputs=paper_query)
+    run_btn.click(
+        fn=run_discovery,
+        inputs=[query, model],
+        outputs=[chat, connectome, timeline, cards, output, model_html],
+    )
+    learn_btn.click(
+        fn=run_paper_search,
+        inputs=[paper_query, pdf_upload],
+        outputs=[learning_graph, papers_panel, journal_panel, upload_status],
+    )
+
+
+if __name__ == "__main__":
+    demo.launch()