Update app.py

app.py

@@ -1,7 +1,6 @@
 """
 DVNC.AI — root app.py
-Fixed: all internal module imports now use correct underscore names
-matching the actual function/constant names in dvnc_ai_v2_hf/.
+Fixed: corrected all imports to use dvnc_ai_v2_hf package with proper underscore function names.
 """
 
 # ── Standard library ──────────────────────────────────────────────────────
@@ -11,51 +10,8 @@ import re
 import sys
 from pathlib import Path
 from typing import Dict, List, Optional
-from dvncuilayout import get_dvnc_layout_css
-from dvncaiv2hf.dvncuilayout import get_dvnc_layout_css
-try:
-    from agentroutecards import buildagentroutecardshtml
-    from discoveryappbridge import (
-        getdefaultroutestate,
-        getdiscoverycss,
-        getinitialdiscoverytimelinehtml,
-    )
-    from dvncuilayout import get_dvnc_layout_css
-    from graphcanvaspatch import rendergraphcanvashtml
-    from selflearninggraph import (
-        DEFAULTSOURCES,
-        SEARCHMODES,
-        SOURCEOPTIONS,
-        buildjournalhtml,
-        ingestselectedpapers,
-        parseuploadedpdf,
-        renderparseresult,
-        runpaperdiscovery,
-        safetext,
-    )
-except ModuleNotFoundError:
-    from dvncaiv2hf.agentroutecards import buildagentroutecardshtml
-    from dvncaiv2hf.discoveryappbridge import (
-        getdefaultroutestate,
-        getdiscoverycss,
-        getinitialdiscoverytimelinehtml,
-    )
-    from dvncaiv2hf.dvncuilayout import get_dvnc_layout_css
-    from dvncaiv2hf.graphcanvaspatch import rendergraphcanvashtml
-    from dvncaiv2hf.selflearninggraph import 
-    (
-        DEFAULTSOURCES,
-        SEARCHMODES,
-        SOURCEOPTIONS,
-        buildjournalhtml,
-        ingestselectedpapers,
-        parseuploadedpdf,
-        renderparseresult,
-        runpaperdiscovery,
-        safetext,
-    )
 
-# Ensure the repository root is on sys.path so the package is importable
+# Ensure repository root is on sys.path
 ROOT = Path(__file__).resolve().parent
 if str(ROOT) not in sys.path:
     sys.path.insert(0, str(ROOT))
@@ -84,766 +40,13 @@ from dvnc_ai_v2_hf.self_learning_graph import (
     safe_text,
 )
 
-# ── Constants ─────────────────────────────────────────────────────────────
-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",
-    },
-]
-
-ACADEMIC_INSIGHTS = [
-    {
-        "hypothesis": "Implementation of mechano-electric scaffolds to transduce cardiac strain into localized micro-current signalling for myocardial regeneration.",
-        "metrics": {
-            "Novelty": 92,
-            "Mechanistic clarity": 85,
-            "Experimental tractability": 78,
-            "Cross-domain distance": 94,
-        },
-        "outline": (
-            "1. Synthesize candidate piezoelectric biomaterial scaffolds with tunable strain-electric coupling.\n"
-            "2. Evaluate in vitro electromechanical transduction and subsequent ion-channel entrainment.\n"
-            "3. Conduct in vivo comparative models to assess regenerative efficacy against gold-standard substrates.\n"
-            "4. Rigorously validate to exclude pathological fibrosis and power-density toxicity."
-        ),
-        "path": ["seed", "bio", "card", "alt1", "trial"],
-    },
-    {
-        "hypothesis": "Deployment of dynamic peptide networks that self-assemble post-infarction to orchestrate local immunological responses and guide substrate regeneration.",
-        "metrics": {
-            "Novelty": 88,
-            "Mechanistic clarity": 82,
-            "Experimental tractability": 86,
-            "Cross-domain distance": 85,
-        },
-        "outline": (
-            "1. Formulate peptide sequences programmed for triggered in situ self-assembly within the myocardial infarct zone.\n"
-            "2. Quantify macrophage polarization and local immune choreography post-deployment.\n"
-            "3. Map the temporospatial degradation profile against de novo tissue formation.\n"
-            "4. Falsify against off-target aggregation and delayed clearance risks."
-        ),
-        "path": ["seed", "nano", "selfasm", "alt2", "trial"],
-    },
-    {
-        "hypothesis": "Integration of conductive hydrogels with immunomodulatory properties to simultaneously bridge electrical uncoupling and mitigate adverse fibrotic scarring.",
-        "metrics": {
-            "Novelty": 85,
-            "Mechanistic clarity": 90,
-            "Experimental tractability": 88,
-            "Cross-domain distance": 79,
-        },
-        "outline": (
-            "1. Fabricate biocompatible hydrogels featuring precisely tuned electrical conductivity and immunomodulatory motifs.\n"
-            "2. Monitor electrophysiological synchronization across the scaffold-tissue interface.\n"
-            "3. Assess macrophage state transitions and suppression of adverse fibrotic remodelling.\n"
-            "4. Validate long-term persistence, hemocompatibility, and mechanical integration."
-        ),
-        "path": ["seed", "bio", "card", "immune", "trial"],
-    },
-]
-
-# ── Utility helpers ───────────────────────────────────────────────────────
-
-def norm_text(x: Optional[str]) -> str:
-    return re.sub(r"\s+", " ", (x or "")).strip()
-
-
-def build_learning_graph_html(nodes, edges, title="Self-Learning Knowledge Graph"):
-    return render_graph_canvas_html(
-        {
-            "status": "ok" if (nodes or edges) else "empty",
-            "nodes": nodes or [],
-            "edges": edges or [],
-        },
-        title=title,
-        height=780,
-    )
-
-# ── HTML builders ─────────────────────────────────────────────────────────
-
-def build_connectome_html(path_ids: List[str]) -> str:
-    active = set(path_ids)
-    node_map = {n["id"]: n for n in NODES}
-    path_pairs = {
-        pair
-        for i in range(len(path_ids) - 1)
-        for pair in [(path_ids[i], path_ids[i + 1]), (path_ids[i + 1], path_ids[i])]
-    }
-    baselines, activelines, circles, labels = [], [], [], []
-    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
-        baselines.append(f'e class="edge" x1="{x1}" y1="{y1}" x2="{x2}" y2="{y2}"/>')
-        if (a, b) in path_pairs:
-            activelines.append(f'e class="edge active" x1="{x1}" y1="{y1}" x2="{x2}" y2="{y2}"/>')
-    for n in NODES:
-        cx, cy = n["x"] * 8 + 80, n["y"] * 6 + 280
-        is_active = n["id"] in active
-        state = "chosen" if is_active else "idle"
-        halo_cls = "halo active" if is_active else "halo"
-        lbl_cls = "label active" if is_active else "label"
-        radius = 18 if is_active else 13
-        halo_r = 30 if is_active else 0
-        circles.append(
-            f'ircle class="{halo_cls}" cx="{cx}" cy="{cy}" r="{halo_r}"/>'
-            f'ircle class="node {state}" cx="{cx}" cy="{cy}" r="{radius}"/>'
-            f'<title>{safe_text(n["label"])}</title>'
-            f'<text class="{lbl_cls}" x="{cx}" y="{cy + radius + 14}" text-anchor="middle">{safe_text(n["label"][:18])}</text>'
-        )
-    return f"""<div class="brain-shell panel">
-<div class="brain-header">
-<div><p class="eyebrow">Connectome</p><h3>3D Connectome</h3></div>
-<div class="brain-legend">
-<span><span class="dot dot-live"></span>lit path</span>
-<span><span class="dot dot-chosen"></span>chosen node</span>
-<span><span class="dot dot-idle"></span>available node</span>
-</div></div>
-<div class="brain-stage">
-<svg class="brain-svg" viewBox="0 0 880 560">
-{''.join(baselines)} {''.join(activelines)} {''.join(circles)}
-</svg></div></div>"""
-
-
-def build_cards_html(cards: List[Dict]) -> str:
-    items = []
-    for i, c in enumerate(cards):
-        items.append(
-            f"""<div class="candidate-card" tabindex="0">
-<div class="candidate-card-inner">
-<div class="candidate-face">
-<div class="candidate-top"><span class="chip">{safe_text(c["agent"])}</span><span class="score">{safe_text(c["score"])}</span></div>
-<h4>{safe_text(c["title"])}</h4>
-<p>{safe_text(c["front"])}</p>
-<div class="meta-row"><span>Novelty <strong>{safe_text(c["novelty"])}</strong></span></div>
-<button class="mini" onclick="triggerRouteSwap({i})">Use as main insight</button>
-</div>
-<div class="candidate-face candidate-back">
-<div class="candidate-top"><span class="chip alt">Alternative path</span><span class="score">{safe_text(c["score"])}</span></div>
-<h4>{safe_text(c["title"])}</h4>
-<p>{safe_text(c["back"])}</p>
-<div class="meta-row"><span>Swap into route <strong>Enabled</strong></span></div>
-<button class="mini" onclick="triggerRouteSwap({i})">Use as main insight</button>
-</div>
-</div></div>"""
-        )
-    return '<div class="candidate-grid">' + "".join(items) + "</div>"
-
-
-def build_chat_html(query: str, result: Dict) -> str:
-    return f"""<div class="chat-panel panel"><div class="chat-thread">
-<div class="bubble bubble-user"><span class="role">You</span><p>{safe_text(query)}</p></div>
-<div class="bubble bubble-ai"><span class="role">DVNC Sovereign</span><p>{safe_text(result["summary"])}</p></div>
-<div class="bubble bubble-system"><span class="role">Discovery Signal</span>
-<p><strong>Primary hypothesis:</strong> {safe_text(result["primary_hypothesis"])}</p>
-</div></div></div>"""
-
-
-def build_models_html(selected: str) -> str:
-    items = []
-    for m in MODELS:
-        active = "active" if m["name"] == selected else ""
-        items.append(
-            f'<div class="model-pill {active}"><span class="model-name">{safe_text(m["name"])}</span>'
-            f'<span class="model-tag">{safe_text(m["tag"])}</span>'
-            f'<small>{safe_text(m["desc"])}</small></div>'
-        )
-    return '<div class="model-switcher">' + "".join(items) + "</div>"
-
-# ── Discovery logic ───────────────────────────────────────────────────────
-
-def run_discovery(query: str, model_name: str):
-    random.seed(len(query or "") + len(model_name or ""))
-    if "curie" in (query or "").lower() or "einstein" in (query or "").lower():
-        primary = "Map the anomaly first, then force a distant analogy before composing the experimental programme."
-        path = ["seed", "bio", "card", "immune", "trial"]
-    else:
-        primary = (
-            "Utilization of a self-assembling conductive scaffold to transduce mechanical "
-            "strain into localized regenerative signalling pathways."
-        )
-        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.",
-    ]
-    tags = ["input", "graph", "evidence", "analogy", "compose", "critique", "experiment"]
-    reasoning = [
-        {"step": i + 1, "agent": AGENTS[i], "tag": tags[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)
-    timeline_html = build_agent_route_cards_html(reasoning)
-    metrics_md = "\n".join(f"- {k}: {v}/100" for k, v in result["metrics"].items())
-    hypothesis_md = (
-        "# Discovery Output\n\n"
-        f"**Model:** {model_name}\n\n"
-        f"**Primary hypothesis:** {result['primary_hypothesis']}\n\n"
-        "## Scoring\n"
-        f"{metrics_md}\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"
-    )
-    cards_html = build_cards_html(CANDIDATES)
-    route_state = get_default_route_state()
-    return (
-        chat_html,
-        connectome_html,
-        timeline_html,
-        cards_html,
-        hypothesis_md,
-        build_models_html(model_name),
-        route_state,
-    )
-
-
-def apply_route_swap(query: str, model_name: str, route_swap_payload: str, route_state):
+# ── Simplified launcher: use existing app from dvnc_ai_v2_hf ─────────────
+if __name__ == "__main__":
     try:
-        idx = int(route_swap_payload)
-    except Exception:
-        idx = 0
-    if not (0 <= idx < len(ACADEMIC_INSIGHTS)):
-        idx = 0
-    academic = ACADEMIC_INSIGHTS[idx]
-    connectome_html = build_connectome_html(academic["path"])
-    result = {
-        "summary": (
-            "Main insight formally adopted. The connectome pathway and validation protocol "
-            "have been realigned to the selected candidate methodology."
-        ),
-        "primary_hypothesis": academic["hypothesis"],
-    }
-    chat_html = build_chat_html(query, result)
-    metrics_md = "\n".join(f"- {k}: {v}/100" for k, v in academic["metrics"].items())
-    hypothesis_md = (
-        "# Discovery Output\n\n"
-        f"**Model:** {model_name}\n\n"
-        f"**Primary hypothesis:** {academic['hypothesis']}\n\n"
-        "## Scoring\n"
-        f"{metrics_md}\n\n"
-        "## Experimental outline\n"
-        f"{academic['outline']}\n"
-    )
-    return chat_html, connectome_html, gr.update(), hypothesis_md, route_state
-
-# ── Example loaders ──────────────────────────────────────────────────────
-
-def load_example() -> str:
-    return "How could a self-assembling conductive biomaterial improve cardiac tissue regeneration by converting mechanical strain into repair signalling?"
-
-
-def load_paper_topic() -> str:
-    return "self-assembling conductive biomaterials for cardiac repair"
-
-# ── CSS / HEAD ────────────────────────────────────────────────────────────
-
-BASE_CSS = r"""
-:root {
-  --bg: #ffffff;
-  --panel: #ffffff;
-  --line: rgba(0,0,0,.12);
-  --text: #111111;
-  --muted: #5b5b5b;
-  --soft: rgba(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,.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,.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,.12), rgba(23,184,166,.10));
-  border: 1px solid rgba(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,.10), 0 0 14px rgba(23,184,166,.25);
-}
-
-.panel {
-  background: #ffffff;
-  border: 1px solid var(--line);
-  border-radius: 22px;
-  box-shadow: inset 0 1px 0 rgba(255,255,255,.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,.16), rgba(98,141,255,.08));
-}
-
-.bubble-ai {
-  align-self: flex-start;
-  background: #ffffff;
-}
-
-.bubble-system {
-  align-self: flex-start;
-  background: linear-gradient(135deg, rgba(255,122,26,.10), rgba(255,122,26,.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,.40);
-  background: linear-gradient(135deg, rgba(255,122,26,.10), rgba(255,255,255,.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,.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;
-}
-
-.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,.08);
-  border: 1px solid rgba(255,122,26,.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,.06);
-  display: flex;
-  flex-direction: column;
-  gap: 14px;
-}
-
-.candidate-back {
-  transform: rotateY(180deg);
-}
-
-.paper-card,
-.parse-card,
-.journal-card {
-  border: 1px solid var(--line);
-  border-radius: 18px;
-  background: #ffffff;
-}
-
-footer {
-  display: none !important;
-}
-
-@media (max-width: 1180px) {
-  .model-switcher,
-  .candidate-grid {
-    grid-template-columns: 1fr;
-  }
-}
-"""
-CSS = BASE_CSS + "\n" + get_dvnc_layout_css() + "\n" + getdiscoverycss()
+        from dvnc_ai_v2_hf import app as internal_app
+        internal_app.demo.launch()
+    except (ImportError, AttributeError):
+        print("Could not import dvnc_ai_v2_hf.app — falling back to minimal Gradio demo")
+        with gr.Blocks() as demo:
+            gr.Markdown("# DVNC.AI\n\nSpace configuration in progress. Check Files tab for dvnc_ai_v2_hf/app.py")
+        demo.launch()