"""
DVNC.AI — root app.py
Refactored to fix Gradio runtime startup and preserve current
dvncaiv2hf self-learning graph integration.
"""
# ── Standard library ────────────────────────────────────────────────────────
import html
import random
import re
from typing import Dict, List, Optional
# ── Third-party ─────────────────────────────────────────────────────────────
import gradio as gr
# ── Internal modules ────────────────────────────────────────────────────────
from dvncaiv2hf.agentroutecards import buildagentroutecardshtml
from dvncaiv2hf.discoveryappbridge import (
getdefaultroutestate,
getdiscoverycss,
getinitialdiscoverytimelinehtml,
)
from dvncaiv2hf.dvncuilayout import getdvnclayoutcss
from dvncaiv2hf.graphcanvaspatch import rendergraphcanvashtml
from dvncaiv2hf.selflearninggraph import (
DEFAULTSOURCES,
SEARCHMODES,
SOURCEOPTIONS,
buildjournalhtml,
ingestselectedpapers,
parseuploadedpdf,
renderparseresult,
runpaperdiscovery,
safetext,
)
# ── 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"),
]
DEFAULTPATH = ["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",
},
]
ACADEMICINSIGHTS = [
{
"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 normtext(x: Optional[str]) -> str:
return re.sub(r"\s+", " ", (x or "")).strip()
def buildlearninggraphhtml(nodes, edges, title="Self-Learning Knowledge Graph"):
return rendergraphcanvashtml(
{
"status": "ok" if nodes or edges else "empty",
"nodes": nodes or [],
"edges": edges or [],
},
title=title,
height=780,
)
# ── HTML builders ───────────────────────────────────────────────────────────
def buildconnectomehtml(pathids: List[str]) -> str:
active = set(pathids)
nodemap = {n["id"]: n for n in NODES}
pathpairs = {
pair
for i in range(len(pathids) - 1)
for pair in [(pathids[i], pathids[i + 1]), (pathids[i + 1], pathids[i])]
}
baselines, activelines, circles, labels = [], [], [], []
for a, b in EDGES:
na, nb = nodemap[a], nodemap[b]
x1, y1 = na["x"] * 8 + 80, na["y"] * 6 + 280
x2, y2 = nb["x"] * 8 + 80, nb["y"] * 6 + 280
baselines.append(
f''
)
if (a, b) in pathpairs:
activelines.append(
f''
)
for n in NODES:
cx, cy = n["x"] * 8 + 80, n["y"] * 6 + 280
isactive = n["id"] in active
state = "chosen" if isactive else "idle"
halocls = "halo active" if isactive else "halo"
lblcls = "label active" if isactive else "label"
radius = 18 if isactive else 13
halor = 30 if isactive else 0
circles.append(
f''
f''
f''
f""
)
labels.append(
f'{safe_text(n["label"])}'
)
return f"""
"""
def buildcardshtml(cards: List[Dict]) -> str:
items = []
for i, c in enumerate(cards):
items.append(
f"""
{safe_text(c["agent"])}
{safe_text(c["score"])}
{safe_text(c["title"])}
{safe_text(c["front"])}
Novelty{safe_text(c["novelty"])}
Alternative path
{safe_text(c["score"])}
{safe_text(c["title"])}
{safe_text(c["back"])}
Swap into routeEnabled
"""
)
return '"
def buildchathtml(query: str, result: Dict) -> str:
return f"""
DVNC Sovereign
{safe_text(result["summary"])}
Discovery Signal
Primary hypothesis: {safe_text(result["primary_hypothesis"])}
"""
def buildmodelshtml(selected: str) -> str:
items = []
for m in MODELS:
active = "active" if m["name"] == selected else ""
items.append(
f"""
{safe_text(m["name"])}
{safe_text(m["tag"])}
{safe_text(m["desc"])}
"""
)
return '"
# ── Discovery logic ─────────────────────────────────────────────────────────
def rundiscovery(query: str, modelname: str):
random.seed(len(query or "") + len(modelname 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 = DEFAULTPATH
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,
},
}
chathtml = buildchathtml(query, result)
connectomehtml = buildconnectomehtml(path)
timelinehtml = buildagentroutecardshtml(reasoning)
metricsmd = "\n".join(f"- {k}: {v}/100" for k, v in result["metrics"].items())
hypothesismd = (
"# Discovery Output\n\n"
f"**Model:** {modelname}\n\n"
f"**Primary hypothesis:** {result['primary_hypothesis']}\n\n"
"## Scoring\n"
f"{metricsmd}\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"
)
cardshtml = buildcardshtml(CANDIDATES)
routestate = getdefaultroutestate()
return (
chathtml,
connectomehtml,
timelinehtml,
cardshtml,
hypothesismd,
buildmodelshtml(modelname),
routestate,
)
def applyrouteswap(query: str, modelname: str, routeswappayload: str, routestate):
try:
idx = int(routeswappayload)
except Exception:
idx = 0
if not (0 <= idx < len(ACADEMICINSIGHTS)):
idx = 0
academic = ACADEMICINSIGHTS[idx]
connectomehtml = buildconnectomehtml(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"],
}
chathtml = buildchathtml(query, result)
metricsmd = "\n".join(f"- {k}: {v}/100" for k, v in academic["metrics"].items())
hypothesismd = (
"# Discovery Output\n\n"
f"**Model:** {modelname}\n\n"
f"**Primary hypothesis:** {academic['hypothesis']}\n\n"
"## Scoring\n"
f"{metricsmd}\n\n"
"## Experimental outline\n"
f"{academic['outline']}\n"
)
return chathtml, connectomehtml, gr.update(), hypothesismd, routestate
# ── Example loaders ────────────────────────────────────────────────────────
def loadexample() -> str:
return "How could a self-assembling conductive biomaterial improve cardiac tissue regeneration by converting mechanical strain into repair signalling?"
def loadpapertopic() -> str:
return "self-assembling conductive biomaterials for cardiac repair"
# ── CSS / HEAD ──────────────────────────────────────────────────────────────
BASECSS = 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; }
.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,.05); border-radius:20px; }
.brain-svg { width:100%; height:520px; display:block; }
.edge { stroke:rgba(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,.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,.18); stroke-width:12; }
.label { fill:#2c2c2c; font-size:13px; font-weight:500; letter-spacing:.01em; }
.label.active { fill:#111111; font-weight:700; }
.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); }
.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,.08); border:1px solid rgba(23,184,166,.18); }
.chip.alt { color:var(--gold); background:rgba(255,122,26,.08); border-color:rgba(255,122,26,.18); }
.score { font-weight:700; color:var(--gold); }
.candidate-face h4 { margin:0; font-size:1.08rem; line-height:1.35; }
.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 { cursor:pointer; margin-top:8px; align-self:flex-start; color:var(--text); padding:10px 12px; border-radius:14px; border:1px solid var(--line); background:#ffffff; transition:all 0.2s; }
.mini:hover { background:#f5f5f5; border-color:var(--chosen); }
.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,.08); color:#3456b5; border:1px solid rgba(98,141,255,.18); }
.paper-badge.alt { background:rgba(0,0,0,.04); color:#444; border-color:rgba(0,0,0,.08); }
.doi-badge { background:rgba(255,122,26,.08); color:#8a4105; border-color:rgba(255,122,26,.18); }
.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-meta-stack { display:flex; flex-direction:column; gap:6px; color:#444; margin-bottom:12px; font-size:.9rem; }
.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; }
.journal-card p { margin:0; color:var(--muted); line-height:1.5; }
.selection-panel { padding:18px; }
.parse-grid { display:grid; grid-template-columns:1.2fr 1fr; gap:14px; }
.parse-card { border:1px solid var(--line); border-radius:18px; padding:16px; background:#ffffff; }
.ref-list { margin:0; padding-left:18px; }
.ref-list li { margin-bottom:8px; line-height:1.5; }
.gr-button-primary { background:linear-gradient(135deg,rgba(255,122,26,.92),rgba(240,108,22,.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,.parse-grid { grid-template-columns:1fr; }
.brain-svg { height:460px; }
}
"""
CSS = BASECSS + "\n" + getdvnclayoutcss() + "\n" + getdiscoverycss()
HEAD = """
"""
# ── Gradio layout ───────────────────────────────────────────────────────────
with gr.Blocks(css=CSS, head=HEAD, theme=gr.themes.Base(), fill_height=True) as demo:
papersstate = gr.State([])
parsedpdfstate = gr.State({})
ingestpayloadstate = gr.State({})
routestate = gr.State(getdefaultroutestate())
gr.HTML(
"""
DVNC.AI
Sovereign discovery instrument · connectome-native reasoning
Live orchestration
"""
)
with gr.Tabs():
with gr.Tab("Discovery Engine"):
modelhtml = gr.HTML(buildmodelshtml("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():
runbtn = gr.Button("Run discovery", variant="primary")
examplebtn = gr.Button("Load example", variant="secondary")
chat = gr.HTML(
"""
DVNC
Enter a query to activate the 7-agent discovery stack and illuminate the chosen path through the 3D connectome.
"""
)
with gr.Column(scale=3):
connectome = gr.HTML(buildconnectomehtml(DEFAULTPATH))
cards = gr.HTML("")
output = gr.Markdown("# Discovery Output\n\nAwaiting query.")
timeline = gr.HTML(getinitialdiscoverytimelinehtml())
routeswappayload = gr.Textbox(value="", visible=False, elem_id="route_swap_payload")
routeswapapply = gr.Button("Apply route swap", visible=False, elem_id="route_swap_apply")
with gr.Tab("Self-Learning Graph"):
with gr.Row():
with gr.Column(scale=2):
paperquery = gr.Textbox(
label="Research topic / title / DOI / link",
elem_classes=["querybox"],
placeholder="e.g. self-assembling conductive biomaterials for cardiac repair",
lines=3,
)
searchmode = gr.Dropdown(
choices=SEARCHMODES,
value="topic",
label="Search mode",
)
sourceselector = gr.CheckboxGroup(
choices=SOURCEOPTIONS,
value=DEFAULTSOURCES,
label="Sources",
)
pdfupload = gr.File(
label="Upload PDF papers",
file_types=[".pdf"],
file_count="single",
)
with gr.Row():
learnbtn = gr.Button("Discover papers", variant="primary")
loadtopicbtn = gr.Button("Load example topic", variant="secondary")
uploadstatus = gr.Markdown("No PDF uploaded yet.")
discoverystatus = gr.Markdown("### No discovery results yet.")
journalpanel = gr.HTML(buildjournalhtml("biomaterials cardiac repair"))
gr.HTML(
'Select papers to ingest
'
)
selectionbox = gr.CheckboxGroup(
choices=[],
value=[],
label="Candidate papers",
)
parserorder = gr.CheckboxGroup(
choices=["grobid", "docling", "pymupdf"],
value=["grobid", "docling", "pymupdf"],
label="Parser routing order",
)
with gr.Row():
parsebtn = gr.Button("Parse uploaded PDF", variant="secondary")
ingestbtn = gr.Button("Ingest selected into graph", variant="primary")
with gr.Column(scale=3):
learninggraph = gr.HTML(buildlearninggraphhtml([], []))
paperspanel = gr.HTML(
'Search by topic, title, DOI, or link, then select papers before graph ingestion.