app_old2.py

"""
DVNC.AI — app.py
Refactored for functional "Use as main insight" logic with academic rigor.
"""
from dvnc_ai_v2_hf.graph_canvas_patch import render_graph_canvas_html
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,
    )

# ── Standard library ────────────────────────────────────────────────────────
import html
import json
import math
import os
import random
import re
import urllib.parse
import xml.etree.ElementTree as ET
from pathlib import Path
from typing import Dict, List, Optional
from urllib.parse import quote
from dvnc_ai_v2_hf.graph_canvas_patch import render_graph_canvas_html

# ── Third-party ──────────────────────────────────────────────────────────────
import gradio as gr
import requests

try:
    import fitz  # PyMuPDF
except Exception:
    fitz = None

try:
    from bs4 import BeautifulSoup
except Exception:
    BeautifulSoup = None

learning_graph = gr.HTML(build_learning_graph_html([], []))

# ── Internal modules ─────────────────────────────────────────────────────────
from dvnc_ai_v2_hf.agent_route_cards import build_agent_route_cards_html
from dvnc_ai_v2_hf.discovery_app_bridge import (
    get_default_route_state,
    get_discovery_css,
    get_initial_discovery_timeline_html,
)
from dvnc_ai_v2_hf.dvnc_ui_layout import get_dvnc_layout_css
from dvnc_ai_v2_hf.self_learning_graph import (
    DEFAULT_SOURCES,
    SEARCH_MODES,
    SOURCE_OPTIONS,
    build_journal_html,
    ingest_selected_papers,
    parse_uploaded_pdf,
    render_parse_result,
    run_paper_discovery,
    safe_text,
)

from dvnc_ai_v2_hf.graph_canvas_patch import render_graph_canvas_html

# ── 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.\n2. Evaluate *in vitro* electromechanical transduction and subsequent ion-channel entrainment.\n3. Conduct *in vivo* comparative models to assess regenerative efficacy against gold-standard substrates.\n4. 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.\n2. Quantify macrophage polarization and local immune choreography post-deployment.\n3. Map the temporospatial degradation profile against *de novo* tissue formation.\n4. 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.\n2. Monitor electrophysiological synchronization across the scaffold-tissue interface.\n3. Assess macrophage state transitions and suppression of adverse fibrotic remodelling.\n4. Validate long-term persistence, hemocompatibility, and mechanical integration.",
        "path": ["seed", "bio", "card", "immune", "trial"]
    }
]

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."},
]

SEMANTIC_SCHOLAR_API_KEY = os.getenv("SEMANTIC_SCHOLAR_API_KEY", "")
GROBID_URL               = os.getenv("GROBID_URL", "").strip()
REQUEST_TIMEOUT          = 25


# ── Utility helpers ──────────────────────────────────────────────────────────
def safe_text(x, default: str = "") -> str:
    return html.escape(str(x if x is not None else default))


def norm_text(x: Optional[str]) -> str:
    return re.sub(r"\s+", " ", (x or "")).strip()


def detect_query_type(query: str) -> str:
    q = (query or "").strip()
    if re.match(r"^10\.\d{4,9}/[-._;()/:A-Z0-9]+$", q, flags=re.I):
        return "doi"
    if q.startswith("http://") or q.startswith("https://"):
        return "link"
    return "topic"


def ensure_list(x):
    return x if isinstance(x, list) else []


# ── 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])]
    }

    base_lines, active_lines, 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
        base_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}" />')

    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'<g class="node-wrap">'
            f'<circle class="{halo_cls}" cx="{cx:.1f}" cy="{cy:.1f}" r="{halo_r}" />'
            f'<circle class="node {n["group"]} {state}" cx="{cx:.1f}" cy="{cy:.1f}" r="{radius}" />'
            f'</g>'
        )
        labels.append(f'<text class="{lbl_cls}" x="{cx + 18:.1f}" y="{cy - 16:.1f}">{safe_text(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(base_lines)}
          {"".join(active_lines)}
          {"".join(circles)}
          {"".join(labels)}
        </svg>
      </div>
    </div>
    """


def build_cards_html(cards: List[Dict]) -> str:
    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">{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</span><strong>{safe_text(c["novelty"])}</strong></div>
              <button class="mini" type="button" 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</span><strong>Enabled</strong></div>
              <button class="mini" type="button" onclick="triggerRouteSwap('{i}')">Use as main insight</button>
            </div>
          </div>
        </article>""")
    return '<div class="panel" style="padding:20px;"><div class="candidate-grid">' + "".join(items) + "</div></div>"


def build_agent_timeline(reasoning: List[Dict]) -> str:
    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">{safe_text(r["step"])}</div>
            <div class="agent-head">
              <h4>{safe_text(r["agent"])}</h4>
              <span>{safe_text(r["tag"])}</span>
            </div>
          </summary>
          <div class="agent-copy">
            <p>{safe_text(r["summary"])}</p>
          </div>
        </details>""")
    return '<div class="panel" style="padding:18px;"><div class="timeline">' + "".join(rows) + "</div></div>"


def build_chat_html(query: str, result: Dict) -> str:
    return f"""
    <div class="panel chat-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>
              <span class="model-tag">{safe_text(m["tag"])}</span>
              <small>{safe_text(m["desc"])}</small>
            </div>""")
    return '<div class="panel" style="padding:18px;"><div class="model-switcher">' + "".join(items) + "</div></div>"


# ── Discovery logic ───────────────────────────────────────────────────────────
def run_discovery(query: str, model_name: str):
    """
    Runs the 7-agent discovery pipeline.
    """
    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 = "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):
    """
    Called when a user clicks 'Use as main insight' on a candidate card.
    Sanitizes the output, adopts academic rigor, updates the connectome and discovery output.
    """
    try:
        idx = int(route_swap_payload)
    except ValueError:
        idx = 0

    if not (0 <= idx < len(ACADEMIC_INSIGHTS)):
        idx = 0

    academic = ACADEMIC_INSIGHTS[idx]
    
    # Update Connectome
    connectome_html = build_connectome_html(academic["path"])
    
    # Update Chat Feedback
    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)

    # Format Oxford-tier markdown output
    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; }
.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; }
.learn-edge { stroke:rgba(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,.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; }
.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,.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; }
.ref-list { margin:0; padding-left:18px; }
.ref-list li { margin-bottom:8px; line-height:1.5; }
.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; }
.selection-panel { padding:18px; }
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  = BASE_CSS + "\n" + get_dvnc_layout_css()
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">
<script>
function triggerRouteSwap(idx) {
    const container = document.getElementById('route_swap_payload');
    if(!container) return;
    const input = container.querySelector('textarea') || container.querySelector('input');
    if(input) {
        input.value = idx.toString();
        input.dispatchEvent(new Event('input', { bubbles: true }));
        setTimeout(() => {
            const btn = document.getElementById('route_swap_apply');
            if(btn) btn.click();
        }, 150);
    }
}
</script>
"""

# ── Gradio layout ─────────────────────────────────────────────────────────────
with gr.Blocks(fill_height=True) as demo:

    # ── Shared state ──────────────────────────────────────────────────────────
    papers_state        = gr.State([])
    parsed_pdf_state    = gr.State({})
    ingest_payload_state = gr.State({})
    route_state         = gr.State(get_default_route_state())

    # ── Header ────────────────────────────────────────────────────────────────
    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():

        # ── Tab 1 · Discovery Engine ──────────────────────────────────────────
        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>
                    """)

                with gr.Column(scale=3):
                    connectome = gr.HTML(build_connectome_html(DEFAULT_PATH))
                    cards      = gr.HTML("")

            output   = gr.Markdown("# Discovery Output\n\nAwaiting query.")
            timeline = gr.HTML(get_initial_discovery_timeline_html())

            route_swap_payload = gr.Textbox(value="", visible=False, elem_id="route_swap_payload")
            route_swap_apply   = gr.Button("Apply route swap", visible=False, elem_id="route_swap_apply")

        # ── Tab 2 · Self-Learning Graph ───────────────────────────────────────
        with gr.Tab("Self-Learning Graph"):
            with gr.Row():
                with gr.Column(scale=2):
                    paper_query = gr.Textbox(
                        label="Research topic / title / DOI / link",
                        elem_classes=["querybox"],
                        placeholder="e.g. self-assembling conductive biomaterials for cardiac repair",
                        lines=3,
                    )
                    search_mode = gr.Dropdown(
                        choices=SEARCH_MODES,
                        value="topic",
                        label="Search mode",
                    )
                    source_selector = gr.CheckboxGroup(
                        choices=SOURCE_OPTIONS,
                        value=DEFAULT_SOURCES,
                        label="Sources",
                    )
                    pdf_upload = gr.File(label="Upload PDF papers", file_types=[".pdf"], file_count="single")

                    with gr.Row():
                        learn_btn      = gr.Button("Discover papers",      variant="primary")
                        load_topic_btn = gr.Button("Load example topic",   variant="secondary")

                    upload_status    = gr.Markdown("No PDF uploaded yet.")
                    discovery_status = gr.Markdown("### No discovery results yet.")
                    journal_panel    = gr.HTML(build_journal_html("biomaterials cardiac repair"))

                    gr.HTML('<div class="panel selection-panel"><h3 style="margin:0 0 12px;">Select papers to ingest</h3></div>')
                    selection_box = gr.CheckboxGroup(choices=[], value=[], label="Candidate papers")

                    parser_order = gr.CheckboxGroup(
                        choices=["grobid", "docling", "pymupdf"],
                        value=["grobid", "docling", "pymupdf"],
                        label="Parser routing order",
                    )

                    with gr.Row():
                        parse_btn  = gr.Button("Parse uploaded PDF",        variant="secondary")
                        ingest_btn = gr.Button("Ingest selected into graph", variant="primary")

                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 by topic, title, DOI, or link, then select papers before graph ingestion.</p></div>')
                    parse_summary  = gr.Markdown("### PDF parse status\n\nAwaiting upload.")
                    parse_panel    = gr.HTML('<div class="panel" style="padding:18px"><p>No parsed document yet.</p></div>')
                    ingest_summary = gr.Markdown("### Graph ingest status\n\nAwaiting paper selection.")
                    ingest_payload = gr.JSON(label="Graph ingest payload", value={"status": "empty", "nodes": [], "edges": []})
                    graph_html = render_graph_canvas_html({"status": "ok", "nodes": NODES, "edges": EDGES},title="Some title",height=780,)
    # ── Event wiring ──────────────────────────────────────────────────────────
    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, route_state],
    )

    route_swap_apply.click(
        fn=apply_route_swap,
        inputs=[query, model, route_swap_payload, route_state],
        outputs=[chat, connectome, timeline, output, route_state],
    )

    load_topic_btn.click(fn=load_paper_topic, outputs=paper_query)

    learn_btn.click(
        fn=run_paper_discovery,
        inputs=[paper_query, search_mode, source_selector, pdf_upload],
        outputs=[learning_graph, papers_panel, journal_panel, upload_status, selection_box, papers_state, discovery_status],
    )

    parse_btn.click(
        fn=parse_uploaded_pdf,
        inputs=[pdf_upload, parser_order],
        outputs=[parse_summary, parsed_pdf_state],
    ).then(
        fn=render_parse_result,
        inputs=[parsed_pdf_state],
        outputs=[parse_panel],
    )

    ingest_btn.click(
        fn=ingest_selected_papers,
        inputs=[paper_query, selection_box, papers_state, pdf_upload, parsed_pdf_state],
        outputs=[learning_graph, ingest_summary, ingest_payload],
    )

if __name__ == "__main__":
    demo.launch(css=CSS, head=HEAD, theme=gr.themes.Base())