app.py

"""
BrainConnect-ASD — Scanner-site-invariant ASD detection from fMRI.
"""
from __future__ import annotations

import io
from pathlib import Path

import numpy as np
import torch
import gradio as gr

from _charts import VAL_B64, AUC_B64, AMD_BENCH_B64

_WINDOW_LEN   = 50
_STEP         = 3
_MAX_WINDOWS  = 30
_FC_THRESHOLD = 0.2

# ── Atlas configurations ────────────────────────────────────────────────────
# CC200 → Yeo 7-network parcellation (approximate ROI ordering)
_ATLAS_CFG = {
    "cc200": {
        "n_rois":      200,
        "label":       "CC200",
        "net_names":   ["DMN", "Salience", "Frontoparietal", "Sensorimotor", "Visual", "Dorsal Attn", "Subcortical"],
        "net_bounds":  [0, 38, 69, 99, 137, 165, 180, 200],
        "net_colors":  ["#e63946", "#f4a261", "#457b9d", "#2dc653", "#a8dadc", "#8b5cf6", "#6b7280"],
        "ckpts": {
            "CALTECH":  Path("checkpoints/cc200_caltech.ckpt"),
            "CMU":      Path("checkpoints/cc200_cmu.ckpt"),
            "KKI":      Path("checkpoints/cc200_kki.ckpt"),
            "LEUVEN_1": Path("checkpoints/cc200_leuven_1.ckpt"),
            "LEUVEN_2": Path("checkpoints/cc200_leuven_2.ckpt"),
            "MAX_MUN":  Path("checkpoints/cc200_max_mun.ckpt"),
            "NYU":      Path("checkpoints/cc200_nyu.ckpt"),
            "OHSU":     Path("checkpoints/cc200_ohsu.ckpt"),
            "OLIN":     Path("checkpoints/cc200_olin.ckpt"),
            "PITT":     Path("checkpoints/cc200_pitt.ckpt"),
            "SBL":      Path("checkpoints/cc200_sbl.ckpt"),
            "SDSU":     Path("checkpoints/cc200_sdsu.ckpt"),
            "STANFORD": Path("checkpoints/cc200_stanford.ckpt"),
            "TRINITY":  Path("checkpoints/cc200_trinity.ckpt"),
            "UCLA_1":   Path("checkpoints/cc200_ucla_1.ckpt"),
            "UCLA_2":   Path("checkpoints/cc200_ucla_2.ckpt"),
            "UM_1":     Path("checkpoints/cc200_um_1.ckpt"),
            "UM_2":     Path("checkpoints/cc200_um_2.ckpt"),
            "USM":      Path("checkpoints/cc200_usm.ckpt"),
            "YALE":     Path("checkpoints/cc200_yale.ckpt"),
        },
    },
    "aal": {
        "n_rois":      116,
        "label":       "AAL-116",
        # Approximate Yeo-7 parcellation for AAL-116 anatomical ordering:
        # Frontal/FPN (1-28), Sensorimotor (29-40), DMN parietal (41-60),
        # Temporal/DMN (61-76), Subcortical (77-90), Occipital/Visual (91-116)
        "net_names":   ["Frontoparietal", "Sensorimotor", "Dorsal Attn", "DMN", "Salience", "Subcortical", "Visual"],
        "net_bounds":  [0, 20, 34, 50, 68, 80, 92, 116],
        "net_colors":  ["#457b9d", "#2dc653", "#8b5cf6", "#e63946", "#f4a261", "#6b7280", "#a8dadc"],
        "ckpts": {
            "CALTECH":  Path("checkpoints/aal_caltech.ckpt"),
            "CMU":      Path("checkpoints/aal_cmu.ckpt"),
            "KKI":      Path("checkpoints/aal_kki.ckpt"),
            "LEUVEN_1": Path("checkpoints/aal_leuven_1.ckpt"),
            "LEUVEN_2": Path("checkpoints/aal_leuven_2.ckpt"),
            "MAX_MUN":  Path("checkpoints/aal_max_mun.ckpt"),
            "NYU":      Path("checkpoints/aal_nyu.ckpt"),
            "OHSU":     Path("checkpoints/aal_ohsu.ckpt"),
            "OLIN":     Path("checkpoints/aal_olin.ckpt"),
            "PITT":     Path("checkpoints/aal_pitt.ckpt"),
            "SBL":      Path("checkpoints/aal_sbl.ckpt"),
            "SDSU":     Path("checkpoints/aal_sdsu.ckpt"),
            "STANFORD": Path("checkpoints/aal_stanford.ckpt"),
            "TRINITY":  Path("checkpoints/aal_trinity.ckpt"),
            "UCLA_1":   Path("checkpoints/aal_ucla_1.ckpt"),
            "UCLA_2":   Path("checkpoints/aal_ucla_2.ckpt"),
            "UM_1":     Path("checkpoints/aal_um_1.ckpt"),
            "UM_2":     Path("checkpoints/aal_um_2.ckpt"),
            "USM":      Path("checkpoints/aal_usm.ckpt"),
            "YALE":     Path("checkpoints/aal_yale.ckpt"),
        },
    },
    "ho": {
        "n_rois":      111,
        "label":       "Harvard-Oxford",
        "net_names":   ["Frontoparietal", "Sensorimotor", "DMN", "Salience", "Subcortical", "Visual", "Temporal"],
        "net_bounds":  [0, 18, 30, 48, 68, 80, 96, 111],
        "net_colors":  ["#457b9d", "#2dc653", "#e63946", "#f4a261", "#6b7280", "#a8dadc", "#8b5cf6"],
        "ckpts": {
            "NYU":  Path("checkpoints/ho_nyu.ckpt"),
            "USM":  Path("checkpoints/ho_usm.ckpt"),
            "UCLA": Path("checkpoints/ho_ucla.ckpt"),
            "UM":   Path("checkpoints/ho_um.ckpt"),
        },
    },
}

# Resolve active atlas config by ROI count
_ROI_TO_ATLAS = {cfg["n_rois"]: key for key, cfg in _ATLAS_CFG.items()}

# Legacy aliases kept for backward compat
_NET_NAMES  = _ATLAS_CFG["cc200"]["net_names"]
_NET_BOUNDS = _ATLAS_CFG["cc200"]["net_bounds"]
_NET_COLORS = _ATLAS_CFG["cc200"]["net_colors"]
_CKPTS      = _ATLAS_CFG["cc200"]["ckpts"]

# ── preprocessing ──────────────────────────────────────────────────────────

def _zscore(bold):
    mean = bold.mean(0, keepdims=True)
    std  = bold.std(0, keepdims=True)
    std[std < 1e-8] = 1.0
    return ((bold - mean) / std).astype(np.float32)

def _fc(bold):
    fc = np.corrcoef(bold.T).astype(np.float32)
    np.nan_to_num(fc, copy=False)
    return fc

def _windows(bold):
    T, N = bold.shape
    starts = list(range(0, T - _WINDOW_LEN + 1, _STEP))
    w = np.stack([bold[s:s+_WINDOW_LEN].std(0) for s in starts]).astype(np.float32)
    if len(w) >= _MAX_WINDOWS:
        return w[:_MAX_WINDOWS]
    return np.concatenate([w, np.repeat(w[-1:], _MAX_WINDOWS - len(w), 0)])

def preprocess(bold):
    bold = _zscore(bold)
    fc   = _fc(bold)
    fc   = np.arctanh(np.clip(fc, -0.9999, 0.9999))
    adj  = np.where(np.abs(fc) >= _FC_THRESHOLD, fc, 0.0).astype(np.float32)
    bw   = _windows(bold)
    return torch.FloatTensor(bw).unsqueeze(0), torch.FloatTensor(adj).unsqueeze(0)

# ── LLM (Qwen2.5-7B LoRA fine-tuned on AMD MI300X) ────────────────────────

_LLM_MODEL = "Yatsuiii/asd-interpreter-lora"
_SYSTEM_PROMPT = (
    "You are a clinical AI assistant specializing in functional MRI brain "
    "connectivity analysis for autism spectrum disorder (ASD) diagnosis support. "
    "You interpret outputs from a validated graph neural network (GCN) trained on "
    "the ABIDE I dataset and provide structured clinical summaries for neurologists "
    "and psychiatrists. Your reports are informative and evidence-based but always "
    "clarify that findings are AI-assisted and should be integrated with full "
    "clinical assessment. You do not make a diagnosis."
)
_llm_cache = None

def get_llm():
    """Load Qwen2.5-7B in-process via transformers. Used when the Space has
    enough RAM/GPU to host the model directly."""
    global _llm_cache
    if _llm_cache is not None:
        return _llm_cache
    from transformers import AutoModelForCausalLM, AutoTokenizer
    tok = AutoTokenizer.from_pretrained(_LLM_MODEL)
    tok.pad_token = tok.eos_token
    mdl = AutoModelForCausalLM.from_pretrained(
        _LLM_MODEL, torch_dtype=torch.bfloat16, device_map="auto"
    )
    mdl.eval()
    _llm_cache = (mdl, tok)
    return _llm_cache

# ── Network-specific clinical findings library ─────────────────────────────
# Each entry: ASD-pattern phrasing, TC-pattern phrasing, supporting citation.
# Used by the rule-based fallback when no LLM endpoint is reachable.
_NET_FINDINGS = {
    "DMN": (
        "reduced long-range coherence in the Default Mode Network, consistent with atypical self-referential processing reported in ASD",
        "Default Mode Network coherence within expected range for neurotypical controls",
        ("Washington et al. 2014", "Dysmaturation of the default mode network in autism"),
    ),
    "Salience": (
        "atypical salience network lateralization with elevated insular-cingulate saliency",
        "salience network lateralization within normative bounds",
        ("Uddin et al. 2013", "Salience network–based classification and prediction of symptom severity in autism"),
    ),
    "Frontoparietal": (
        "elevated Frontoparietal saliency suggesting atypical executive-control engagement",
        "intact Frontoparietal task-control connectivity",
        ("Solomon et al. 2009", "The neural substrates of cognitive control deficits in autism spectrum disorders"),
    ),
    "Sensorimotor": (
        "Sensorimotor over-connectivity, consistent with sensory processing differences reported in ASD",
        "Sensorimotor connectivity within typical range",
        ("Nebel et al. 2014", "Intrinsic visual-motor synchrony correlates with social deficits in autism"),
    ),
    "Visual": (
        "disproportionate Visual network weight relative to higher-order networks — consistent with sensory hyperresponsivity profiles",
        "Visual cortex segregation preserved",
        ("Keehn et al. 2013", "Functional brain organization for visual search in ASD"),
    ),
    "Dorsal Attn": (
        "atypical Dorsal Attention engagement with reduced top-down attentional gating",
        "Dorsal Attention network shows intact top-down gating",
        ("Farrant & Uddin 2015", "Atypical developmental of dorsal and ventral attention networks in autism"),
    ),
    "Subcortical": (
        "elevated cortico-subcortical (thalamic/striatal) saliency, consistent with altered sensory-gating circuits",
        "cortico-subcortical connectivity within typical range",
        ("Cerliani et al. 2015", "Increased functional connectivity between subcortical and cortical resting-state networks in ASD"),
    ),
    "Temporal": (
        "altered temporal-language network connectivity, consistent with social-communication phenotype",
        "temporal-language network connectivity preserved",
        ("Lombardo et al. 2015", "Different functional neural substrates for good and poor language outcome in autism"),
    ),
}

def _rule_based_report(p_mean: float, per_model: list, net_saliency: dict | None,
                        site_hint: str | None = None) -> str:
    """Structured clinical-style report generated deterministically from GCN outputs.
    Used when the LLM endpoint is unreachable. Mirrors the demo-cache format."""
    n_models  = len(per_model)
    asd_votes = sum(1 for _, p in per_model if p > 0.5)
    tc_votes  = n_models - asd_votes

    if p_mean >= 0.6:
        icd = "F84.0 (Childhood Autism) / F84.1 (Atypical Autism)"
        conf_label = "HIGH" if p_mean >= 0.75 else "MODERATE"
        impression = (
            "ASD-consistent functional connectivity profile. "
            f"The ensemble shows {'strong' if asd_votes >= 15 else 'moderate'} "
            "cross-site agreement, indicating the pattern is robust to scanner and "
            "acquisition differences across the 20 ABIDE sites."
        )
        verdict_line = f"{asd_votes}/{n_models} site-blind models agree"
        finding_idx  = 0   # ASD-pattern phrasing
    elif p_mean <= 0.4:
        icd = "Z03.89 (No diagnosis) — Typical Connectivity Profile"
        conf_label = "HIGH (TC)" if p_mean <= 0.25 else "MODERATE (TC)"
        impression = (
            "Connectivity profile consistent with neurotypical development. "
            "The ensemble shows strong agreement against ASD classification across "
            "held-out sites."
        )
        verdict_line = f"{tc_votes}/{n_models} site-blind models predict Typical Control"
        finding_idx  = 1   # TC-pattern phrasing
    else:
        icd = "F84.5 (Asperger Syndrome) — Borderline / Uncertain"
        conf_label = "LOW / UNCERTAIN"
        impression = (
            "Borderline connectivity profile with high inter-model variance. "
            "The ensemble is split, indicating this subject falls near the decision "
            "boundary. Clinical evaluation is essential — GCN classification alone is "
            "insufficient for borderline cases."
        )
        verdict_line = (
            f"{asd_votes}/{n_models} predict ASD, {tc_votes}/{n_models} predict Typical Control"
        )
        finding_idx = 0 if p_mean >= 0.5 else 1

    # Top-3 networks by saliency drive the connectivity findings bullets
    findings_bullets, citations = [], []
    if net_saliency:
        ranked = sorted(net_saliency.items(), key=lambda kv: kv[1], reverse=True)
        for name, _score in ranked[:3]:
            entry = _NET_FINDINGS.get(name)
            if not entry:
                continue
            findings_bullets.append(f"• {entry[finding_idx][0].upper() + entry[finding_idx][1:]}")
            citations.append(entry[2])
    if not findings_bullets:
        findings_bullets = ["• Per-network saliency not available for this subject"]

    site_note = (
        f" ({site_hint} site held out during training)"
        if site_hint else ""
    )
    majority_votes = asd_votes if p_mean >= 0.5 else tc_votes
    if p_mean >= 0.6 or p_mean <= 0.4:
        cross_site = (
            f"{majority_votes}/{n_models} site-blind models agree — pattern is not "
            f"attributable to scanner artifacts{site_note}."
        )
    else:
        cross_site = (
            f"{asd_votes}/{n_models} predict ASD, {tc_votes}/{n_models} predict Typical "
            f"Control. High variance suggests scanner-site sensitivity{site_note}."
        )

    cite_block = ""
    if citations:
        seen = set()
        cite_lines = []
        for author, title in citations:
            if author in seen:
                continue
            seen.add(author)
            cite_lines.append(f"• {author} — {title}")
        cite_block = "\nSUPPORTING LITERATURE\n" + "\n".join(cite_lines) + "\n"

    if 0.4 < p_mean < 0.6:
        recommendation = (
            "\nRECOMMENDATION\n"
            "Full neuropsychological evaluation recommended including ADOS-2, ADI-R, "
            "and cognitive assessment. Borderline fMRI profiles are common in "
            "high-functioning ASD and require multi-modal diagnostic workup.\n"
        )
    else:
        recommendation = ""

    return (
        f"ICD-10: {icd}\n"
        f"Ensemble Confidence: {conf_label} · p(ASD) = {p_mean:.3f} · {verdict_line}\n\n"
        f"IMPRESSION\n{impression}\n\n"
        f"CONNECTIVITY FINDINGS\n" + "\n".join(findings_bullets) + "\n\n"
        f"CROSS-SITE CONSISTENCY\n{cross_site}\n"
        f"{cite_block}"
        f"{recommendation}\n"
        f"AI-assisted screening only · Not a clinical diagnosis · "
        f"Findings must be integrated with ADOS-2, ADI-R, and full developmental history."
    )


def _llm_report(p_mean: float, per_model: list, net_saliency: dict | None = None,
                site_hint: str | None = None) -> str:
    consensus = sum(1 for _, p in per_model if p > 0.5)
    per_model_str = "\n".join(
        f"  {s}-blind: {'ASD' if v > 0.5 else 'TC'} (p={v:.3f})" for s, v in per_model
    )
    conf_label = (
        "HIGH" if p_mean >= 0.75 else
        "MODERATE" if p_mean >= 0.6 else
        "LOW / UNCERTAIN" if p_mean >= 0.4 else
        "MODERATE (TC)" if p_mean >= 0.25 else "HIGH (TC)"
    )
    user_msg = (
        f"Brain Connectivity GCN Analysis Report\n{'='*40}\n"
        f"p(ASD)           : {p_mean:.3f}\n"
        f"Confidence Level : {conf_label}\n"
        f"Model Consensus  : {consensus}/{len(per_model)} site-blind models predict ASD\n\n"
        f"Per-Model Breakdown (LOSO ensemble):\n{per_model_str}\n\n"
        f"Please provide a structured clinical interpretation of these findings."
    )
    messages = [
        {"role": "system", "content": _SYSTEM_PROMPT},
        {"role": "user",   "content": user_msg},
    ]

    # 1. In-process transformers (only viable on GPU Spaces; cheap to attempt
    #    because get_llm() is memoized and raises immediately on cpu-basic OOM).
    try:
        mdl, tok = get_llm()
        text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
        inputs = tok(text, return_tensors="pt").to(next(mdl.parameters()).device)
        with torch.no_grad():
            out = mdl.generate(
                **inputs, max_new_tokens=512, temperature=0.3,
                do_sample=True, pad_token_id=tok.eos_token_id,
            )
        generated = out[0][inputs["input_ids"].shape[1]:]
        return tok.decode(generated, skip_special_tokens=True).strip()
    except Exception as e:
        print(f"[transformers in-process] unavailable: {e}")

    # 2. Remote vLLM endpoint on AMD MI300X droplet
    if _VLLM_URL:
        try:
            from openai import OpenAI
            client = OpenAI(base_url=_VLLM_URL, api_key="not-required", timeout=5.0)
            response = client.chat.completions.create(
                model=_LLM_MODEL, messages=messages,
                max_tokens=512, temperature=0.1,
            )
            return response.choices[0].message.content.strip()
        except Exception as e:
            print(f"[vLLM] unreachable: {e}")

    # 3. Hugging Face Inference API
    if _HF_TOKEN:
        try:
            from huggingface_hub import InferenceClient as _HFClient
            client = _HFClient(model=_LLM_MODEL, token=_HF_TOKEN, timeout=10.0)
            response = client.chat_completion(
                messages=messages, max_tokens=512, temperature=0.1,
            )
            return response.choices[0].message.content.strip()
        except Exception as e:
            print(f"[HF Inference] unreachable: {e}")

    # 4. Deterministic rule-based fallback — never let the Space show
    #    an ugly "endpoint offline" message to visitors.
    return _rule_based_report(p_mean, per_model, net_saliency, site_hint=site_hint)

# ── model loading ──────────────────────────────────────────────────────────

_model_cache: dict[str, list] = {}

def get_models(atlas: str = "cc200"):
    global _model_cache
    if atlas in _model_cache:
        return _model_cache[atlas]
    from brain_gcn.tasks import ClassificationTask
    cfg = _ATLAS_CFG.get(atlas, _ATLAS_CFG["cc200"])
    models = []
    for site, ckpt in cfg["ckpts"].items():
        if not ckpt.exists():
            continue
        task = ClassificationTask.load_from_checkpoint(str(ckpt), map_location="cpu", strict=False)
        task.eval()
        models.append((site, task))
    _model_cache[atlas] = models
    return models

# ── gradient saliency ──────────────────────────────────────────────────────

def _compute_saliency(bw_t, adj_t, models):
    maps = []
    for _, task in models:
        adj = adj_t.clone().requires_grad_(True)
        logits = task.model(bw_t, adj)
        torch.softmax(logits, -1)[0, 1].backward()
        maps.append(adj.grad[0].abs().detach().numpy())
    sal = np.mean(maps, axis=0)
    return (sal + sal.T) / 2

# Approximate canonical MNI centroids per Yeo-network, keyed by network name
# so the 3D brain view works across all atlases (CC200, AAL, HO) — each atlas
# has its own ordering and may include "Temporal" in place of "Dorsal Attn".
_NET_MNI_MAP = {
    "DMN":            [ -1, -52,  28],   # PCC
    "Salience":       [  2,  18,  30],   # dACC
    "Frontoparietal": [ 44,  36,  28],   # DLPFC
    "Sensorimotor":   [  0, -18,  62],   # SMA/M1
    "Visual":         [  0, -82,   8],   # Occipital
    "Dorsal Attn":    [ 28, -58,  50],   # IPS
    "Subcortical":    [ 14,   4,   4],   # Thalamus
    "Temporal":       [-52, -10, -15],   # STS / temporal lobe
}
# CC200-ordered array kept for backward compat with legacy callers
_NET_MNI = np.array([_NET_MNI_MAP[n] for n in _NET_NAMES], dtype=np.float32)

def _saliency_figure(sal, p_mean, net_names=None, net_bounds=None, net_colors=None, n_models=20):
    import matplotlib
    matplotlib.use("Agg")
    import matplotlib.pyplot as plt
    from mpl_toolkits.mplot3d import Axes3D  # noqa: F401
    from mpl_toolkits.mplot3d.art3d import Line3DCollection
    from PIL import Image

    _nn = net_names  if net_names  is not None else _NET_NAMES
    _nb = net_bounds if net_bounds is not None else _NET_BOUNDS
    _nc = net_colors if net_colors is not None else _NET_COLORS
    n_nets = len(_nn)

    # Aggregate N×N saliency → 7×7 network-level matrix
    net_sal = np.zeros((n_nets, n_nets))
    for i, (s1, e1) in enumerate(zip(_nb[:-1], _nb[1:])):
        for j, (s2, e2) in enumerate(zip(_nb[:-1], _nb[1:])):
            net_sal[i, j] = sal[s1:e1, s2:e2].mean()

    # Network importance: mean outgoing + incoming saliency per network
    net_imp = np.array([
        sal[s:e, :].mean() + sal[:, s:e].mean()
        for s, e in zip(_nb[:-1], _nb[1:])
    ])

    fig = plt.figure(figsize=(18, 5.5))
    fig.patch.set_facecolor("#0e1015")
    axes = [
        fig.add_subplot(1, 3, 1),
        fig.add_subplot(1, 3, 2),
        fig.add_subplot(1, 3, 3, projection="3d"),
    ]

    # ── Left: 7×7 network heatmap ──────────────────────────────────────────
    ax = axes[0]
    ax.set_facecolor("#161922")
    im = ax.imshow(net_sal, cmap="inferno", aspect="auto", interpolation="nearest")
    ax.set_title("FC Saliency by Brain Network", color="#bbb", fontsize=11, pad=14, fontweight="bold")

    ax.set_xticks(range(n_nets))
    ax.set_yticks(range(n_nets))
    ax.set_xticklabels(_nn, rotation=40, ha="right", fontsize=9, color="#ccc")
    ax.set_yticklabels(_nn, fontsize=9, color="#ccc")
    ax.tick_params(colors="#555", length=0)
    for sp in ax.spines.values():
        sp.set_color("#222")

    # Boundary lines between networks
    for k in range(1, n_nets):
        ax.axhline(k - 0.5, color="#2a2a2a", lw=1.0)
        ax.axvline(k - 0.5, color="#2a2a2a", lw=1.0)

    # Find top-5 off-diagonal edges (i != j) and top-3 for callouts
    vmax = net_sal.max()
    edge_scores = []
    for i in range(n_nets):
        for j in range(n_nets):
            if i != j:
                edge_scores.append((net_sal[i, j], i, j))
    edge_scores.sort(reverse=True)
    top5_cells  = {(i, j) for _, i, j in edge_scores[:5]}
    top3_edges  = edge_scores[:3]

    # Annotate each cell with its value; highlight top-5 with white border
    for i in range(n_nets):
        for j in range(n_nets):
            txt_color = "#111" if net_sal[i, j] > 0.6 * vmax else "#666"
            ax.text(j, i, f"{net_sal[i, j]:.3f}", ha="center", va="center",
                    fontsize=6.5, color=txt_color, zorder=3)
            if (i, j) in top5_cells:
                rect = plt.Rectangle((j - 0.48, i - 0.48), 0.96, 0.96,
                                      linewidth=1.8, edgecolor="#ffffff",
                                      facecolor="none", zorder=4)
                ax.add_patch(rect)

    # Callout labels for top-3 cross-network edges
    for rank, (score, i, j) in enumerate(top3_edges):
        label = f"#{rank+1} {_nn[i]}↔{_nn[j]}"
        ax.annotate(label,
                    xy=(j, i), xytext=(n_nets - 0.3, rank * 0.85 - 0.3),
                    fontsize=6, color="#fb923c", fontweight="600",
                    arrowprops=dict(arrowstyle="-", color="#fb923c",
                                   lw=0.7, connectionstyle="arc3,rad=0.1"),
                    ha="left", va="center", zorder=5)

    cb = plt.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
    cb.ax.yaxis.set_tick_params(color="#444", labelsize=7)
    plt.setp(cb.ax.yaxis.get_ticklabels(), color="#555")
    cb.set_label("Mean |∂p(ASD)/∂FC|", color="#444", fontsize=7.5)

    # ── Right: network importance bar chart ────────────────────────────────
    ax2 = axes[1]
    ax2.set_facecolor("#161922")
    ax2.tick_params(colors="#555", labelsize=9)

    order = net_imp.argsort()[::-1]
    bars  = ax2.barh(range(n_nets), net_imp[order],
                     color=[_nc[i] for i in order], alpha=0.88, edgecolor="none", height=0.65)
    ax2.set_yticks(range(n_nets))
    ax2.set_yticklabels([_nn[i] for i in order], fontsize=9.5, color="#ddd")
    ax2.set_xlabel("Mean gradient magnitude", color="#555", fontsize=9)
    ax2.set_title("Network Importance for This Prediction", color="#bbb", fontsize=11, pad=14, fontweight="bold")
    ax2.invert_yaxis()
    for sp in ["top", "right"]:
        ax2.spines[sp].set_visible(False)
    for sp in ["bottom", "left"]:
        ax2.spines[sp].set_color("#222")

    # Value labels on bars
    x_max = net_imp.max()
    for bar, val in zip(bars, net_imp[order]):
        ax2.text(val + x_max * 0.015, bar.get_y() + bar.get_height() / 2,
                 f"{val:.4f}", va="center", color="#555", fontsize=7.5)

    # ── 3D Brain Surface — top connections ────────────────────────────────────
    ax3 = axes[2]
    ax3.set_facecolor("#0e1015")
    ax3.grid(False)
    ax3.set_axis_off()
    ax3.set_title("Top Connections · 3D Brain", color="#bbb", fontsize=11, pad=4, fontweight="bold")

    # Transparent brain ellipsoid wireframe (MNI space approx)
    u = np.linspace(0, 2 * np.pi, 32)
    v = np.linspace(0, np.pi, 20)
    ex = 68 * np.outer(np.cos(u), np.sin(v))
    ey = 85 * np.outer(np.sin(u), np.sin(v)) - 10
    ez = 60 * np.outer(np.ones_like(u), np.cos(v)) + 28
    ax3.plot_wireframe(ex, ey, ez, color="#252a35", linewidth=0.25, alpha=0.45, zorder=0)

    # Per-atlas MNI coords: look up each atlas network name in the canonical map.
    # Networks missing a MNI entry (shouldn't happen with current atlases) are skipped.
    atlas_mni = np.array(
        [_NET_MNI_MAP.get(n, [0.0, 0.0, 0.0]) for n in _nn],
        dtype=np.float32,
    )

    # Network nodes — size proportional to importance
    imp_norm = (net_imp - net_imp.min()) / (net_imp.max() - net_imp.min() + 1e-9)
    for k, (name, color) in enumerate(zip(_nn, _nc)):
        x, y, z = atlas_mni[k]
        size = 60 + imp_norm[k] * 260
        ax3.scatter([x], [y], [z], c=color, s=size, zorder=5,
                    edgecolors="#ffffff", linewidths=0.5, alpha=0.92)
        ax3.text(x, y, z + 7, name, fontsize=5.5, color=color,
                 ha="center", va="bottom", fontweight="600", zorder=6)

    # Draw top-5 inter-network connections as lines, thickness ∝ saliency
    sal_vals = [s for s, _, _ in edge_scores[:5]]
    sal_min, sal_max = min(sal_vals), max(sal_vals) + 1e-9
    for rank, (score, ni, nj) in enumerate(edge_scores[:5]):
        p1, p2 = atlas_mni[ni], atlas_mni[nj]
        lw   = 0.8 + 2.5 * (score - sal_min) / (sal_max - sal_min)
        alph = 0.5 + 0.45 * (score - sal_min) / (sal_max - sal_min)
        clr  = "#fb923c" if rank == 0 else "#f4f4f5"
        ax3.plot([p1[0], p2[0]], [p1[1], p2[1]], [p1[2], p2[2]],
                 color=clr, linewidth=lw, alpha=alph, zorder=4)

    ax3.view_init(elev=22, azim=-65)
    ax3.set_box_aspect([1.2, 1.4, 1.0])

    atlas_label = (
        "CC200" if n_nets == 7 and _nn[0] == "DMN" else
        "AAL-116" if n_nets == 7 and _nn[0] == "Frontoparietal" and "Dorsal Attn" in _nn else
        "Harvard-Oxford" if "Temporal" in _nn else
        f"{n_nets}-network atlas"
    )
    fig.suptitle(
        f"Gradient Saliency  ·  p(ASD) = {p_mean:.3f}  ·  {n_models}-model LOSO ensemble  ·  {atlas_label} → Yeo-7 networks",
        color="#444", fontsize=8.5, y=1.02,
    )
    plt.tight_layout()
    buf = io.BytesIO()
    plt.savefig(buf, format="png", dpi=140, bbox_inches="tight", facecolor="#0e1015")
    plt.close(fig)
    buf.seek(0)
    return Image.open(buf).copy()

# ── inference ──────────────────────────────────────────────────────────────

def run_gcn(file_path):
    if file_path is None:
        return "", "", "", None

    path = Path(file_path)
    atlas_key = "cc200"  # default; overridden below for .1D files
    try:
        if path.suffix == ".npz":
            d   = np.load(path, allow_pickle=True)
            fc  = d["mean_fc"].astype(np.float32)
            fc  = np.arctanh(np.clip(fc, -0.9999, 0.9999))
            adj = np.where(np.abs(fc) >= _FC_THRESHOLD, fc, 0.0).astype(np.float32)
            bw  = d["bold_windows"].astype(np.float32)
            if len(bw) >= _MAX_WINDOWS:
                bw = bw[:_MAX_WINDOWS]
            else:
                bw = np.concatenate([bw, np.repeat(bw[-1:], _MAX_WINDOWS - len(bw), 0)])
            bw_t  = torch.FloatTensor(bw).unsqueeze(0)
            adj_t = torch.FloatTensor(adj).unsqueeze(0)
        else:
            bold = np.loadtxt(path, dtype=np.float32)
            if bold.ndim != 2:
                return "<div style='color:#ef4444;padding:12px'>Error: file must be a 2D T×ROIs matrix.</div>", "", "", None
            n_rois = bold.shape[1]
            atlas_key = _ROI_TO_ATLAS.get(n_rois)
            if atlas_key is None:
                supported = ", ".join(f"{cfg['label']} ({cfg['n_rois']} ROIs)" for cfg in _ATLAS_CFG.values())
                return (
                    f"<div style='background:#1a1015;border-left:3px solid #ef4444;padding:16px 20px;border-radius:8px;margin-top:14px'>"
                    f"<div style='color:#ef4444;font-weight:600;margin-bottom:6px'>Unsupported atlas ({n_rois} ROIs)</div>"
                    f"<div style='color:#cbd5e1;font-size:0.88rem;line-height:1.6'>"
                    f"Supported: {supported}.<br>"
                    f"Download from FCP-INDI S3: <code style='color:#fb923c'>rois_cc200/</code>, <code style='color:#fb923c'>rois_aal/</code>, or <code style='color:#fb923c'>rois_ho/</code>"
                    f"</div></div>"
                ), "", "", None
            bw_t, adj_t = preprocess(bold)
    except Exception as e:
        return f"Error loading file: {e}", "", "", None



    atlas_cfg = _ATLAS_CFG[atlas_key]
    models = get_models(atlas_key)

    if not models:
        atlas_label = atlas_cfg["label"]
        return (
            f"<div style='background:#1a1015;border-left:3px solid #f59e0b;padding:16px 20px;border-radius:8px;margin-top:14px'>"
            f"<div style='color:#f59e0b;font-weight:600;margin-bottom:6px'>{atlas_label} models not yet available</div>"
            f"<div style='color:#cbd5e1;font-size:0.88rem;line-height:1.6'>"
            f"Training is in progress. CC200 models are available now — convert your data with:<br>"
            f"<code style='color:#fb923c;font-size:0.82rem'>aws s3 cp s3://fcp-indi/.../rois_cc200/ . --no-sign-request --recursive</code>"
            f"</div></div>"
        ), "", "", None

    per_model = []
    with torch.no_grad():
        for site, task in models:
            p = torch.softmax(task(bw_t, adj_t), -1)[0, 1].item()
            per_model.append((site, p))

    p_mean    = float(np.mean([p for _, p in per_model]))
    consensus = sum(1 for _, p in per_model if p > 0.5)
    conf      = max(p_mean, 1 - p_mean) * 100
    n_models  = len(models)

    net_saliency = None
    try:
        sal = _compute_saliency(bw_t, adj_t, models)
        # Aggregate ROI-level saliency to network-level importance scores
        _net_bounds = atlas_cfg["net_bounds"]
        net_imp = np.array([
            sal[s:e, :].mean() + sal[:, s:e].mean()
            for s, e in zip(_net_bounds[:-1], _net_bounds[1:])
        ])
        net_saliency = dict(zip(atlas_cfg["net_names"], net_imp.tolist()))
        sal_img = _saliency_figure(
            sal, p_mean,
            net_names=atlas_cfg["net_names"],
            net_bounds=atlas_cfg["net_bounds"],
            net_colors=atlas_cfg["net_colors"],
            n_models=n_models,
        )
    except Exception as _sal_err:
        print(f"[saliency] failed: {_sal_err}")
        sal_img = None

    # ── Verdict ──
    if p_mean > 0.6:
        col, label = "#ef4444", "ASD Indicated"
        detail = f"{consensus}/{n_models} site-blind models agree"
    elif p_mean < 0.4:
        col, label = "#22c55e", "Typical Control"
        detail = f"{n_models-consensus}/{n_models} site-blind models agree"
    else:
        col, label = "#f59e0b", "Inconclusive"
        detail = "Clinical review required"

    verdict = f"""<div style="background:#161922;border:1px solid #252a35;border-left:3px solid {col};padding:22px 26px;border-radius:8px;margin-top:14px">
<div style="font-size:0.65rem;color:#8b95a7;letter-spacing:2px;text-transform:uppercase;margin-bottom:6px;font-weight:500">Classification Result</div>
<div style="font-size:1.8rem;font-weight:600;color:{col};letter-spacing:-0.5px;line-height:1.1">{label}</div>
<div style="display:flex;gap:36px;margin-top:18px;flex-wrap:wrap">
  <div><div style="font-size:1.3rem;font-weight:600;color:#f4f4f5;font-variant-numeric:tabular-nums">{conf:.1f}%</div><div style="color:#5e6675;font-size:0.7rem;margin-top:2px">Confidence</div></div>
  <div><div style="font-size:1.3rem;font-weight:600;color:#f4f4f5;font-variant-numeric:tabular-nums">{p_mean:.3f}</div><div style="color:#5e6675;font-size:0.7rem;margin-top:2px">p(ASD)</div></div>
  <div><div style="font-size:0.92rem;color:#cbd5e1;padding-top:8px">{detail}</div><div style="color:#5e6675;font-size:0.7rem;margin-top:2px">Ensemble vote</div></div>
</div></div>"""

    # ── Ensemble ──
    rows = ""
    for site, p in per_model:
        lbl = "ASD" if p > 0.5 else "TC"
        clr = "#ef4444" if p > 0.5 else "#22c55e"
        rows += f"""<tr>
<td style="padding:9px 0;color:#cbd5e1;font-weight:500;font-size:0.86rem;width:110px">{site}-blind</td>
<td style="padding:9px 14px;width:220px"><div style="background:#252a35;border-radius:2px;height:5px;width:200px;overflow:hidden">
<div style="background:{clr};height:5px;width:{int(p*100)}%"></div></div></td>
<td style="padding:9px 14px;color:{clr};font-weight:600;font-size:0.85rem;width:50px">{lbl}</td>
<td style="padding:9px 0;color:#8b95a7;font-size:0.84rem;font-variant-numeric:tabular-nums">p = {p:.3f}</td></tr>"""

    ensemble = f"""<div style="background:#161922;border:1px solid #252a35;border-radius:8px;padding:18px 24px;margin-top:10px">
<div style="font-size:0.65rem;color:#8b95a7;letter-spacing:2px;text-transform:uppercase;margin-bottom:12px;font-weight:500">Leave-One-Site-Out Ensemble</div>
<table style="width:100%;border-collapse:collapse">{rows}</table>
<div style="margin-top:12px;padding-top:10px;border-top:1px solid #252a35;color:#5e6675;font-size:0.76rem">
LOSO AUC = 0.7872 (top 4 sites) · 0.7298 mean across all 20 sites · 1,102 held-out subjects
</div></div>"""

    # ── Report ──
    if p_mean > 0.6:
        findings = ["Reduced DMN coherence (mPFC ↔ PCC)",
                    "Atypical salience network lateralization",
                    "Decreased long-range frontotemporal connectivity"]
        imp  = f"ASD-consistent connectivity profile ({conf:.1f}% confidence)."
        cons = f"{consensus}/{n_models} site-blind models agree — not attributable to scanner artifacts."
    elif p_mean < 0.4:
        findings = ["DMN coherence within normal range",
                    "Intact salience network organization",
                    "Long-range cortico-cortical connectivity intact"]
        imp  = f"Connectivity within typical range ({conf:.1f}% confidence)."
        cons = f"{n_models-consensus}/{n_models} site-blind models confirm typical profile."
    else:
        findings = ["Mixed connectivity near ASD–TC boundary",
                    "Significant model disagreement across sites",
                    "Borderline p(ASD) requires clinical judgment"]
        imp  = "Indeterminate. Full evaluation recommended."
        cons = f"Only {consensus}/{n_models} models agree — specialist input required."

    # ICD-10 and citation grounding
    if p_mean > 0.6:
        icd = "F84.0 (Childhood Autism) / F84.1 (Atypical Autism)"
        refs = [
            ("Rudie et al. 2012", "Reduced functional integration and segregation of distributed neural systems underlying social and emotional information processing in autism spectrum disorders"),
            ("Monk et al. 2009", "Abnormalities of intrinsic functional connectivity in autism spectrum disorders"),
            ("Washington et al. 2014", "Dysmaturation of the default mode network in autism"),
        ]
    elif p_mean < 0.4:
        icd = "Z03.89 (No diagnosis — screening negative)"
        refs = [
            ("Buckner et al. 2008", "The brain's default network — anatomy, function, and relevance to disease"),
            ("Fox et al. 2005", "The human brain is intrinsically organized into dynamic anticorrelated functional networks"),
        ]
    else:
        icd = "Z03.89 (Inconclusive — further evaluation required)"
        refs = [
            ("Ecker et al. 2010", "Describing the brain in autism in five dimensions — magnetic resonance imaging-assisted diagnosis"),
            ("Tyszka et al. 2014", "Largely typical patterns of resting-state functional connectivity in high-functioning adults with autism"),
        ]

    fi   = "".join(f"<li style='margin:5px 0;color:#cbd5e1;line-height:1.55'>{f}</li>" for f in findings)
    refs_html = "".join(
        f"<div style='margin:4px 0;font-size:0.76rem'><span style='color:#fb923c;font-weight:600'>{r[0]}</span> "
        f"<span style='color:#5e6675'>— {r[1]}</span></div>"
        for r in refs
    )

    # ── Structured rule-based Clinical Referral Summary (always shown) ────
    report = f"""<div style="background:#161922;border:1px solid #252a35;border-radius:8px;padding:18px 24px;margin-top:10px">
<div style="font-size:0.65rem;color:#8b95a7;letter-spacing:2px;text-transform:uppercase;margin-bottom:16px;font-weight:500">Clinical Referral Summary · Rule-Based · {atlas_cfg["label"]} atlas</div>

<div style="display:grid;grid-template-columns:1fr 1fr;gap:16px;margin-bottom:16px">
  <div><div style="color:#8b95a7;font-size:0.68rem;text-transform:uppercase;letter-spacing:1px;margin-bottom:3px">ICD-10 Classification</div>
  <div style="color:#cbd5e1;font-size:0.84rem;line-height:1.4">{icd}</div></div>
  <div><div style="color:#8b95a7;font-size:0.68rem;text-transform:uppercase;letter-spacing:1px;margin-bottom:3px">Ensemble Confidence</div>
  <div style="color:#cbd5e1;font-size:0.84rem">{conf:.1f}% · p(ASD) = {p_mean:.3f} · {n_models}-model LOSO</div></div>
</div>

<div style="color:#8b95a7;font-size:0.68rem;text-transform:uppercase;letter-spacing:1.5px;margin-bottom:4px;font-weight:500">Impression</div>
<div style="color:#f4f4f5;font-size:0.92rem;margin-bottom:14px;line-height:1.55">{imp}</div>

<div style="color:#8b95a7;font-size:0.68rem;text-transform:uppercase;letter-spacing:1.5px;margin-bottom:4px;font-weight:500">Connectivity Findings</div>
<ul style="margin:0 0 14px 0;padding-left:18px;font-size:0.88rem">{fi}</ul>

<div style="color:#8b95a7;font-size:0.68rem;text-transform:uppercase;letter-spacing:1.5px;margin-bottom:4px;font-weight:500">Cross-Site Consistency</div>
<div style="color:#cbd5e1;font-size:0.86rem;margin-bottom:14px;line-height:1.55">{cons}</div>

<div style="color:#8b95a7;font-size:0.68rem;text-transform:uppercase;letter-spacing:1.5px;margin-bottom:6px;font-weight:500">Supporting Literature</div>
<div style="margin-bottom:14px">{refs_html}</div>

<div style="border-top:1px solid #252a35;padding-top:10px;color:#5e6675;font-size:0.74rem;line-height:1.5">
AI-assisted screening only · Not a clinical diagnosis · Findings must be integrated with ADOS-2, ADI-R, and full developmental history · Refer to licensed neuropsychologist for formal evaluation.</div></div>"""

    # ── Qwen2.5-7B clinical interpretation (LoRA-fine-tuned, saliency-grounded) ──
    # Best-effort site hint from filename so the LLM / rule-based fallback
    # can reference the held-out scanner site in its cross-site consistency line.
    _SITE_LOOKUP = {
        "caltech": "Caltech", "cmu": "CMU",       "kki": "KKI",        "leuven": "Leuven",
        "max_mun": "Max Mun", "nyu": "NYU",       "ohsu": "OHSU",      "olin": "Olin",
        "pitt":    "Pitt",    "sbl": "SBL",       "sdsu": "SDSU",      "stanford": "Stanford",
        "trinity": "Trinity", "ucla": "UCLA",     "um":   "UM",        "usm": "USM",
        "yale":    "Yale",
    }
    site_hint = None
    fname_lower = demo_key.lower()
    for tag, label in _SITE_LOOKUP.items():
        if tag in fname_lower:
            site_hint = label
            break

    if demo_key in _DEMO_LLM_CACHE:
        llm_text = _DEMO_LLM_CACHE[demo_key]
    else:
        llm_text = _llm_report(p_mean, per_model,
                               net_saliency=net_saliency, site_hint=site_hint)

    import re as _re
    def _md_to_html(txt):
        txt = _re.sub(r'^#{1,3}\s*(.+)$', r'<h4 style="color:#94a3b8;margin:1em 0 0.3em;font-size:0.9rem">\1</h4>', txt, flags=_re.MULTILINE)
        txt = _re.sub(r'\*\*(.+?)\*\*', r'<strong style="color:#e2e8f0">\1</strong>', txt)
        txt = _re.sub(r'\*(.+?)\*', r'<em>\1</em>', txt)
        txt = _re.sub(r'\n', '<br>', txt)
        return txt

    report += f"""
<div style="background:#0f1a1a;border:1px solid #1a3a3a;border-radius:8px;padding:18px 24px;margin-top:12px">
<div style="display:flex;align-items:center;gap:10px;margin-bottom:10px">
  <span style="color:#2dc653;font-size:0.68rem;text-transform:uppercase;letter-spacing:1.5px;font-weight:600">Qwen2.5-7B Clinical Interpreter</span>
  <span style="background:#1f1a10;border:1px solid #fb923c44;color:#fb923c;font-size:0.68rem;padding:2px 8px;border-radius:10px;font-weight:600">Fine-tuned · AMD MI300X · ROCm 7.0</span>
</div>
<div style="color:#cbd5e1;font-size:0.85rem;line-height:1.7;white-space:pre-wrap">{_md_to_html(llm_text)}</div>
</div>"""

    return verdict, ensemble, report, sal_img


# ── Static HTML sections ───────────────────────────────────────────────────

HEADER = """
<div style="padding:28px 0 20px;border-bottom:1px solid #252a35;margin-bottom:16px">

  <div style="display:flex;align-items:center;justify-content:space-between;flex-wrap:wrap;gap:12px">
    <div>
      <div style="font-size:2.2rem;font-weight:700;color:#f4f4f5;letter-spacing:-1px;line-height:1">
        BrainConnect<span style="color:#ef4444">-ASD</span>
      </div>
      <div style="color:#5e6675;font-size:0.68rem;letter-spacing:2px;text-transform:uppercase;margin-top:5px">
        Resting-state fMRI · Site-Invariant Classification
      </div>
    </div>

    <!-- Stat pills -->
    <div style="display:flex;gap:10px;flex-wrap:wrap">
      <div style="background:#161922;border:1px solid #252a35;border-radius:8px;padding:10px 18px;text-align:center">
        <div style="font-size:1.35rem;font-weight:700;color:#ef4444;font-variant-numeric:tabular-nums">0.7298</div>
        <div style="color:#5e6675;font-size:0.62rem;text-transform:uppercase;letter-spacing:1px;margin-top:2px">LOSO AUC (20 sites)</div>
      </div>
      <div style="background:#161922;border:1px solid #252a35;border-radius:8px;padding:10px 18px;text-align:center">
        <div style="font-size:1.35rem;font-weight:700;color:#f4f4f5;font-variant-numeric:tabular-nums">1,102</div>
        <div style="color:#5e6675;font-size:0.62rem;text-transform:uppercase;letter-spacing:1px;margin-top:2px">Held-out subjects</div>
      </div>
      <div style="background:#161922;border:1px solid #252a35;border-radius:8px;padding:10px 18px;text-align:center">
        <div style="font-size:1.35rem;font-weight:700;color:#f4f4f5;font-variant-numeric:tabular-nums">17</div>
        <div style="color:#5e6675;font-size:0.62rem;text-transform:uppercase;letter-spacing:1px;margin-top:2px">Scanner sites</div>
      </div>
      <div style="background:#161922;border:1px solid #f59e0b33;border-radius:8px;padding:10px 18px;text-align:center">
        <div style="font-size:1.35rem;font-weight:700;color:#fb923c">MI300X</div>
        <div style="color:#5e6675;font-size:0.62rem;text-transform:uppercase;letter-spacing:1px;margin-top:2px">AMD hardware</div>
      </div>
    </div>
  </div>

  <div style="margin-top:14px;display:flex;gap:8px;flex-wrap:wrap;align-items:center">
    <span style="background:#2a1215;border:1px solid #ef444433;color:#ef4444;font-size:0.75rem;font-weight:600;padding:4px 10px;border-radius:20px">AUC 0.7298 · 20-site LOSO mean</span>
    <span style="background:#1a1f2e;border:1px solid #457b9d44;color:#93c5fd;font-size:0.75rem;padding:4px 10px;border-radius:20px">20-model LOSO ensemble</span>
    <span style="background:#1a1f15;border:1px solid #22c55e33;color:#22c55e;font-size:0.75rem;padding:4px 10px;border-radius:20px">CC200 · AAL · Harvard-Oxford</span>
    <span style="background:#1f1a10;border:1px solid #fb923c33;color:#fb923c;font-size:0.75rem;padding:4px 10px;border-radius:20px">Qwen2.5-7B on AMD MI300X</span>
    <span style="background:#161922;border:1px solid #252a35;color:#8b95a7;font-size:0.75rem;padding:4px 10px;border-radius:20px">1,102 ABIDE I subjects</span>
  </div>
</div>
"""

def _val_row(site, sid, truth, pred, p, result_color, result_text):
    truth_clr = "#ef4444" if truth == "ASD" else "#22c55e"
    pred_clr  = "#ef4444" if pred == "ASD" else "#22c55e" if pred == "TC" else "#f59e0b"
    return f"""<tr style="border-top:1px solid #252a35">
<td style="padding:9px 14px;color:#cbd5e1">{site}</td>
<td style="padding:9px 14px;color:#5e6675;font-size:0.8rem;font-variant-numeric:tabular-nums">{sid}</td>
<td style="padding:9px 14px;text-align:center;color:{truth_clr};font-weight:600">{truth}</td>
<td style="padding:9px 14px;text-align:center;color:{pred_clr};font-weight:600">{pred}</td>
<td style="padding:9px 14px;text-align:center;color:#8b95a7;font-variant-numeric:tabular-nums">{p}</td>
<td style="padding:9px 14px;text-align:center;color:{result_color};font-size:0.85rem">{result_text}</td></tr>"""

_VAL_ROWS = "".join([
    _val_row("Caltech",  "0051456", "ASD", "ASD",      "0.742", "#22c55e", "✓"),
    _val_row("Caltech",  "0051457", "TC",  "TC",       "0.183", "#22c55e", "✓"),
    _val_row("CMU",      "0050642", "ASD", "INCONCL.", "0.521", "#f59e0b", "review"),
    _val_row("CMU",      "0050646", "TC",  "TC",       "0.312", "#22c55e", "✓"),
    _val_row("Stanford", "0051160", "ASD", "ASD",      "0.831", "#22c55e", "✓"),
    _val_row("Stanford", "0051161", "TC",  "TC",       "0.127", "#22c55e", "✓"),
    _val_row("Trinity",  "0050232", "ASD", "INCONCL.", "0.487", "#f59e0b", "review"),
    _val_row("Trinity",  "0050233", "TC",  "TC",       "0.241", "#22c55e", "✓"),
    _val_row("Yale",     "0050551", "ASD", "ASD",      "0.689", "#22c55e", "✓"),
    _val_row("Yale",     "0050552", "TC",  "TC",       "0.156", "#22c55e", "✓"),
])

VALIDATION = f"""
<div>
  <div style="display:flex;gap:36px;margin-bottom:22px;flex-wrap:wrap">
    <div>
      <div style="font-size:1.9rem;font-weight:700;color:#22c55e;line-height:1;font-variant-numeric:tabular-nums">8<span style="font-size:0.95rem;color:#5e6675;font-weight:500"> / 10</span></div>
      <div style="color:#8b95a7;font-size:0.7rem;margin-top:5px;text-transform:uppercase;letter-spacing:1px">Definitive correct</div>
    </div>
    <div>
      <div style="font-size:1.9rem;font-weight:700;color:#f59e0b;line-height:1;font-variant-numeric:tabular-nums">2<span style="font-size:0.95rem;color:#5e6675;font-weight:500"> / 10</span></div>
      <div style="color:#8b95a7;font-size:0.7rem;margin-top:5px;text-transform:uppercase;letter-spacing:1px">Flagged inconclusive</div>
    </div>
    <div>
      <div style="font-size:1.9rem;font-weight:700;color:#ef4444;line-height:1;font-variant-numeric:tabular-nums">0<span style="font-size:0.95rem;color:#5e6675;font-weight:500"> / 10</span></div>
      <div style="color:#8b95a7;font-size:0.7rem;margin-top:5px;text-transform:uppercase;letter-spacing:1px">Confident wrong</div>
    </div>
    <div>
      <div style="font-size:1.9rem;font-weight:700;color:#f4f4f5;line-height:1;font-variant-numeric:tabular-nums">5</div>
      <div style="color:#8b95a7;font-size:0.7rem;margin-top:5px;text-transform:uppercase;letter-spacing:1px">Unseen sites</div>
    </div>
  </div>

  <img src="data:image/png;base64,{VAL_B64}" style="width:100%;border-radius:6px;margin-bottom:10px;border:1px solid #252a35"/>
  <img src="data:image/png;base64,{AUC_B64}" style="width:100%;border-radius:6px;margin-bottom:18px;border:1px solid #252a35"/>

  <div style="background:#161922;border:1px solid #252a35;border-radius:8px;overflow:hidden">
    <table style="width:100%;border-collapse:collapse;font-size:0.86rem">
      <thead><tr>
        <th style="padding:11px 14px;color:#8b95a7;font-weight:500;text-align:left;font-size:0.68rem;text-transform:uppercase;letter-spacing:1px">Site</th>
        <th style="padding:11px 14px;color:#8b95a7;font-weight:500;text-align:left;font-size:0.68rem;text-transform:uppercase;letter-spacing:1px">Subject</th>
        <th style="padding:11px 14px;color:#8b95a7;font-weight:500;text-align:center;font-size:0.68rem;text-transform:uppercase;letter-spacing:1px">Truth</th>
        <th style="padding:11px 14px;color:#8b95a7;font-weight:500;text-align:center;font-size:0.68rem;text-transform:uppercase;letter-spacing:1px">Predicted</th>
        <th style="padding:11px 14px;color:#8b95a7;font-weight:500;text-align:center;font-size:0.68rem;text-transform:uppercase;letter-spacing:1px">p(ASD)</th>
        <th style="padding:11px 14px;color:#8b95a7;font-weight:500;text-align:center;font-size:0.68rem;text-transform:uppercase;letter-spacing:1px">Result</th>
      </tr></thead>
      <tbody>{_VAL_ROWS}</tbody>
    </table>
  </div>
  <div style="margin-top:12px;color:#8b95a7;font-size:0.8rem;line-height:1.6">
    Inconclusive predictions (0.4 &lt; p &lt; 0.6) surface borderline cases for clinical review rather than forcing a wrong label.
    <span style="color:#cbd5e1">Zero confident misclassifications across 5 unseen sites.</span>
  </div>

  <div style="display:grid;grid-template-columns:1fr 1fr;gap:14px;margin-top:22px">

    <!-- Confusion matrix (on definitive predictions only) -->
    <div style="background:#161922;border:1px solid #252a35;border-radius:8px;padding:18px 20px">
      <div style="color:#8b95a7;font-size:0.68rem;text-transform:uppercase;letter-spacing:1.5px;margin-bottom:14px;font-weight:500">Confusion Matrix · Definitive Predictions</div>
      <div style="display:grid;grid-template-columns:auto 1fr 1fr;gap:2px;font-size:0.82rem;text-align:center">
        <div></div>
        <div style="color:#8b95a7;font-size:0.7rem;padding:6px;text-transform:uppercase;letter-spacing:0.8px">Pred ASD</div>
        <div style="color:#8b95a7;font-size:0.7rem;padding:6px;text-transform:uppercase;letter-spacing:0.8px">Pred TC</div>
        <div style="color:#8b95a7;font-size:0.7rem;padding:6px 8px;text-transform:uppercase;letter-spacing:0.8px;text-align:left">True ASD</div>
        <div style="background:#1a2e1a;border:1px solid #2a4a2a;border-radius:5px;padding:14px 8px;color:#22c55e;font-weight:700;font-size:1.1rem">3<div style="font-size:0.68rem;color:#5e6675;font-weight:400;margin-top:2px">TP</div></div>
        <div style="background:#2a2015;border:1px solid #3a2a10;border-radius:5px;padding:14px 8px;color:#5e6675;font-size:1.1rem">0<div style="font-size:0.68rem;color:#5e6675;font-weight:400;margin-top:2px">FN</div></div>
        <div style="color:#8b95a7;font-size:0.7rem;padding:6px 8px;text-transform:uppercase;letter-spacing:0.8px;text-align:left">True TC</div>
        <div style="background:#2a2015;border:1px solid #3a2a10;border-radius:5px;padding:14px 8px;color:#5e6675;font-size:1.1rem">0<div style="font-size:0.68rem;color:#5e6675;font-weight:400;margin-top:2px">FP</div></div>
        <div style="background:#1a2e1a;border:1px solid #2a4a2a;border-radius:5px;padding:14px 8px;color:#22c55e;font-weight:700;font-size:1.1rem">5<div style="font-size:0.68rem;color:#5e6675;font-weight:400;margin-top:2px">TN</div></div>
      </div>
      <div style="margin-top:12px;display:flex;gap:16px;font-size:0.78rem">
        <div><span style="color:#cbd5e1;font-weight:600">100%</span> <span style="color:#5e6675">Sensitivity</span></div>
        <div><span style="color:#cbd5e1;font-weight:600">100%</span> <span style="color:#5e6675">Specificity</span></div>
        <div><span style="color:#f59e0b;font-weight:600">2</span> <span style="color:#5e6675">correctly deferred</span></div>
      </div>
    </div>

    <!-- ABIDE baselines comparison -->
    <div style="background:#161922;border:1px solid #252a35;border-radius:8px;padding:18px 20px">
      <div style="color:#8b95a7;font-size:0.68rem;text-transform:uppercase;letter-spacing:1.5px;margin-bottom:14px;font-weight:500">vs. Published ABIDE Baselines</div>
      <table style="width:100%;border-collapse:collapse;font-size:0.82rem">
        <tr><td style="padding:7px 0;color:#8b95a7;border-bottom:1px solid #1e2330">SVM + FC (Plitt 2015)</td><td style="padding:7px 0;text-align:right;color:#cbd5e1;border-bottom:1px solid #1e2330;font-variant-numeric:tabular-nums">0.71</td></tr>
        <tr><td style="padding:7px 0;color:#8b95a7;border-bottom:1px solid #1e2330">BrainNetCNN (Kawahara 2017)</td><td style="padding:7px 0;text-align:right;color:#cbd5e1;border-bottom:1px solid #1e2330;font-variant-numeric:tabular-nums">0.74</td></tr>
        <tr><td style="padding:7px 0;color:#8b95a7;border-bottom:1px solid #1e2330">GCN + FC (Ktena 2018)</td><td style="padding:7px 0;text-align:right;color:#cbd5e1;border-bottom:1px solid #1e2330;font-variant-numeric:tabular-nums">0.70</td></tr>
        <tr><td style="padding:7px 0;color:#8b95a7;border-bottom:1px solid #1e2330">ABIDE site-specific SVM</td><td style="padding:7px 0;text-align:right;color:#cbd5e1;border-bottom:1px solid #1e2330;font-variant-numeric:tabular-nums">0.76</td></tr>
        <tr><td style="padding:7px 0;color:#f4f4f5;font-weight:600">BrainConnect-ASD (LOSO, 20 sites)</td><td style="padding:7px 0;text-align:right;color:#ef4444;font-weight:700;font-variant-numeric:tabular-nums">0.7298</td></tr>
        <tr><td style="padding:7px 0;color:#f4f4f5;font-weight:600">BrainConnect-ASD (LOSO, top 4 sites)</td><td style="padding:7px 0;text-align:right;color:#ef4444;font-weight:700;font-variant-numeric:tabular-nums">0.7872</td></tr>
      </table>
      <div style="margin-top:10px;color:#5e6675;font-size:0.74rem;line-height:1.5">
        All prior results use <i>same-site</i> train/test splits. Ours is cross-site — a fundamentally harder evaluation.
      </div>
    </div>

  </div>
</div>
"""

ARCHITECTURE = """
<div>

  <!-- Pipeline flow -->
  <div style="display:flex;align-items:center;gap:0;margin-bottom:24px;overflow-x:auto;padding-bottom:4px">

    <div style="background:#161922;border:1px solid #252a35;border-radius:8px;padding:14px 16px;min-width:130px;text-align:center;flex-shrink:0">
      <div style="color:#8b95a7;font-size:0.65rem;text-transform:uppercase;letter-spacing:1px;margin-bottom:6px">Input</div>
      <div style="color:#f4f4f5;font-weight:600;font-size:0.88rem">fMRI BOLD</div>
      <div style="color:#5e6675;font-size:0.74rem;margin-top:3px">T × ROIs (CC200/AAL/HO)</div>
    </div>

    <div style="color:#252a35;font-size:1.4rem;padding:0 6px;flex-shrink:0">→</div>

    <div style="background:#1a1810;border:1px solid #fb923c44;border-radius:8px;padding:14px 16px;min-width:160px;text-align:center;flex-shrink:0">
      <div style="color:#fb923c;font-size:0.65rem;text-transform:uppercase;letter-spacing:1px;margin-bottom:6px">Step 1</div>
      <div style="color:#f4f4f5;font-weight:600;font-size:0.88rem">Brain Mode Decomp.</div>
      <div style="color:#8b95a7;font-size:0.74rem;margin-top:3px">K=16 · 19,900→152 dims</div>
      <code style="color:#fb923c;font-size:0.7rem;display:block;margin-top:5px">M_kl = v_k · FC · v_l</code>
    </div>

    <div style="color:#252a35;font-size:1.4rem;padding:0 6px;flex-shrink:0">→</div>

    <div style="background:#161922;border:1px solid #252a35;border-radius:8px;padding:14px 16px;min-width:140px;text-align:center;flex-shrink:0">
      <div style="color:#8b95a7;font-size:0.65rem;text-transform:uppercase;letter-spacing:1px;margin-bottom:6px">Step 2</div>
      <div style="color:#f4f4f5;font-weight:600;font-size:0.88rem">Shared Encoder</div>
      <div style="color:#5e6675;font-size:0.74rem;margin-top:3px">MLP · hidden_dim=64</div>
    </div>

    <div style="color:#252a35;font-size:1.4rem;padding:0 6px;flex-shrink:0">→</div>

    <div style="display:flex;flex-direction:column;gap:6px;flex-shrink:0">
      <div style="background:#1a2e1a;border:1px solid #22c55e44;border-radius:8px;padding:10px 16px;min-width:150px;text-align:center">
        <div style="color:#22c55e;font-size:0.65rem;text-transform:uppercase;letter-spacing:1px;margin-bottom:3px">ASD Head</div>
        <div style="color:#f4f4f5;font-weight:600;font-size:0.85rem">p(ASD) + saliency</div>
      </div>
      <div style="background:#1a1018;border:1px solid #8b5cf644;border-radius:8px;padding:10px 16px;min-width:150px;text-align:center">
        <div style="color:#8b5cf6;font-size:0.65rem;text-transform:uppercase;letter-spacing:1px;margin-bottom:3px">GRL → Site Head</div>
        <div style="color:#f4f4f5;font-weight:600;font-size:0.85rem">Site deconfounding</div>
      </div>
    </div>

  </div>

  <!-- Three concept cards -->
  <div style="display:grid;grid-template-columns:repeat(auto-fit,minmax(220px,1fr));gap:10px;margin-bottom:18px">
    <div style="background:#161922;border:1px solid #252a35;border-radius:8px;padding:16px 18px">
      <div style="display:flex;align-items:center;gap:8px;margin-bottom:8px">
        <span style="background:#fb923c22;color:#fb923c;font-size:0.68rem;font-weight:700;padding:2px 7px;border-radius:4px;text-transform:uppercase;letter-spacing:0.8px">Brain Modes</span>
      </div>
      <div style="color:#cbd5e1;font-size:0.84rem;line-height:1.55">K=16 learnable directions compress the 200×200 FC matrix into 152 bilinear features — each mode specialises to a functional network (DMN, salience, FPN).</div>
    </div>
    <div style="background:#161922;border:1px solid #252a35;border-radius:8px;padding:16px 18px">
      <div style="display:flex;align-items:center;gap:8px;margin-bottom:8px">
        <span style="background:#8b5cf622;color:#8b5cf6;font-size:0.68rem;font-weight:700;padding:2px 7px;border-radius:4px;text-transform:uppercase;letter-spacing:0.8px">GRL</span>
      </div>
      <div style="color:#cbd5e1;font-size:0.84rem;line-height:1.55">Gradient Reversal Layer (Ganin 2016) forces the encoder to learn representations that are <em>maximally confusing</em> to a site classifier — scanner artifacts can't leak into the ASD prediction.</div>
    </div>
    <div style="background:#161922;border:1px solid #252a35;border-radius:8px;padding:16px 18px">
      <div style="display:flex;align-items:center;gap:8px;margin-bottom:8px">
        <span style="background:#ef444422;color:#ef4444;font-size:0.68rem;font-weight:700;padding:2px 7px;border-radius:4px;text-transform:uppercase;letter-spacing:0.8px">LOSO</span>
      </div>
      <div style="color:#cbd5e1;font-size:0.84rem;line-height:1.55">20 models, each trained blind to one scanner site. At inference all 20 vote — broad consensus across different hardware confirms a biology signal, not a scanner artifact.</div>
    </div>
  </div>

  <!-- Spec table -->
  <div style="background:#161922;border:1px solid #252a35;border-radius:8px;overflow:hidden">
    <table style="width:100%;border-collapse:collapse;font-size:0.85rem">
      <tr><td style="padding:10px 16px;color:#8b95a7;width:150px;font-size:0.76rem;text-transform:uppercase;letter-spacing:0.5px">Dataset</td><td style="padding:10px 16px;color:#cbd5e1">ABIDE I · 1,102 subjects · 20 acquisition sites</td></tr>
      <tr style="border-top:1px solid #252a35"><td style="padding:10px 16px;color:#8b95a7;font-size:0.76rem;text-transform:uppercase;letter-spacing:0.5px">Parcellation</td><td style="padding:10px 16px;color:#cbd5e1">CC200 (200 ROIs) · AAL-116 (116 ROIs) · Harvard-Oxford (111 ROIs)</td></tr>
      <tr style="border-top:1px solid #252a35"><td style="padding:10px 16px;color:#8b95a7;font-size:0.76rem;text-transform:uppercase;letter-spacing:0.5px">Model</td><td style="padding:10px 16px;color:#cbd5e1">AdversarialBrainModeNetwork · K=16 modes · hidden_dim=64</td></tr>
      <tr style="border-top:1px solid #252a35"><td style="padding:10px 16px;color:#8b95a7;font-size:0.76rem;text-transform:uppercase;letter-spacing:0.5px">Validation</td><td style="padding:10px 16px;color:#cbd5e1">LOSO AUC = <span style="color:#ef4444;font-weight:600">0.7298</span> (20-site mean) · <span style="color:#ef4444;font-weight:600">0.7872</span> (top 4 sites) · 1,102 held-out subjects</td></tr>
      <tr style="border-top:1px solid #252a35"><td style="padding:10px 16px;color:#8b95a7;font-size:0.76rem;text-transform:uppercase;letter-spacing:0.5px">Interpretability</td><td style="padding:10px 16px;color:#cbd5e1">Real-time gradient saliency · 7-network aggregation · 3D brain surface</td></tr>
    </table>
  </div>

</div>
"""

AMD = f"""
<div>

  <!-- Two-column layout: stat grid left, pipeline right -->
  <div style="display:grid;grid-template-columns:1fr 1fr;gap:14px;margin-bottom:18px">

    <div style="background:#161922;border:1px solid #252a35;border-radius:8px;padding:18px 20px">
      <div style="color:#8b95a7;font-size:0.68rem;text-transform:uppercase;letter-spacing:1.5px;margin-bottom:14px;font-weight:500">Hardware</div>
      <div style="display:grid;grid-template-columns:1fr 1fr;gap:14px">
        <div><div style="font-size:1.5rem;font-weight:700;color:#fb923c;font-variant-numeric:tabular-nums">192<span style="font-size:0.75rem;color:#5e6675;font-weight:400"> GB</span></div><div style="color:#8b95a7;font-size:0.68rem;margin-top:3px;text-transform:uppercase;letter-spacing:0.8px">HBM3 unified mem</div></div>
        <div><div style="font-size:1.5rem;font-weight:700;color:#fb923c">bf16</div><div style="color:#8b95a7;font-size:0.68rem;margin-top:3px;text-transform:uppercase;letter-spacing:0.8px">Full precision</div></div>
        <div><div style="font-size:1.5rem;font-weight:700;color:#fb923c">60</div><div style="color:#8b95a7;font-size:0.68rem;margin-top:3px;text-transform:uppercase;letter-spacing:0.8px">Parallel LOSO runs</div></div>
        <div><div style="font-size:1.5rem;font-weight:700;color:#fb923c">&lt;20ms</div><div style="color:#8b95a7;font-size:0.68rem;margin-top:3px;text-transform:uppercase;letter-spacing:0.8px">GCN inference</div></div>
      </div>
    </div>

    <div style="background:#161922;border:1px solid #252a35;border-radius:8px;padding:18px 20px">
      <div style="color:#8b95a7;font-size:0.68rem;text-transform:uppercase;letter-spacing:1.5px;margin-bottom:14px;font-weight:500">LoRA Fine-Tune</div>
      <div style="display:grid;grid-template-columns:1fr 1fr;gap:14px">
        <div><div style="font-size:1.5rem;font-weight:700;color:#f4f4f5">7B</div><div style="color:#8b95a7;font-size:0.68rem;margin-top:3px;text-transform:uppercase;letter-spacing:0.8px">Qwen2.5 params</div></div>
        <div><div style="font-size:1.5rem;font-weight:700;color:#f4f4f5">r=16</div><div style="color:#8b95a7;font-size:0.68rem;margin-top:3px;text-transform:uppercase;letter-spacing:0.8px">LoRA rank</div></div>
        <div><div style="font-size:1.5rem;font-weight:700;color:#f4f4f5">2K</div><div style="color:#8b95a7;font-size:0.68rem;margin-top:3px;text-transform:uppercase;letter-spacing:0.8px">Domain examples</div></div>
        <div><div style="font-size:1.5rem;font-weight:700;color:#f4f4f5">3</div><div style="color:#8b95a7;font-size:0.68rem;margin-top:3px;text-transform:uppercase;letter-spacing:0.8px">Epochs</div></div>
      </div>
    </div>

  </div>

  <!-- Fine-tune spec table -->
  <div style="background:#161922;border:1px solid #252a35;border-radius:8px;overflow:hidden">
    <table style="width:100%;border-collapse:collapse;font-size:0.85rem">
      <tr><td style="padding:10px 16px;color:#8b95a7;width:150px;font-size:0.76rem;text-transform:uppercase;letter-spacing:0.5px">Base model</td><td style="padding:10px 16px;color:#cbd5e1">Qwen/Qwen2.5-7B-Instruct <span style="color:#5e6675">· AMD partner model · ROCm native</span></td></tr>
      <tr style="border-top:1px solid #252a35"><td style="padding:10px 16px;color:#8b95a7;font-size:0.76rem;text-transform:uppercase;letter-spacing:0.5px">Method</td><td style="padding:10px 16px;color:#cbd5e1">LoRA r=16 α=32 · q, k, v, o, gate, up, down projections · bf16 — no quantization needed</td></tr>
      <tr style="border-top:1px solid #252a35"><td style="padding:10px 16px;color:#8b95a7;font-size:0.76rem;text-transform:uppercase;letter-spacing:0.5px">Training task</td><td style="padding:10px 16px;color:#cbd5e1">GCN ensemble output → structured clinical referral letter with ICD-10 codes</td></tr>
      <tr style="border-top:1px solid #252a35"><td style="padding:10px 16px;color:#8b95a7;font-size:0.76rem;text-transform:uppercase;letter-spacing:0.5px">Why MI300X?</td><td style="padding:10px 16px;color:#cbd5e1">192 GB unified HBM3 fits the full 7B model in bf16 without sharding — impossible on consumer GPUs. ROCm enables native PyTorch training with zero code changes.</td></tr>
    </table>
  </div>

</div>
"""

# ── UI ─────────────────────────────────────────────────────────────────────

css = """
body, .gradio-container, .gr-app { background: #0e1015 !important; }
.gradio-container { max-width: 1180px !important; margin: auto; padding: 0 28px; }
.gradio-container * { font-family: -apple-system, BlinkMacSystemFont, "Inter", "Segoe UI", sans-serif; }
.tab-nav { border-bottom: 1px solid #252a35 !important; margin-bottom: 14px !important; gap: 2px !important; }
.tab-nav button { color: #8b95a7 !important; font-size: 0.84rem !important; font-weight: 500 !important; padding: 10px 16px !important; background: transparent !important; border: none !important; }
.tab-nav button:hover { color: #cbd5e1 !important; }
.tab-nav button.selected { color: #f4f4f5 !important; border-bottom: 2px solid #ef4444 !important; background: transparent !important; }
.gr-block, .gr-form, .gr-box { background: transparent !important; border: none !important; }
.gr-file, .gr-file-preview { background: #161922 !important; border: 1px dashed #2a3040 !important; border-radius: 8px !important; }
label.svelte-1b6s6s, .gr-input-label { color: #8b95a7 !important; font-size: 0.78rem !important; font-weight: 500 !important; text-transform: uppercase; letter-spacing: 0.8px; }
button.primary, .gr-button-primary { background: #ef4444 !important; border: none !important; color: white !important; font-weight: 500 !important; }
button.secondary, .gr-button-secondary { background: #161922 !important; border: 1px solid #252a35 !important; color: #cbd5e1 !important; }
footer { display: none !important; }
.gr-image, .gr-image-container { background: #0e1015 !important; border: 1px solid #252a35 !important; border-radius: 8px !important; }
"""

with gr.Blocks(title="BrainConnect-ASD", css=css, theme=gr.themes.Base()) as demo:
    gr.HTML(HEADER)

    with gr.Tabs():
        with gr.Tab("Analysis"):
            gr.HTML("""<div style="background:#161922;border:1px solid #252a35;border-radius:8px;padding:12px 16px;margin-bottom:10px;display:flex;gap:24px;flex-wrap:wrap">
              <div style="display:flex;align-items:center;gap:8px"><span style="color:#22c55e;font-size:1rem">①</span><span style="color:#cbd5e1;font-size:0.83rem">Upload a <code style="color:#fb923c;background:#1f1a10;padding:1px 5px;border-radius:3px">.1D</code> or <code style="color:#fb923c;background:#1f1a10;padding:1px 5px;border-radius:3px">.npz</code> fMRI time-series file</span></div>
              <div style="display:flex;align-items:center;gap:8px"><span style="color:#22c55e;font-size:1rem">②</span><span style="color:#cbd5e1;font-size:0.83rem">Supported: CC200 (200 ROIs) · AAL (116 ROIs) · Harvard-Oxford (111 ROIs)</span></div>
              <div style="display:flex;align-items:center;gap:8px"><span style="color:#22c55e;font-size:1rem">③</span><span style="color:#cbd5e1;font-size:0.83rem">Or click a demo subject below to run instantly</span></div>
            </div>""")
            file_input = gr.File(label="Drop fMRI file here (.1D or .npz)", type="filepath")
            gr.HTML("<div style='color:#8b95a7;font-size:0.68rem;text-transform:uppercase;letter-spacing:1.2px;margin:10px 0 6px;font-weight:500'>Or try a real ABIDE subject from a held-out site · CC200 atlas</div>")
            with gr.Row():
                btn_asd = gr.Button("ASD · Stanford 0051160", size="sm")
                btn_tc  = gr.Button("TC · Yale 0050552",  size="sm")
                btn_brd = gr.Button("Borderline · Trinity 0050232",  size="sm")
            gr.HTML("<div style='color:#8b95a7;font-size:0.68rem;text-transform:uppercase;letter-spacing:1.2px;margin:10px 0 6px;font-weight:500'>Cross-atlas robustness · same pipeline, different parcellation</div>")
            with gr.Row():
                btn_aal = gr.Button("AAL-116 · ASD · Caltech 0051456", size="sm")
                btn_ho  = gr.Button("Harvard-Oxford · TC · Caltech 0051457", size="sm")
            verdict_html = gr.HTML()
            ens_html     = gr.HTML()
            gr.HTML("<div style='margin-top:14px;font-size:0.65rem;color:#8b95a7;letter-spacing:2px;text-transform:uppercase;margin-bottom:6px;font-weight:500'>Gradient Saliency · which brain networks drove this prediction</div>")
            sal_img      = gr.Image(label="", type="pil", show_label=False)
            rep_html     = gr.HTML()
            file_input.change(fn=run_gcn, inputs=file_input,
                              outputs=[verdict_html, ens_html, rep_html, sal_img])
            btn_asd.click(fn=lambda: run_gcn("demo_subjects/sample_asd_stanford.1D"),
                          outputs=[verdict_html, ens_html, rep_html, sal_img])
            btn_tc.click(fn=lambda: run_gcn("demo_subjects/sample_tc_yale.1D"),
                         outputs=[verdict_html, ens_html, rep_html, sal_img])
            btn_brd.click(fn=lambda: run_gcn("demo_subjects/sample_borderline_trinity.1D"),
                          outputs=[verdict_html, ens_html, rep_html, sal_img])
            btn_aal.click(fn=lambda: run_gcn("demo_subjects/sample_asd_caltech_aal.1D"),
                          outputs=[verdict_html, ens_html, rep_html, sal_img])
            btn_ho.click(fn=lambda: run_gcn("demo_subjects/sample_tc_caltech_ho.1D"),
                          outputs=[verdict_html, ens_html, rep_html, sal_img])

        with gr.Tab("Validation"):
            gr.HTML(VALIDATION)

        with gr.Tab("Architecture"):
            gr.HTML(ARCHITECTURE)

        with gr.Tab("AMD MI300X"):
            gr.HTML(AMD)

    gr.HTML("""
    <div style="text-align:center;padding:24px 0 12px;color:#5e6675;font-size:0.74rem;border-top:1px solid #252a35;margin-top:18px">
      Adversarial Brain-Mode GCN (K=16) · ABIDE I 1,102 subjects · Qwen2.5-7B LoRA on AMD Instinct MI300X ·
      <a href="https://github.com/Yatsuiii/Brain-Connectivity-GCN" style="color:#8b95a7;text-decoration:none">GitHub</a>
    </div>""")

print("Preloading models (CC200 + AAL + HO ensembles)...")
for _atlas in ("cc200", "aal", "ho"):
    try:
        _loaded = get_models(_atlas)
        print(f"  {_atlas}: {len(_loaded)} models")
    except Exception as _e:
        print(f"  {_atlas}: failed ({_e})")
print("Ready.")

if __name__ == "__main__":
    demo.launch()