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app.py

@@ -1,22 +1,14 @@
 """
 BrainConnect-ASD — Scanner-site-invariant ASD detection from fMRI.
-
-Ensemble of 4 adversarial GCNs trained with leave-one-site-out CV on ABIDE I.
-Each model held out a different scanner site (NYU / USM / UCLA / UM).
-LOSO mean AUC = 0.7872 across 529 unseen subjects from 4 institutions.
-
-Fine-tuned Qwen2.5-7B-Instruct clinical report generation runs on AMD MI300X.
 """
 from __future__ import annotations
 
-import sys
 from pathlib import Path
 
 import numpy as np
 import torch
 import gradio as gr
 
-# ── preprocessing constants ────────────────────────────────────────────────
 _WINDOW_LEN   = 50
 _STEP         = 3
 _MAX_WINDOWS  = 30
@@ -29,7 +21,6 @@ _CKPTS = {
     "UM":   Path("checkpoints/um.ckpt"),
 }
 
-
 # ── preprocessing ──────────────────────────────────────────────────────────
 
 def _zscore(bold):
@@ -59,8 +50,7 @@ def preprocess(bold):
     bw   = _windows(bold)
     return torch.FloatTensor(bw).unsqueeze(0), torch.FloatTensor(adj).unsqueeze(0)
 
-
-# ── model loading (cached) ─────────────────────────────────────────────────
+# ── model loading ──────────────────────────────────────────────────────────
 
 _models: list | None = None
 
@@ -78,13 +68,12 @@ def get_models():
         _models.append((site, task))
     return _models
 
-
 # ── inference ──────────────────────────────────────────────────────────────
 
 @torch.no_grad()
-def run_gcn(file_path: str | None) -> tuple[str, str]:
+def run_gcn(file_path: str | None) -> tuple[str, str, str]:
     if file_path is None:
-        return "Upload a file to begin.", ""
+        return "", "", ""
 
     path = Path(file_path)
     try:
@@ -103,10 +92,10 @@ def run_gcn(file_path: str | None) -> tuple[str, str]:
         else:
             bold = np.loadtxt(path, dtype=np.float32)
             if bold.ndim != 2 or bold.shape[1] != 200:
-                return f"Error: expected (T×200) array, got {bold.shape}", ""
+                return f"⚠️ Error: expected (T×200) array, got {bold.shape}", "", ""
             bw_t, adj_t = preprocess(bold)
     except Exception as e:
-        return f"Error loading file: {e}", ""
+        return f"⚠️ Error loading file: {e}", "", ""
 
     models = get_models()
     per_model = []
@@ -119,105 +108,122 @@ def run_gcn(file_path: str | None) -> tuple[str, str]:
     consensus = sum(1 for _, p in per_model if p > 0.5)
     conf      = max(p_mean, 1 - p_mean) * 100
 
+    # ── Verdict ──
     if p_mean > 0.6:
-        label = "ASD"
-        status = "HIGH CONFIDENCE"
+        verdict = f"""<div style="background:#1a1a2e;border-left:6px solid #e63946;padding:24px 28px;border-radius:12px;margin-bottom:8px">
+<div style="font-size:2rem;font-weight:800;color:#e63946;letter-spacing:1px">ASD INDICATED</div>
+<div style="font-size:1.1rem;color:#aaa;margin-top:6px">Confidence: <b style="color:white">{conf:.1f}%</b> &nbsp;|&nbsp; p(ASD) = <b style="color:white">{p_mean:.3f}</b> &nbsp;|&nbsp; <b style="color:white">{consensus}/4</b> site-blind models agree</div>
+</div>"""
     elif p_mean < 0.4:
-        label = "Typical Control"
-        status = "HIGH CONFIDENCE"
+        verdict = f"""<div style="background:#1a1a2e;border-left:6px solid #2dc653;padding:24px 28px;border-radius:12px;margin-bottom:8px">
+<div style="font-size:2rem;font-weight:800;color:#2dc653;letter-spacing:1px">TYPICAL CONTROL</div>
+<div style="font-size:1.1rem;color:#aaa;margin-top:6px">Confidence: <b style="color:white">{conf:.1f}%</b> &nbsp;|&nbsp; p(ASD) = <b style="color:white">{p_mean:.3f}</b> &nbsp;|&nbsp; <b style="color:white">{4-consensus}/4</b> site-blind models agree</div>
+</div>"""
     else:
-        label = "Inconclusive"
-        status = "LOW CONFIDENCE — CLINICAL REVIEW RECOMMENDED"
-
-    gcn_out  = f"Prediction  : {label}\n"
-    gcn_out += f"Status      : {status}\n"
-    gcn_out += f"Confidence  : {conf:.1f}%   (p_ASD = {p_mean:.3f})\n"
-    gcn_out += f"Consensus   : {consensus}/4 site models\n\n"
-    gcn_out += "Per-model breakdown:\n"
-    for site, p in per_model:
-        lbl = "ASD" if p > 0.5 else "TC"
-        gcn_out += f"  {site:<6}  {lbl:<3}  p={p:.3f}\n"
-
-    asd_features = [
-        "Reduced DMN coherence (mPFC ↔ PCC)",
-        "Atypical salience network lateralization",
-        "Decreased long-range frontotemporal connectivity",
-        "Hypoconnectivity in social brain circuit (TPJ, STS)",
-        "Atypical cerebellar–cortical coupling",
-    ]
-    tc_features = [
-        "DMN coherence within normal range",
-        "Intact salience network organization",
-        "Normal long-range cortico-cortical connectivity",
-        "Typical social brain circuit integrity",
-        "Cerebellar–cortical coupling within expected range",
-    ]
-
-    report  = "## Clinical Connectivity Summary\n\n"
-    report += f"**Overall**: {label} ({conf:.1f}% confidence, {consensus}/4 site consensus)\n\n"
+        verdict = f"""<div style="background:#1a1a2e;border-left:6px solid #f4a261;padding:24px 28px;border-radius:12px;margin-bottom:8px">
+<div style="font-size:2rem;font-weight:800;color:#f4a261;letter-spacing:1px">INCONCLUSIVE</div>
+<div style="font-size:1.1rem;color:#aaa;margin-top:6px">Confidence: <b style="color:white">{conf:.1f}%</b> &nbsp;|&nbsp; p(ASD) = <b style="color:white">{p_mean:.3f}</b> &nbsp;|&nbsp; Model disagreement — clinical review required</div>
+</div>"""
 
+    # ── Site ensemble breakdown ──
+    rows = ""
+    for site, p in per_model:
+        lbl   = "ASD" if p > 0.5 else "TC"
+        color = "#e63946" if p > 0.5 else "#2dc653"
+        bar_w = int(p * 100)
+        rows += f"""<tr>
+<td style="padding:8px 12px;color:#ccc;font-weight:600">{site}-blind</td>
+<td style="padding:8px 12px"><div style="background:#333;border-radius:4px;height:18px;width:160px">
+<div style="background:{color};height:18px;width:{bar_w}%;border-radius:4px;opacity:0.85"></div></div></td>
+<td style="padding:8px 12px;color:{color};font-weight:700">{lbl}</td>
+<td style="padding:8px 12px;color:#888">p={p:.3f}</td>
+</tr>"""
+
+    ensemble = f"""<div style="background:#111;border-radius:10px;padding:20px;margin-top:4px">
+<div style="color:#888;font-size:0.8rem;text-transform:uppercase;letter-spacing:2px;margin-bottom:14px">Leave-One-Site-Out Ensemble — each model never trained on that site's data</div>
+<table style="width:100%;border-collapse:collapse">{rows}</table>
+<div style="margin-top:14px;color:#666;font-size:0.82rem">Cross-site consensus: {consensus}/4 models agree &nbsp;·&nbsp; LOSO AUC = 0.7872 across 529 held-out subjects</div>
+</div>"""
+
+    # ── Clinical report ──
     if p_mean > 0.6:
-        report += "**Key Findings**:\n"
-        for f in asd_features[:3]:
-            report += f"- {f}\n"
-        report += "\n**Cross-Site Consistency**: ASD-consistent patterns detected across "
-        report += f"{consensus}/4 independent scanner sites, indicating findings are not "
-        report += "attributable to acquisition-site artifacts.\n\n"
+        findings = [
+            "Reduced DMN coherence (mPFC ↔ PCC)",
+            "Atypical salience network lateralization",
+            "Decreased long-range frontotemporal connectivity",
+        ]
+        consistency = f"{consensus}/4 site-blind models flag ASD-consistent patterns — findings are not attributable to scanner artifacts."
+        impression = f"Connectivity profile consistent with ASD ({conf:.1f}% confidence)."
     elif p_mean < 0.4:
-        report += "**Key Findings**:\n"
-        for f in tc_features[:3]:
-            report += f"- {f}\n"
-        report += "\n**Cross-Site Consistency**: Typical connectivity profile confirmed "
-        report += f"by {4 - consensus}/4 independent site models.\n\n"
+        findings = [
+            "DMN coherence within normal range",
+            "Intact salience network organization",
+            "Normal long-range cortico-cortical connectivity",
+        ]
+        consistency = f"{4-consensus}/4 site-blind models confirm typical connectivity profile."
+        impression = f"Connectivity profile within typical range ({conf:.1f}% confidence)."
     else:
-        report += "**Inconclusive — Clinical Review Required**\n\n"
-        report += "Connectivity pattern falls near the ASD–Typical Control boundary. "
-        report += f"Model disagreement ({consensus}/4 site models predict ASD) indicates "
-        report += "insufficient confidence for an automated call.\n\n"
-        report += "**Recommended action**: Refer for full neuropsychological evaluation "
-        report += "(ADOS-2, ADI-R) and structural MRI review.\n\n"
-
-    report += "*This report is AI-assisted and does not constitute a diagnosis. "
-    report += "Full clinical assessment required.*\n\n"
-    report += "---\n*Clinical report generation powered by Qwen2.5-7B fine-tuned on AMD MI300X (coming soon)*"
-
-    return gcn_out, report
-
-
-# ── Gradio UI ──────────────────────────────────────────────────────────────
+        findings = [
+            "Mixed connectivity features near ASD–TC boundary",
+            "Model disagreement across scanner sites",
+            "Insufficient confidence for automated classification",
+        ]
+        consistency = f"Only {consensus}/4 models agree — borderline case requiring specialist input."
+        impression = "Inconclusive. Full neuropsychological evaluation recommended (ADOS-2, ADI-R)."
+
+    fi = "".join(f"<li style='margin:6px 0;color:#ccc'>{f}</li>" for f in findings)
+    report = f"""<div style="background:#111;border-radius:10px;padding:20px;margin-top:4px">
+<div style="color:#888;font-size:0.8rem;text-transform:uppercase;letter-spacing:2px;margin-bottom:14px">Clinical Connectivity Summary</div>
+<div style="color:#eee;font-size:1rem;margin-bottom:16px"><b>Impression:</b> {impression}</div>
+<div style="color:#aaa;font-size:0.9rem;margin-bottom:8px"><b style="color:#eee">Key Findings:</b></div>
+<ul style="margin:0 0 16px 0;padding-left:20px">{fi}</ul>
+<div style="color:#aaa;font-size:0.9rem;margin-bottom:16px"><b style="color:#eee">Cross-Site Consistency:</b> {consistency}</div>
+<div style="background:#1a1a1a;border-radius:6px;padding:12px;color:#666;font-size:0.8rem">
+⚕️ AI-assisted analysis only. Does not constitute a diagnosis. Integrate with clinical history, behavioral assessment, and standardized instruments.<br>
+<span style="color:#444;margin-top:6px;display:block">Clinical report generation: Qwen2.5-7B fine-tuned on AMD Instinct MI300X (coming soon)</span>
+</div></div>"""
+
+    return verdict, ensemble, report
+
+
+# ── UI ─────────────────────────────────────────────────────────────────────
+
+css = """
+body { background: #0d0d0d; }
+.gradio-container { max-width: 900px; margin: auto; }
+"""
 
-with gr.Blocks(title="BrainConnect-ASD") as demo:
-    gr.Markdown("""
-# BrainConnect-ASD
-### Scanner-site-invariant ASD detection from resting-state fMRI
+with gr.Blocks(title="BrainConnect-ASD", css=css, theme=gr.themes.Base()) as demo:
+    gr.HTML("""
+    <div style="text-align:center;padding:32px 0 16px">
+        <div style="font-size:2.2rem;font-weight:900;color:white;letter-spacing:-1px">BrainConnect<span style="color:#e63946">-ASD</span></div>
+        <div style="color:#888;font-size:1rem;margin-top:8px">Scanner-site-invariant ASD detection from resting-state fMRI</div>
+        <div style="display:flex;justify-content:center;gap:24px;margin-top:16px;flex-wrap:wrap">
+            <span style="background:#1a1a2e;color:#aaa;padding:6px 14px;border-radius:20px;font-size:0.85rem">LOSO AUC 0.7872</span>
+            <span style="background:#1a1a2e;color:#aaa;padding:6px 14px;border-radius:20px;font-size:0.85rem">529 held-out subjects</span>
+            <span style="background:#1a1a2e;color:#aaa;padding:6px 14px;border-radius:20px;font-size:0.85rem">4 independent institutions</span>
+            <span style="background:#1a1a2e;color:#aaa;padding:6px 14px;border-radius:20px;font-size:0.85rem">AMD Instinct MI300X</span>
+        </div>
+    </div>
+    """)
 
-Ensemble of **4 adversarial GCNs** trained with leave-one-site-out cross-validation on ABIDE I.
-Each model was held out from a different scanner site — the ensemble generalizes to **unseen institutions**.
+    file_input = gr.File(label="Upload CC200 fMRI file (.1D or .npz)", type="filepath")
 
-**LOSO AUC = 0.7872** across 529 held-out subjects from 4 independent institutions (NYU / USM / UCLA / UM).
+    verdict_html   = gr.HTML()
+    ensemble_html  = gr.HTML()
+    report_html    = gr.HTML()
 
-Fine-tuned **Qwen2.5-7B-Instruct** clinical report generation running on **AMD Instinct MI300X**.
-    """)
+    file_input.change(
+        fn=run_gcn,
+        inputs=file_input,
+        outputs=[verdict_html, ensemble_html, report_html],
+    )
 
-    with gr.Row():
-        file_input = gr.File(
-            label="Upload CC200 fMRI file (.1D or .npz)",
-            type="filepath",
-        )
-
-    with gr.Row():
-        gcn_out    = gr.Textbox(label="GCN Prediction", lines=10)
-        report_out = gr.Textbox(label="Clinical Report", lines=20)
-
-    file_input.change(fn=run_gcn, inputs=file_input, outputs=[gcn_out, report_out])
-
-    gr.Markdown("""
----
-**Model**: Adversarial Brain-Mode GCN (k=16 modes) with gradient reversal site deconfounding
-**Dataset**: ABIDE I (1,102 subjects, 17 acquisition sites)
-**Validation**: Leave-one-site-out across NYU (n=184), USM (n=101), UCLA (n=99), UM (n=145)
-**Hardware**: AMD Instinct MI300X via AMD Developer Cloud
-**Code**: [GitHub](https://github.com/Yatsuiii/Brain-Connectivity-GCN)
+    gr.HTML("""
+    <div style="text-align:center;padding:24px 0;color:#444;font-size:0.8rem">
+        Adversarial Brain-Mode GCN (k=16) · ABIDE I (1,102 subjects, 17 sites) ·
+        <a href="https://github.com/Yatsuiii/Brain-Connectivity-GCN" style="color:#666">GitHub</a>
+    </div>
     """)
 
 print("Preloading models...")