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app.py

@@ -11,7 +11,7 @@ import numpy as np
 import torch
 import gradio as gr
 
-from _charts import VAL_B64, AUC_B64, AMD_BENCH_B64
+from _charts import VAL_B64, AUC_B64
 
 _WINDOW_LEN   = 50
 _STEP         = 3
@@ -378,24 +378,27 @@ def _saliency_figure(sal, p_mean, net_names=None, net_bounds=None, net_colors=No
         for s, e in zip(_nb[:-1], _nb[1:])
     ])
 
-    fig = plt.figure(figsize=(18, 5.5))
+    fig = plt.figure(figsize=(20, 18))
     fig.patch.set_facecolor("#0e1015")
+    import matplotlib.gridspec as gridspec
+    gs = gridspec.GridSpec(2, 2, figure=fig, hspace=0.38, wspace=0.32,
+                           height_ratios=[1.1, 1.0])
     axes = [
-        fig.add_subplot(1, 3, 1),
-        fig.add_subplot(1, 3, 2),
-        fig.add_subplot(1, 3, 3, projection="3d"),
+        fig.add_subplot(gs[0, 0]),           # heatmap
+        fig.add_subplot(gs[0, 1]),           # bar chart
+        fig.add_subplot(gs[1, :], projection="3d"),  # 3D brain — full bottom row
     ]
 
     # ── 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_title("FC Saliency by Brain Network", color="#bbb", fontsize=14, pad=16, 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.set_xticklabels(_nn, rotation=40, ha="right", fontsize=12, color="#ccc")
+    ax.set_yticklabels(_nn, fontsize=12, color="#ccc")
     ax.tick_params(colors="#555", length=0)
     for sp in ax.spines.values():
         sp.set_color("#222")
@@ -421,7 +424,7 @@ def _saliency_figure(sal, p_mean, net_names=None, net_bounds=None, net_colors=No
         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)
+                    fontsize=9, 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",
@@ -433,7 +436,7 @@ def _saliency_figure(sal, p_mean, net_names=None, net_bounds=None, net_colors=No
         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",
+                    fontsize=8.5, color="#fb923c", fontweight="600",
                     arrowprops=dict(arrowstyle="-", color="#fb923c",
                                    lw=0.7, connectionstyle="arc3,rad=0.1"),
                     ha="left", va="center", zorder=5)
@@ -441,7 +444,7 @@ def _saliency_figure(sal, p_mean, net_names=None, net_bounds=None, net_colors=No
     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)
+    cb.set_label("Mean |∂p(ASD)/∂FC|", color="#444", fontsize=10)
 
     # ── Right: network importance bar chart ────────────────────────────────
     ax2 = axes[1]
@@ -452,9 +455,9 @@ def _saliency_figure(sal, p_mean, net_names=None, net_bounds=None, net_colors=No
     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.set_yticklabels([_nn[i] for i in order], fontsize=12, color="#ddd")
+    ax2.set_xlabel("Mean gradient magnitude", color="#555", fontsize=11)
+    ax2.set_title("Network Importance for This Prediction", color="#bbb", fontsize=14, pad=16, fontweight="bold")
     ax2.invert_yaxis()
     for sp in ["top", "right"]:
         ax2.spines[sp].set_visible(False)
@@ -465,14 +468,14 @@ def _saliency_figure(sal, p_mean, net_names=None, net_bounds=None, net_colors=No
     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)
+                 f"{val:.4f}", va="center", color="#555", fontsize=10)
 
     # ── 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")
+    ax3.set_title("Top Connections · 3D Brain", color="#bbb", fontsize=14, pad=8, fontweight="bold")
 
     # Transparent brain ellipsoid wireframe (MNI space approx)
     u = np.linspace(0, 2 * np.pi, 32)
@@ -489,7 +492,7 @@ def _saliency_figure(sal, p_mean, net_names=None, net_bounds=None, net_colors=No
         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,
+        ax3.text(x, y, z + 7, name, fontsize=8, color=color,
                  ha="center", va="bottom", fontweight="600", zorder=6)
 
     # Draw top-5 inter-network connections as lines, thickness ∝ saliency
@@ -508,11 +511,10 @@ def _saliency_figure(sal, p_mean, net_names=None, net_bounds=None, net_colors=No
 
     fig.suptitle(
         f"Gradient Saliency  ·  p(ASD) = {p_mean:.3f}  ·  20-model LOSO ensemble  ·  CC200 → Yeo-7 networks",
-        color="#444", fontsize=8.5, y=1.02,
+        color="#888", fontsize=12, y=1.01,
     )
-    plt.tight_layout()
     buf = io.BytesIO()
-    plt.savefig(buf, format="png", dpi=140, bbox_inches="tight", facecolor="#0e1015")
+    plt.savefig(buf, format="png", dpi=120, bbox_inches="tight", facecolor="#0e1015")
     plt.close(fig)
     buf.seek(0)
     return Image.open(buf).copy()
@@ -1008,35 +1010,50 @@ ARCHITECTURE = """
 </div>
 """
 
-AMD = f"""
+AMD = """
 <div>
 
-  <!-- Benchmark chart first — most impressive thing -->
-  <img src="data:image/png;base64,{AMD_BENCH_B64}" style="width:100%;border-radius:8px;margin-bottom:20px;border:1px solid #252a35"/>
-
-  <!-- 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">30×</div><div style="color:#8b95a7;font-size:0.68rem;margin-top:3px;text-transform:uppercase;letter-spacing:0.8px">Faster than CPU</div></div>
-        <div><div style="font-size:1.5rem;font-weight:700;color:#fb923c">94ms</div><div style="color:#8b95a7;font-size:0.68rem;margin-top:3px;text-transform:uppercase;letter-spacing:0.8px">Per subject</div></div>
-      </div>
+  <!-- Stat grid: real benchmarked numbers -->
+  <div style="display:grid;grid-template-columns:repeat(4,1fr);gap:14px;margin-bottom:18px">
+    <div style="background:#161922;border:1px solid #252a35;border-radius:8px;padding:18px 16px;text-align:center">
+      <div style="font-size:1.8rem;font-weight:700;color:#fb923c;font-variant-numeric:tabular-nums">~20<span style="font-size:0.8rem;color:#5e6675;font-weight:400"> ms</span></div>
+      <div style="color:#8b95a7;font-size:0.67rem;margin-top:5px;text-transform:uppercase;letter-spacing:0.8px">End-to-end per patient</div>
+      <div style="color:#5e6675;font-size:0.64rem;margin-top:3px">preprocess + 20-model ensemble</div>
+    </div>
+    <div style="background:#161922;border:1px solid #252a35;border-radius:8px;padding:18px 16px;text-align:center">
+      <div style="font-size:1.8rem;font-weight:700;color:#fb923c">192<span style="font-size:0.8rem;color:#5e6675;font-weight:400"> GB</span></div>
+      <div style="color:#8b95a7;font-size:0.67rem;margin-top:5px;text-transform:uppercase;letter-spacing:0.8px">HBM3 unified memory</div>
+      <div style="color:#5e6675;font-size:0.64rem;margin-top:3px">7B model fits in bf16, no sharding</div>
+    </div>
+    <div style="background:#161922;border:1px solid #252a35;border-radius:8px;padding:18px 16px;text-align:center">
+      <div style="font-size:1.8rem;font-weight:700;color:#fb923c">60</div>
+      <div style="color:#8b95a7;font-size:0.67rem;margin-top:5px;text-transform:uppercase;letter-spacing:0.8px">Models trained on MI300X</div>
+      <div style="color:#5e6675;font-size:0.64rem;margin-top:3px">20 folds × 3 atlases · 150 epochs each</div>
     </div>
+    <div style="background:#161922;border:1px solid #252a35;border-radius:8px;padding:18px 16px;text-align:center">
+      <div style="font-size:1.8rem;font-weight:700;color:#fb923c">ROCm<span style="font-size:0.8rem;color:#5e6675;font-weight:400"> 7.0</span></div>
+      <div style="color:#8b95a7;font-size:0.67rem;margin-top:5px;text-transform:uppercase;letter-spacing:0.8px">Native PyTorch stack</div>
+      <div style="color:#5e6675;font-size:0.64rem;margin-top:3px">zero code changes from CUDA</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>
+  <!-- How AMD is used -->
+  <div style="background:#161922;border:1px solid #252a35;border-radius:8px;padding:18px 20px;margin-bottom:14px">
+    <div style="color:#8b95a7;font-size:0.68rem;text-transform:uppercase;letter-spacing:1.5px;margin-bottom:14px;font-weight:500">AMD MI300X Usage in This Project</div>
+    <div style="display:grid;grid-template-columns:1fr 1fr 1fr;gap:16px">
+      <div>
+        <div style="color:#fb923c;font-size:0.78rem;font-weight:600;margin-bottom:6px">① GCN Training</div>
+        <div style="color:#cbd5e1;font-size:0.82rem;line-height:1.55">60 site-holdout models trained via PyTorch Lightning on ROCm 7.0. K=32 modes, 150 epochs, 3 atlases (CC200, AAL, HO).</div>
+      </div>
+      <div>
+        <div style="color:#fb923c;font-size:0.78rem;font-weight:600;margin-bottom:6px">② LLM Fine-Tuning</div>
+        <div style="color:#cbd5e1;font-size:0.82rem;line-height:1.55">Qwen2.5-7B fine-tuned with LoRA (r=16, bf16) on 2K domain examples. 192GB HBM3 fits the full model without sharding.</div>
+      </div>
+      <div>
+        <div style="color:#fb923c;font-size:0.78rem;font-weight:600;margin-bottom:6px">③ Live LLM Inference</div>
+        <div style="color:#cbd5e1;font-size:0.82rem;line-height:1.55">Clinical reports generated in real-time via vLLM on the MI300X droplet. Every report you see is live AMD inference.</div>
       </div>
     </div>
-
   </div>
 
   <!-- Fine-tune spec table -->
@@ -1044,7 +1061,8 @@ AMD = f"""
     <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">GCN inference</td><td style="padding:10px 16px;color:#cbd5e1">~20ms end-to-end per patient · benchmarked on AMD MI300X · ROCm 7.0 · PyTorch 2.5.1</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">LLM serving</td><td style="padding:10px 16px;color:#cbd5e1">vLLM on AMD MI300X · OpenAI-compatible API · live inference for every clinical report</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>