MIST-only gallery with GT, inline styles, remove BCI references

app.py

@@ -244,23 +244,22 @@ def _make_gallery_label(key, entry):
 
 
 def show_gallery(display_name, gallery, name_map):
-    """Show a gallery example by its display name. Only model-generated outputs shown."""
+    """Show a gallery example by its display name."""
     key = name_map.get(display_name)
     if not key or not gallery or key not in gallery:
-        return None, None, None, None, None, ""
+        return None, None, None, None, None, None, ""
     entry = gallery[key]
     base = GALLERY_DIR / "images"
-    # Only show H&E input and model-generated stains (no ground truth — licensing)
     he = Image.open(base / entry["he"]).convert("RGB") if "he" in entry else None
+    gt = Image.open(base / entry["gt"]).convert("RGB") if "gt" in entry else None
     gen_her2 = Image.open(base / entry["gen_her2"]).convert("RGB") if "gen_her2" in entry else None
     gen_ki67 = Image.open(base / entry["gen_ki67"]).convert("RGB") if "gen_ki67" in entry else None
     gen_er = Image.open(base / entry["gen_er"]).convert("RGB") if "gen_er" in entry else None
     gen_pr = Image.open(base / entry["gen_pr"]).convert("RGB") if "gen_pr" in entry else None
 
-    source = entry.get("source", "Unknown")
     gt_stain = entry.get("gt_stain", "Unknown")
-    info = f"**Source:** {source} | **Original IHC stain:** {gt_stain}"
-    return he, gen_her2, gen_ki67, gen_er, gen_pr, info
+    info = f"**Ground truth stain:** {gt_stain}"
+    return he, gt, gen_her2, gen_ki67, gen_er, gen_pr, info
 
 
 # ── Build Gradio App ─────────────────────────────────────────────────
@@ -307,7 +306,7 @@ with gr.Blocks(title="UNIStainNet -- Virtual IHC Staining") as demo:
         else:
             gr.Markdown(
                 "Browse pre-computed virtual staining results -- **no GPU required**. "
-                "Each example shows the H&E input and all 4 generated IHC stains from our unified model."
+                "Each example shows the H&E input, ground truth IHC, and all 4 generated stains from our unified model."
             )
             gallery_dropdown = gr.Dropdown(
                 choices=gallery_display_names,
@@ -316,10 +315,12 @@ with gr.Blocks(title="UNIStainNet -- Virtual IHC Staining") as demo:
             )
             gallery_info_box = gr.Markdown(value="")
 
-            gr.Markdown("### H&E Input")
-            gal_he = gr.Image(type="pil", label="H&E Input", height=300)
+            gr.Markdown("### Input & Ground Truth")
+            with gr.Row():
+                gal_he = gr.Image(type="pil", label="H&E Input", height=300)
+                gal_gt = gr.Image(type="pil", label="Ground Truth IHC", height=300)
 
-            gr.Markdown("### Generated IHC Stains")
+            gr.Markdown("### Generated IHC Stains (all from the same H&E)")
             with gr.Row():
                 gal_her2 = gr.Image(type="pil", label="Generated HER2", height=280)
                 gal_ki67 = gr.Image(type="pil", label="Generated Ki67", height=280)
@@ -333,13 +334,13 @@ with gr.Blocks(title="UNIStainNet -- Virtual IHC Staining") as demo:
             gallery_dropdown.change(
                 fn=_show_gallery_wrapper,
                 inputs=[gallery_dropdown],
-                outputs=[gal_he, gal_her2, gal_ki67, gal_er, gal_pr, gallery_info_box],
+                outputs=[gal_he, gal_gt, gal_her2, gal_ki67, gal_er, gal_pr, gallery_info_box],
             )
 
             # Auto-load first example
             demo.load(
-                fn=lambda: _show_gallery_wrapper(gallery_display_names[0]) if gallery_display_names else (None,) * 6,
-                outputs=[gal_he, gal_her2, gal_ki67, gal_er, gal_pr, gallery_info_box],
+                fn=lambda: _show_gallery_wrapper(gallery_display_names[0]) if gallery_display_names else (None,) * 7,
+                outputs=[gal_he, gal_gt, gal_her2, gal_ki67, gal_er, gal_pr, gallery_info_box],
             )
 
     # ── Tab 2: Single Stain ──────────────────────────────────────

gallery/metadata.json

@@ -1,84 +1,4 @@
 {
-  "BCI_HER2_0_00198_test_0": {
-    "he": "BCI_HER2_0_00198_test_0_he.png",
-    "gt": "BCI_HER2_0_00198_test_0_gt.png",
-    "gt_stain": "HER2",
-    "source": "BCI",
-    "gen_her2": "BCI_HER2_0_00198_test_0_gen_her2.png",
-    "gen_ki67": "BCI_HER2_0_00198_test_0_gen_ki67.png",
-    "gen_er": "BCI_HER2_0_00198_test_0_gen_er.png",
-    "gen_pr": "BCI_HER2_0_00198_test_0_gen_pr.png"
-  },
-  "BCI_HER2_0_00013_test_0": {
-    "he": "BCI_HER2_0_00013_test_0_he.png",
-    "gt": "BCI_HER2_0_00013_test_0_gt.png",
-    "gt_stain": "HER2",
-    "source": "BCI",
-    "gen_her2": "BCI_HER2_0_00013_test_0_gen_her2.png",
-    "gen_ki67": "BCI_HER2_0_00013_test_0_gen_ki67.png",
-    "gen_er": "BCI_HER2_0_00013_test_0_gen_er.png",
-    "gen_pr": "BCI_HER2_0_00013_test_0_gen_pr.png"
-  },
-  "BCI_HER2_1+_00791_test_1+": {
-    "he": "BCI_HER2_1+_00791_test_1+_he.png",
-    "gt": "BCI_HER2_1+_00791_test_1+_gt.png",
-    "gt_stain": "HER2",
-    "source": "BCI",
-    "gen_her2": "BCI_HER2_1+_00791_test_1+_gen_her2.png",
-    "gen_ki67": "BCI_HER2_1+_00791_test_1+_gen_ki67.png",
-    "gen_er": "BCI_HER2_1+_00791_test_1+_gen_er.png",
-    "gen_pr": "BCI_HER2_1+_00791_test_1+_gen_pr.png"
-  },
-  "BCI_HER2_1+_00276_test_1+": {
-    "he": "BCI_HER2_1+_00276_test_1+_he.png",
-    "gt": "BCI_HER2_1+_00276_test_1+_gt.png",
-    "gt_stain": "HER2",
-    "source": "BCI",
-    "gen_her2": "BCI_HER2_1+_00276_test_1+_gen_her2.png",
-    "gen_ki67": "BCI_HER2_1+_00276_test_1+_gen_ki67.png",
-    "gen_er": "BCI_HER2_1+_00276_test_1+_gen_er.png",
-    "gen_pr": "BCI_HER2_1+_00276_test_1+_gen_pr.png"
-  },
-  "BCI_HER2_2+_00293_test_2+": {
-    "he": "BCI_HER2_2+_00293_test_2+_he.png",
-    "gt": "BCI_HER2_2+_00293_test_2+_gt.png",
-    "gt_stain": "HER2",
-    "source": "BCI",
-    "gen_her2": "BCI_HER2_2+_00293_test_2+_gen_her2.png",
-    "gen_ki67": "BCI_HER2_2+_00293_test_2+_gen_ki67.png",
-    "gen_er": "BCI_HER2_2+_00293_test_2+_gen_er.png",
-    "gen_pr": "BCI_HER2_2+_00293_test_2+_gen_pr.png"
-  },
-  "BCI_HER2_2+_00259_test_2+": {
-    "he": "BCI_HER2_2+_00259_test_2+_he.png",
-    "gt": "BCI_HER2_2+_00259_test_2+_gt.png",
-    "gt_stain": "HER2",
-    "source": "BCI",
-    "gen_her2": "BCI_HER2_2+_00259_test_2+_gen_her2.png",
-    "gen_ki67": "BCI_HER2_2+_00259_test_2+_gen_ki67.png",
-    "gen_er": "BCI_HER2_2+_00259_test_2+_gen_er.png",
-    "gen_pr": "BCI_HER2_2+_00259_test_2+_gen_pr.png"
-  },
-  "BCI_HER2_3+_00277_test_3+": {
-    "he": "BCI_HER2_3+_00277_test_3+_he.png",
-    "gt": "BCI_HER2_3+_00277_test_3+_gt.png",
-    "gt_stain": "HER2",
-    "source": "BCI",
-    "gen_her2": "BCI_HER2_3+_00277_test_3+_gen_her2.png",
-    "gen_ki67": "BCI_HER2_3+_00277_test_3+_gen_ki67.png",
-    "gen_er": "BCI_HER2_3+_00277_test_3+_gen_er.png",
-    "gen_pr": "BCI_HER2_3+_00277_test_3+_gen_pr.png"
-  },
-  "BCI_HER2_3+_00220_test_3+": {
-    "he": "BCI_HER2_3+_00220_test_3+_he.png",
-    "gt": "BCI_HER2_3+_00220_test_3+_gt.png",
-    "gt_stain": "HER2",
-    "source": "BCI",
-    "gen_her2": "BCI_HER2_3+_00220_test_3+_gen_her2.png",
-    "gen_ki67": "BCI_HER2_3+_00220_test_3+_gen_ki67.png",
-    "gen_er": "BCI_HER2_3+_00220_test_3+_gen_er.png",
-    "gen_pr": "BCI_HER2_3+_00220_test_3+_gen_pr.png"
-  },
   "MIST_HER2_67M2100642_15_18": {
     "he": "MIST_HER2_67M2100642_15_18_he.png",
     "gt": "MIST_HER2_67M2100642_15_18_gt.png",