Refomatting by editor

app.py

@@ -223,51 +223,66 @@ FULL_CSS = CSS + title_css(TEXT_DARK, PRIMARY, PRIMARY_DARK, TEXT_LIGHT)
 # ───────────────────────────────
 # Torch / transforms
 # ───────────────────────────────
-_tx = transforms.Compose([
-    transforms.ToTensor(),
-    transforms.Normalize([0.5, 0.5, 0.5],[0.5, 0.5, 0.5]),
-])
-def get_edge_model(name:str)->torch.nn.Module:
+_tx = transforms.Compose(
+    [
+        transforms.ToTensor(),
+        transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
+    ]
+)
+
+
+def get_edge_model(name: str) -> torch.nn.Module:
     if name not in get_edge_model.cache:
-        mdl=torch.hub.load("otroshi/edgeface",name,source="github",pretrained=True).eval()
+        mdl = torch.hub.load(
+            "otroshi/edgeface", name, source="github", pretrained=True
+        ).eval()
         mdl.to("cuda" if torch.cuda.is_available() else "cpu")
-        get_edge_model.cache[name]=mdl
+        get_edge_model.cache[name] = mdl
     return get_edge_model.cache[name]
-get_edge_model.cache={}
+
+
+get_edge_model.cache = {}
+
 
 # ───────────────────────────────
 # Helpers
 # ───────────────────────────────
-def _as_rgb(path:Path)->np.ndarray:
-    return cv2.cvtColor(cv2.imread(str(path)),cv2.COLOR_BGR2RGB)
+def _as_rgb(path: Path) -> np.ndarray:
+    return cv2.cvtColor(cv2.imread(str(path)), cv2.COLOR_BGR2RGB)
 
-def badge(text:str,colour:str)->str:
+
+def badge(text: str, colour: str) -> str:
     return f'<div class="match-badge" style="background:{colour}22;color:{colour}">{text}</div>'
 
+
 # ───────────────────────────────
 # Face comparison
 # ───────────────────────────────
-def compare(img_left,img_right,variant):
-    crop_a,crop_b=align_crop(img_left),align_crop(img_right)
+def compare(img_left, img_right, variant):
+    crop_a, crop_b = align_crop(img_left), align_crop(img_right)
     if crop_a is None and crop_b is None:
-        return None,None,badge("No face detected","#DC2626")
+        return None, None, badge("No face detected", "#DC2626")
     if crop_a is None:
-        return None,None,badge("No face in A","#DC2626")
+        return None, None, badge("No face in A", "#DC2626")
     if crop_b is None:
-        return None,None,badge("No face in B","#DC2626")
-    mdl=get_edge_model(variant);dev=next(mdl.parameters()).device
+        return None, None, badge("No face in B", "#DC2626")
+    mdl = get_edge_model(variant)
+    dev = next(mdl.parameters()).device
     with torch.no_grad():
-        ea=mdl(_tx(cv2.cvtColor(crop_a,cv2.COLOR_RGB2BGR))[None].to(dev))[0]
-        eb=mdl(_tx(cv2.cvtColor(crop_b,cv2.COLOR_RGB2BGR))[None].to(dev))[0]
-    pct=float(F.cosine_similarity(ea[None],eb[None]).item()*100)
-    pct=max(0,min(100,pct))
-    colour="#15803D" if pct>=80 else "#CA8A04" if pct>=50 else "#DC2626"
-    return crop_a,crop_b,badge(f"{pct:.2f}% match",colour)
+        ea = mdl(_tx(cv2.cvtColor(crop_a, cv2.COLOR_RGB2BGR))[None].to(dev))[0]
+        eb = mdl(_tx(cv2.cvtColor(crop_b, cv2.COLOR_RGB2BGR))[None].to(dev))[0]
+    pct = float(F.cosine_similarity(ea[None], eb[None]).item() * 100)
+    pct = max(0, min(100, pct))
+    colour = "#15803D" if pct >= 80 else "#CA8A04" if pct >= 50 else "#DC2626"
+    return crop_a, crop_b, badge(f"{pct:.2f}% match", colour)
+
 
 # ───────────────────────────────
 # Static HTML
 # ───────────────────────────────
-TITLE_HTML = title_with_logo("""<span class="brand">EdgeFace:</span> Efficient Face Recognition Model for Edge Devices""")
+TITLE_HTML = title_with_logo(
+    """<span class="brand">EdgeFace:</span> Efficient Face Recognition Model for Edge Devices"""
+)
 
 # <div id="hero-badge">
 #   🏆 Winner of IJCB 2023 Efficient Face Recognition Competition
@@ -308,12 +323,22 @@ with gr.Blocks(css=FULL_CSS, title="EdgeFace Demo") as demo:
     gr.HTML(HERO_HTML)
 
     with gr.Row():
-        gal_a = gr.Gallery(PRELOADED, columns=[5], height=120,
-                           label="Image A", object_fit="contain",
-                           elem_classes="card")
-        gal_b = gr.Gallery(PRELOADED, columns=[5], height=120,
-                           label="Image B", object_fit="contain",
-                           elem_classes="card")
+        gal_a = gr.Gallery(
+            PRELOADED,
+            columns=[5],
+            height=120,
+            label="Image A",
+            object_fit="contain",
+            elem_classes="card",
+        )
+        gal_b = gr.Gallery(
+            PRELOADED,
+            columns=[5],
+            height=120,
+            label="Image B",
+            object_fit="contain",
+            elem_classes="card",
+        )
 
     with gr.Row():
         # img_a = gr.Image(type="numpy", height=300, label="Image A",
@@ -321,18 +346,30 @@ with gr.Blocks(css=FULL_CSS, title="EdgeFace Demo") as demo:
         # img_b = gr.Image(type="numpy", height=300, label="Image B",
         #                  elem_classes="preview")
 
-        img_a = gr.Image(type="numpy", height=300, label="Image A (click or drag-drop)",
-                 interactive=True, elem_classes="preview card")
-        img_b = gr.Image(type="numpy", height=300, label="Image B (click or drag-drop)",
-                        interactive=True, elem_classes="preview card")
+        img_a = gr.Image(
+            type="numpy",
+            height=300,
+            label="Image A (click or drag-drop)",
+            interactive=True,
+            elem_classes="preview card",
+        )
+        img_b = gr.Image(
+            type="numpy",
+            height=300,
+            label="Image B (click or drag-drop)",
+            interactive=True,
+            elem_classes="preview card",
+        )
 
     def _fill(evt: gr.SelectData):
         return _as_rgb(PRELOADED[evt.index]) if evt.index is not None else None
+
     gal_a.select(_fill, outputs=img_a)
     gal_b.select(_fill, outputs=img_b)
 
-    variant_dd = gr.Dropdown(EDGE_MODELS, value="edgeface_base",
-                             label="Model variant", elem_classes="card")
+    variant_dd = gr.Dropdown(
+        EDGE_MODELS, value="edgeface_base", label="Model variant", elem_classes="card"
+    )
     btn = gr.Button("Compare", variant="primary")
 
     with gr.Row():
@@ -340,8 +377,7 @@ with gr.Blocks(css=FULL_CSS, title="EdgeFace Demo") as demo:
         out_b = gr.Image(label="Aligned B (112×112)", elem_classes="card")
     score_html = gr.HTML(elem_id="score-area")
 
-    btn.click(compare, [img_a, img_b, variant_dd],
-              [out_a, out_b, score_html])
+    btn.click(compare, [img_a, img_b, variant_dd], [out_a, out_b, score_html])
 
     gr.HTML(CITATION_HTML)