Force https root_url (custom-domain proxy drops x-forwarded-proto); harden count_crowd with cuda fallback + error handling; 60s GPU reservation

app.py

@@ -5,12 +5,30 @@ os.environ.setdefault("PYTORCH_ENABLE_MPS_FALLBACK", "1")
 
 import cv2
 import gradio as gr
+import gradio.route_utils
 import spaces
 import torch
 from PIL import Image
 
 import count
 
+# The crowd.skyebrowse.com proxy forwards x-forwarded-host but not
+# x-forwarded-proto, so Gradio resolves an http:// root and every asset/API
+# URL gets mixed-content blocked on the https page. Upgrade the scheme.
+_original_get_root_url = gradio.route_utils.get_root_url
+
+
+def _https_get_root_url(request, route_path, root_path):
+    root_url = _original_get_root_url(request, route_path, root_path)
+    if root_url.startswith("http://") and not any(
+        host in root_url for host in ("localhost", "127.0.0.1", "0.0.0.0")
+    ):
+        root_url = "https://" + root_url[len("http://"):]
+    return root_url
+
+
+gradio.route_utils.get_root_url = _https_get_root_url
+
 TILE = 768
 OVERLAP = 192
 UPSCALE = 1.0
@@ -33,7 +51,7 @@ def add_watermark(image):
     return image.convert("RGB")
 
 
-@spaces.GPU(duration=5)
+@spaces.GPU(duration=60)
 def count_crowd(image_path, threshold):
     if image_path is None:
         return None, "Upload an image first."
@@ -41,12 +59,25 @@ def count_crowd(image_path, threshold):
     if image_bgr is None:
         return None, "Could not read that image."
 
-    device = torch.device("cuda")
-    model = _model.to(device)
-    points = count.predict_points_tiled(model, image_bgr, device, UPSCALE, TILE, OVERLAP, float(threshold))
-    total = int(points.shape[0])
-    overlay = add_watermark(count.render_overlay(image_bgr, points, total))
-    return overlay, f"{total:,} people"
+    try:
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        model = _model.to(device)
+        with torch.inference_mode():
+            points = count.predict_points_tiled(
+                model,
+                image_bgr,
+                device,
+                UPSCALE,
+                TILE,
+                OVERLAP,
+                float(threshold),
+            )
+        total = int(points.shape[0])
+        overlay = add_watermark(count.render_overlay(image_bgr, points, total))
+        return overlay, f"{total:,} people"
+    except Exception as exc:
+        print(f"count_crowd failed: {exc}", flush=True)
+        return None, "Crowd counter failed. Please try a smaller image or try again in a minute."
 
 
 css = """
@@ -67,7 +98,7 @@ footer {
     max-width: 1100px !important;
 }
 .gradio-container a { text-decoration: none !important; }
-#count-box .output-class, #count-box .label-content, #count-box .text { color: #5A11FF !important; font-weight: 800 !important; }
+#count-box .output-class, #count-box .label-content, #count-box .text, #count-box textarea, #count-box input { color: #5A11FF !important; font-weight: 800 !important; }
 #count-box { color: #5A11FF !important; }
 .sponsor-banner {
     text-align: center;
@@ -209,7 +240,7 @@ with gr.Blocks(title="Crowd Counter | SkyeBrowse") as demo:
             threshold = gr.Slider(0.2, 0.6, value=0.35, step=0.05, label="Head-confidence threshold (lower = more dense-core recall)")
         with gr.Column(scale=1, min_width=300):
             overlay_output = gr.Image(type="pil", label="Counted overlay", interactive=False)
-            count_output = gr.Label(label="Estimated count", elem_id="count-box")
+            count_output = gr.Textbox(label="Estimated count", elem_id="count-box", interactive=False)
 
     inputs = [image_input, threshold]
     outputs = [overlay_output, count_output]