Update app.py

app.py

@@ -1,96 +1,103 @@
-import gradio as gr
-from gradio_bbox_annotator import BBoxAnnotator
-from PIL import Image
-import numpy as np
-
-# 你已有的推理代码
-from inference import load_model, get_embedding, run
-
-# ---- 仅加载一次模型 ----
-model, device = load_model("medsam_vit_b.pth")
-def predict(value):
-    # value: (image_path, [(xmin, ymin, xmax, ymax, label), ...])
-    return value  # 直接回显
-
-def make_example(path):
-    return [path, []]
-
-def parse_first_bbox(bboxes):
-    """
-    从 annot 的 bboxes 里取第一个框，返回 (xmin, ymin, xmax, ymax)
-    兼容两种格式：
-    - dict: {"x":..,"y":..,"width":..,"height":..}
-    - list: [xmin, ymin, xmax, ymax, ...]
-    """
-    if not bboxes:
-        return None
-    b = bboxes[0]
-    if isinstance(b, dict):
-        x, y = float(b["x"]), float(b["y"])
-        w, h = float(b["width"]), float(b["height"])
-        return x, y, x + w, y + h
-    if isinstance(b, (list, tuple)) and len(b) >= 4:
-        return float(b[0]), float(b[1]), float(b[2]), float(b[3])
-    return None
-
-def segment(annot_value):
-    """
-    annot_value 形如 [image_path, bboxes]
-    - image_path: 字符串
-    - bboxes: 框列表
-    """
-    if annot_value is None or len(annot_value) < 1:
-        return None, "请先在上方上传图片并拖一个矩形框。"
-
-    img_path = annot_value[0]
-    bboxes = annot_value[1] if len(annot_value) > 1 else []
-
-    if not bboxes:
-        return None, "未检测到矩形框，请在标注区按住左键拖拽一个框。"
-
-    # 读取图片
-    img = Image.open(img_path).convert("RGB")
-    img_np = np.array(img)
-    H, W, _ = img_np.shape
-
-    # 取第一个框
-    box = parse_first_bbox(bboxes)
-    if box is None:
-        return None, "解析矩形框失败，请重画。"
-
-    xmin, ymin, xmax, ymax = box
-
-    # 归一化到 1024 并推理
-    box_np = np.array([[xmin, ymin, xmax, ymax]], dtype=float)
-    box_1024 = box_np / np.array([W, H, W, H]) * 1024.0
-
-    embedding = get_embedding(model, img_np, device)
-    mask = run(model, embedding, box_1024, H, W)  # (H, W) 0/1
-
-    # 黑白 mask（白=前景）
-    mask_rgb = np.stack([mask * 255] * 3, axis=-1).astype(np.uint8)
-    bbox_text = f"xmin={int(xmin)}, ymin={int(ymin)}, xmax={int(xmax)}, ymax={int(ymax)}"
-
-    return Image.fromarray(mask_rgb), bbox_text
-
-# --- 构造一个可用的示例值（让画布里有图可直接拖） ---
-example = ("003_img.png", [(50, 60, 120, 150, "cell")])
-
-demo = gr.Interface(
-    fn=segment,  # ← 调你的推理函数
-    inputs=BBoxAnnotator(
-        value=example,                 # 默认示例；组件里自带“上传”按钮，可以换图
-        categories=["cell", "nucleus"],
-        label="upload"
-    ),
-    outputs=[
-        gr.Image(type="pil", label="Mask result"),
-        gr.Textbox(label="location")
-    ],
-    examples=[[example]],
-    cache_examples=False
-)
-
-if __name__ == "__main__":
-    demo.launch(server_port=7860)
-
+import gradio as gr
+from gradio_bbox_annotator import BBoxAnnotator
+from PIL import Image
+import numpy as np
+
+# 你已有的推理代码
+from inference import load_model, get_embedding, run
+
+# ---- 仅加载一次模型 ----
+model, device = load_model("medsam_vit_b.pth")
+def predict(value):
+    # value: (image_path, [(xmin, ymin, xmax, ymax, label), ...])
+    return value  # 直接回显
+
+def make_example(path):
+    return [path, []]
+
+def parse_first_bbox(bboxes):
+    """
+    从 annot 的 bboxes 里取第一个框，返回 (xmin, ymin, xmax, ymax)
+    兼容两种格式：
+    - dict: {"x":..,"y":..,"width":..,"height":..}
+    - list: [xmin, ymin, xmax, ymax, ...]
+    """
+    if not bboxes:
+        return None
+    b = bboxes[0]
+    if isinstance(b, dict):
+        x, y = float(b["x"]), float(b["y"])
+        w, h = float(b["width"]), float(b["height"])
+        return x, y, x + w, y + h
+    if isinstance(b, (list, tuple)) and len(b) >= 4:
+        return float(b[0]), float(b[1]), float(b[2]), float(b[3])
+    return None
+
+def segment(annot_value):
+    """
+    annot_value 形如 [image_path, bboxes]
+    - image_path: 字符串
+    - bboxes: 框列表
+    """
+    if annot_value is None or len(annot_value) < 1:
+        return None, "请先在上方上传图片并拖一个矩形框。"
+
+    img_path = annot_value[0]
+    bboxes = annot_value[1] if len(annot_value) > 1 else []
+
+    if not bboxes:
+        return None, "未检测到矩形框，请在标注区按住左键拖拽一个框。"
+
+    # 读取图片
+    img = Image.open(img_path).convert("RGB")
+    img_np = np.array(img)
+    H, W, _ = img_np.shape
+
+    # 取第一个框
+    box = parse_first_bbox(bboxes)
+    if box is None:
+        return None, "解析矩形框失败，请重画。"
+
+    xmin, ymin, xmax, ymax = box
+
+    # 归一化到 1024 并推理
+    box_np = np.array([[xmin, ymin, xmax, ymax]], dtype=float)
+    box_1024 = box_np / np.array([W, H, W, H]) * 1024.0
+
+    embedding = get_embedding(model, img_np, device)
+    mask = run(model, embedding, box_1024, H, W)  # (H, W) 0/1
+
+    # 黑白 mask（白=前景）
+    mask_rgb = np.stack([mask * 255] * 3, axis=-1).astype(np.uint8)
+    bbox_text = f"xmin={int(xmin)}, ymin={int(ymin)}, xmax={int(xmax)}, ymax={int(ymax)}"
+
+    return Image.fromarray(mask_rgb), bbox_text
+
+# --- 构造一个可用的示例值（让画布里有图可直接拖） ---
+example = ("003_img.png", [(50, 60, 120, 150, "cell")])
+
+demo = gr.Interface(
+    fn=segment,  # ← 调你的推理函数
+    inputs=BBoxAnnotator(
+        value=example,                 # 默认示例；组件里自带“上传”按钮，可以换图
+        categories=["cell", "nucleus"],
+        label="upload"
+    ),
+    outputs=[
+        gr.Image(type="pil", label="Mask result"),
+        gr.Textbox(label="location")
+    ],
+    examples=[[example]],
+    cache_examples=False
+)
+
+if __name__ == "__main__":
+    demo.queue(concurrency_count=2).launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False,         # 不需要 public link，HF 会自动映射
+        show_error=True,
+        ssr_mode=False       # 关闭 SSR（这个是触发崩溃的常见元凶）
+    )
+
+