Update app.py

app.py

@@ -1,278 +1,3 @@
-# import gradio as gr
-# from gradio_bbox_annotator import BBoxAnnotator
-# from PIL import Image
-# import numpy as np
-# import torch
-# import os
-# import shutil
-# import subprocess
-# import time, json, uuid
-# from pathlib import Path
-# import tempfile
-# from inference import load_model, run
-# from skimage import measure
-# # === 图像处理依赖 ===
-# from scipy.ndimage import label
-# from matplotlib import cm
-# # ===== 清理缓存目录 =====
-# print("===== Space Usage =====")
-# subprocess.run("du -sh *", shell=True)
-# print("===== ~/.cache =====")
-# subprocess.run("ls -lh ~/.cache", shell=True)
-# cache_path = os.path.expanduser("~/.cache")
-# if os.path.exists(cache_path):
-#     shutil.rmtree(cache_path)
-#     print("✅ Deleted ~/.cache to free space.")
-
-# # ===== 模型初始化 =====
-# MODEL = None
-# DEVICE = torch.device("cpu")
-# CUDA_READY = False
-
-# def load_model_cpu():
-#     global MODEL, DEVICE
-#     MODEL, DEVICE = load_model(use_box=False)
-# load_model_cpu()
-
-# def prepare_cuda():
-#     global MODEL, DEVICE, CUDA_READY
-#     if torch.cuda.is_available() and not CUDA_READY:
-#         MODEL.to("cuda")
-#         DEVICE = torch.device("cuda")
-#         CUDA_READY = True
-#         _ = torch.zeros(1, device=DEVICE)
-
-# # ===== BBox 解析 =====
-# def parse_first_bbox(bboxes):
-#     if not bboxes:
-#         return None
-#     b = bboxes[0]
-#     if isinstance(b, dict):
-#         x, y = float(b.get("x", 0)), float(b.get("y", 0))
-#         w, h = float(b.get("width", 0)), float(b.get("height", 0))
-#         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
-
-# # ===== 保存用户反馈 =====
-# DATASET_DIR = Path("solver_cache")
-# DATASET_DIR.mkdir(parents=True, exist_ok=True)
-
-# def save_feedback(query_id, feedback_type, feedback_text=None, img_path=None, bboxes=None):
-#     feedback_data = {
-#         "query_id": query_id,
-#         "feedback_type": feedback_type,
-#         "feedback_text": feedback_text,
-#         "image": img_path,
-#         "bboxes": bboxes,
-#         "datetime": time.strftime("%Y%m%d_%H%M%S")
-#     }
-#     feedback_file = DATASET_DIR / query_id / "feedback.json"
-#     feedback_file.parent.mkdir(parents=True, exist_ok=True)
-#     if feedback_file.exists():
-#         with feedback_file.open("r") as f:
-#             existing = json.load(f)
-#             if not isinstance(existing, list):
-#                 existing = [existing]
-#             existing.append(feedback_data)
-#             feedback_data = existing
-#     else:
-#         feedback_data = [feedback_data]
-#     with feedback_file.open("w") as f:
-#         json.dump(feedback_data, f, indent=4, ensure_ascii=False)
-
-# # ===== 彩色 mask 可视化 =====
-# def colorize_mask(mask: np.ndarray, num_colors: int = 512) -> np.ndarray:
-#     mask = mask.astype(np.int32)
-
-#     def hsv_to_rgb(hh, ss, vv):
-#         i = int(hh * 6.0)
-#         f = hh * 6.0 - i
-#         p = vv * (1.0 - ss)
-#         q = vv * (1.0 - f * ss)
-#         t = vv * (1.0 - (1.0 - f) * ss)
-#         i = i % 6
-#         if i == 0: r, g, b = vv, t, p
-#         elif i == 1: r, g, b = q, vv, p
-#         elif i == 2: r, g, b = p, vv, t
-#         elif i == 3: r, g, b = p, q, vv
-#         elif i == 4: r, g, b = t, p, vv
-#         else: r, g, b = vv, p, q
-#         return int(r*255), int(g*255), int(b*255)
-
-#     palette = [(0, 0, 0)]
-#     for k in range(1, num_colors):
-#         hue = (k % num_colors) / float(num_colors)
-#         palette.append(hsv_to_rgb(hue, 1.0, 0.95))
-
-#     color_idx = mask % num_colors
-#     palette_arr = np.array(palette, dtype=np.uint8)
-#     return palette_arr[color_idx]
-
-# # ===== 推理 + 实例彩色可视化 =====
-# def segment_with_choice(use_box_choice, annot_value, mode="Overlay"):
-#     prepare_cuda()
-#     if annot_value is None or len(annot_value) < 1:
-#         print("❌ No annotation input")
-#         return None
-
-#     img_path = annot_value[0]
-#     bboxes = annot_value[1] if len(annot_value) > 1 else []
-
-#     print(f"🖼️ Image path: {img_path}")
-#     box_array = None
-#     if use_box_choice == "Yes" and bboxes:
-#         box = parse_first_bbox(bboxes)
-#         if box:
-#             xmin, ymin, xmax, ymax = map(int, box)
-#             box_array = [[xmin, ymin, xmax, ymax]]
-#             print(f"📦 Using box: {box_array}")
-
-#     try:
-#         mask = run(MODEL, img_path, box=box_array, device=DEVICE)
-#         print("📏 Mask shape:", mask.shape, "dtype:", mask.dtype, "unique:", np.unique(mask))
-#     except Exception as e:
-#         print(f"❌ Error during inference: {e}")
-#         return None
-
-#     try:
-#         img = Image.open(img_path)
-#         print("📷 Image mode:", img.mode, "size:", img.size)
-#     except Exception as e:
-#         print(f"❌ Failed to open image: {e}")
-#         return None
-
-#     try:
-#         img_rgb = img.convert("RGB").resize(mask.shape[::-1], resample=Image.BILINEAR)
-#         img_np = np.array(img_rgb, dtype=np.float32)
-#         if img_np.max() > 1.5:
-#             img_np = img_np / 255.0
-#     except Exception as e:
-#         print(f"❌ Error in image conversion/resizing: {e}")
-#         return None
-
-#     mask_np = np.array(mask)
-#     inst_mask = mask_np.astype(np.int32)
-#     unique_ids = np.unique(inst_mask)
-#     num_instances = len(unique_ids[unique_ids != 0])
-#     print(f"✅ Instance IDs found: {unique_ids}, Total instances: {num_instances}")
-
-#     if num_instances == 0:
-#         print("⚠️ No instance found, returning dummy red image")
-#         return Image.new("RGB", mask.shape[::-1], (255, 0, 0))
-
-#     # ==== Color Overlay (每个实例一个颜色) ====
-#     overlay = img_np.copy()
-#     alpha = 0.5
-#     cmap = cm.get_cmap("nipy_spectral", num_instances + 1)
-
-#     for inst_id in np.unique(inst_mask):
-#         if inst_id == 0:
-#             continue
-#         binary_mask = (inst_mask == inst_id).astype(np.uint8)
-#         color = np.array(cmap(inst_id / (num_instances + 1))[:3])  # RGB only, ignore alpha
-#         overlay[binary_mask == 1] = (1 - alpha) * overlay[binary_mask == 1] + alpha * color
-
-#         # 可选：绘制轮廓
-#         contours = measure.find_contours(binary_mask, 0.5)
-#         for contour in contours:
-#             contour = contour.astype(np.int32)
-#             overlay[contour[:, 0], contour[:, 1]] = [1.0, 1.0, 0.0]  # 黄色轮廓
-
-#     overlay = np.clip(overlay * 255.0, 0, 255).astype(np.uint8)
-
-#     if mode == "Instance Mask Only":
-#         return Image.fromarray(colorize_mask(inst_mask, num_colors=512))
-
-#     return Image.fromarray(overlay)
-
-# # ===== 示例图像 =====
-# example_data = [
-#     ("003_img.png", [(50, 60, 120, 150, "cell")]),
-#     ("1977_Well_F-5_Field_1.png", [(30, 40, 100, 130, "cell")]),
-# ]
-# gallery_images = [p for p, _ in example_data]
-
-# # ===== Gradio UI =====
-# with gr.Blocks(title="Microscopy Cell Segmentation") as demo:
-#     gr.Markdown("## 🧬 Microscopy Image Segmentation — One Cell, One Color")
-
-#     with gr.Row():
-#         with gr.Column(scale=1):
-#             annotator = BBoxAnnotator(label="🖼️ Upload & Annotate", categories=["cell"])
-
-#             example_gallery = gr.Gallery(
-#                 value=gallery_images,
-#                 label="📁 Example Inputs",
-#                 columns=[3], object_fit="cover", height=128
-#             )
-
-#             image_uploader = gr.Image(label="➕ Upload Image", type="filepath")
-
-#             run_btn = gr.Button("▶️ Run Segmentation")
-#             use_box_radio = gr.Radio(choices=["Yes", "No"], label="🔲 Use Bounding Box?", visible=False)
-#             confirm_btn = gr.Button("✅ Confirm", visible=False)
-#             mode_radio = gr.Radio(choices=["Overlay", "Instance Mask Only"], value="Overlay",
-#                                   label="🎨 Display Mode")
-
-#         with gr.Column(scale=2):
-#             image_output = gr.Image(type="pil", label="📸 Segmentation Result", height=400)
-#             score = gr.Slider(1, 5, step=1, value=3, label="🌟 Satisfaction (1–5)")
-#             comment_box = gr.Textbox(placeholder="Type your feedback...", lines=2, label="💬 Feedback")
-#             submit_score = gr.Button("💾 Submit Rating")
-
-#     user_uploaded_images = gr.State([])
-
-#     def add_uploaded_image(img_path, current_gallery):
-#         if not img_path:
-#             return current_gallery
-#         try:
-#             img = Image.open(img_path)
-#             img.thumbnail((128, 128))
-#             temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
-#             img.save(temp_file.name, format="PNG")
-#             thumb_path = temp_file.name
-#             if thumb_path not in current_gallery:
-#                 current_gallery.append(thumb_path)
-#         except Exception as e:
-#             print(f"❌ Failed image: {e}")
-#         return current_gallery
-
-#     image_uploader.upload(add_uploaded_image, [image_uploader, user_uploaded_images], [example_gallery, user_uploaded_images])
-
-#     def on_gallery_select(evt: gr.SelectData, gallery_images):
-#         index = evt.index
-#         if index < len(example_data):
-#             selected_path, selected_boxes = example_data[index]
-#             return selected_path, selected_boxes
-#         else:
-#             selected_path = gallery_images[index]
-#             return selected_path, []
-
-#     example_gallery.select(on_gallery_select, inputs=[user_uploaded_images], outputs=[annotator])
-
-#     def show_radio():
-#         return gr.update(visible=True), gr.update(visible=True)
-
-#     run_btn.click(fn=show_radio, outputs=[use_box_radio, confirm_btn])
-#     confirm_btn.click(fn=segment_with_choice,
-#                       inputs=[use_box_radio, annotator, mode_radio],
-#                       outputs=image_output)
-
-#     def handle_comment(comment, annot_value):
-#         save_feedback(time.strftime("%Y%m%d_%H%M%S") + "_" + str(uuid.uuid4())[:8], "comment", comment, annot_value[0], annot_value[1])
-#         return ""
-
-#     def handle_rating(score, annot_value):
-#         save_feedback(time.strftime("%Y%m%d_%H%M%S") + "_" + str(uuid.uuid4())[:8], "rating", f"Satisfaction Score: {score}", annot_value[0], annot_value[1])
-#         return 3
-
-#     comment_box.submit(fn=handle_comment, inputs=[comment_box, annotator], outputs=[comment_box])
-#     submit_score.click(fn=handle_rating, inputs=[score, annotator], outputs=[score])
-
-# if __name__ == "__main__":
-#     demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=True, show_error=True)
 import gradio as gr
 from gradio_bbox_annotator import BBoxAnnotator
 from PIL import Image
@@ -286,32 +11,24 @@ import json
 import uuid
 from pathlib import Path
 import tempfile
+import zipfile
 from skimage import measure
 from matplotlib import cm
 
-
 # ===== 导入三个推理模块 =====
 from inference_seg import load_model as load_seg_model, run as run_seg
 from inference_count import load_model as load_count_model, run as run_count
 from inference_track import load_model as load_track_model, run as run_track
-import subprocess
-
-print("\n===== 🔍 TOP 20 Disk Usage in your Space =====")
-subprocess.run("du -sh /* /home/* /home/user/* | sort -hr | head -n 20", shell=True)
-print("\n===== 🔍 Inside .cache =====")
-subprocess.run("du -sh ~/.cache/* | sort -hr | head -n 10", shell=True)
-print("\n===== 🔍 Inside current working dir =====")
-subprocess.run("du -sh ./* | sort -hr | head -n 10", shell=True)
 
 # ===== 清理缓存目录 =====
-print("===== Space Usage =====")
-subprocess.run("du -sh *", shell=True)
-print("===== ~/.cache =====")
-subprocess.run("ls -lh ~/.cache", shell=True)
+print("===== 清理缓存 =====")
 cache_path = os.path.expanduser("~/.cache")
 if os.path.exists(cache_path):
-    shutil.rmtree(cache_path)
-    print("✅ Deleted ~/.cache to free space.")
+    try:
+        shutil.rmtree(cache_path)
+        print("✅ Deleted ~/.cache to free space.")
+    except Exception as e:
+        print(f"⚠️ Could not delete cache: {e}")
 
 # ===== 全局模型变量 =====
 SEG_MODEL = None
@@ -354,9 +71,8 @@ def load_all_models():
 # 启动时加载所有模型
 load_all_models()
 
-# ===== 辅助函数 =====
+# ===== BBox 解析 =====
 def parse_first_bbox(bboxes):
-    """解析第一个边界框"""
     if not bboxes:
         return None
     b = bboxes[0]
@@ -368,8 +84,36 @@ def parse_first_bbox(bboxes):
         return float(b[0]), float(b[1]), float(b[2]), float(b[3])
     return None
 
+# ===== 保存用户反馈 =====
+DATASET_DIR = Path("solver_cache")
+DATASET_DIR.mkdir(parents=True, exist_ok=True)
+
+def save_feedback(query_id, feedback_type, feedback_text=None, img_path=None, bboxes=None):
+    """保存用户反馈到JSON文件"""
+    feedback_data = {
+        "query_id": query_id,
+        "feedback_type": feedback_type,
+        "feedback_text": feedback_text,
+        "image": img_path,
+        "bboxes": bboxes,
+        "datetime": time.strftime("%Y%m%d_%H%M%S")
+    }
+    feedback_file = DATASET_DIR / query_id / "feedback.json"
+    feedback_file.parent.mkdir(parents=True, exist_ok=True)
+    if feedback_file.exists():
+        with feedback_file.open("r") as f:
+            existing = json.load(f)
+            if not isinstance(existing, list):
+                existing = [existing]
+            existing.append(feedback_data)
+            feedback_data = existing
+    else:
+        feedback_data = [feedback_data]
+    with feedback_file.open("w") as f:
+        json.dump(feedback_data, f, indent=4, ensure_ascii=False)
+
+# ===== 彩色 mask 可视化 =====
 def colorize_mask(mask: np.ndarray, num_colors: int = 512) -> np.ndarray:
-    """将实例掩码转换为彩色图像"""
     mask = mask.astype(np.int32)
 
     def hsv_to_rgb(hh, ss, vv):
@@ -396,173 +140,117 @@ def colorize_mask(mask: np.ndarray, num_colors: int = 512) -> np.ndarray:
     palette_arr = np.array(palette, dtype=np.uint8)
     return palette_arr[color_idx]
 
-# ===== 分割功能 =====
-# ===== 彩色 mask 可视化 =====
-def colorize_mask(mask: np.ndarray, num_colors: int = 512) -> np.ndarray:
-    def hsv_to_rgb(h, s, v):
-        i = int(h * 6.0)
-        f = h * 6.0 - i
-        i = i % 6
-        p = v * (1 - s)
-        q = v * (1 - f * s)
-        t = v * (1 - (1 - f) * s)
-        if i == 0: r, g, b = v, t, p
-        elif i == 1: r, g, b = q, v, p
-        elif i == 2: r, g, b = p, v, t
-        elif i == 3: r, g, b = p, q, v
-        elif i == 4: r, g, b = t, p, v
-        else: r, g, b = v, p, q
-        return int(r * 255), int(g * 255), int(b * 255)
-
-    palette = [(0, 0, 0)]  # 背景为黑色
-    for i in range(1, num_colors):
-        h = (i % num_colors) / float(num_colors)
-        palette.append(hsv_to_rgb(h, 1.0, 0.95))
-
-    palette_arr = np.array(palette, dtype=np.uint8)
-    color_idx = mask % num_colors
-    return palette_arr[color_idx]
-
-def overlay_instances(img, mask, alpha=0.5, cmap_name="tab20"):
-    img = img.astype(np.float32)
-    if len(img.shape) == 2:
-        img = np.stack([img]*3, axis=-1)
-    if img.max() > 1.5:
-        img = img / 255.0
-
-    overlay = img.copy()
-    cmap = cm.get_cmap(cmap_name, np.max(mask) + 1)
-
-    for inst_id in np.unique(mask):
-        if inst_id == 0:
-            continue
-        color = np.array(cmap(inst_id)[:3])
-        overlay[mask == inst_id] = (1 - alpha) * overlay[mask == inst_id] + alpha * color
-
-    return overlay
 # ===== 推理 + 实例彩色可视化 =====
 def segment_with_choice(use_box_choice, annot_value, mode="Overlay"):
-    prepare_cuda()
     if annot_value is None or len(annot_value) < 1:
         print("❌ No annotation input")
-        return None, "请上传图像"
+        return None, "❌ 没有输入图像"
 
     img_path = annot_value[0]
     bboxes = annot_value[1] if len(annot_value) > 1 else []
 
-    print(f"🖼️ 图像路径: {img_path}")
+    print(f"🖼️ Image path: {img_path}")
     box_array = None
     if use_box_choice == "Yes" and bboxes:
         box = parse_first_bbox(bboxes)
         if box:
             xmin, ymin, xmax, ymax = map(int, box)
             box_array = [[xmin, ymin, xmax, ymax]]
-            print(f"📦 使用边界框: {box_array}")
+            print(f"📦 Using box: {box_array}")
 
-    # === Run model
     try:
-        mask = run(MODEL, img_path, box=box_array, device=DEVICE)
-        print("📏 mask shape:", mask.shape, "unique ids:", np.unique(mask))
+        mask = run_seg(SEG_MODEL, img_path, box=box_array, device=SEG_DEVICE)
+        print("📏 Mask shape:", mask.shape, "dtype:", mask.dtype, "unique:", np.unique(mask))
     except Exception as e:
+        print(f"❌ Error during inference: {e}")
         return None, f"❌ 推理失败: {str(e)}"
 
-    # === 读取原图
     try:
-        img = Image.open(img_path).convert("RGB").resize(mask.shape[::-1], resample=Image.BILINEAR)
-        img_np = np.array(img).astype(np.float32)
+        img = Image.open(img_path)
+        print("📷 Image mode:", img.mode, "size:", img.size)
+    except Exception as e:
+        print(f"❌ Failed to open image: {e}")
+        return None, f"❌ 无法打开图像: {str(e)}"
+
+    try:
+        img_rgb = img.convert("RGB").resize(mask.shape[::-1], resample=Image.BILINEAR)
+        img_np = np.array(img_rgb, dtype=np.float32)
         if img_np.max() > 1.5:
-            img_np /= 255.0
+            img_np = img_np / 255.0
     except Exception as e:
-        return None, f"❌ 图像读取失败: {str(e)}"
+        print(f"❌ Error in image conversion/resizing: {e}")
+        return None, f"❌ 图像转换失败: {str(e)}"
 
-    inst_mask = mask.astype(np.int32)
+    mask_np = np.array(mask)
+    inst_mask = mask_np.astype(np.int32)
     unique_ids = np.unique(inst_mask)
     num_instances = len(unique_ids[unique_ids != 0])
-    print(f"✅ 实例数量: {num_instances}")
+    print(f"✅ Instance IDs found: {unique_ids}, Total instances: {num_instances}")
 
     if num_instances == 0:
-        return Image.new("RGB", mask.shape[::-1], (255, 0, 0)), "⚠️ 未检测到实例"
-
-    # === 可视化：Overlay 模式
-    if mode == "Overlay":
-        overlay = overlay_instances(img_np, inst_mask, alpha=0.5, cmap_name="tab20")
-        overlay_img = Image.fromarray((overlay * 255).astype(np.uint8))
-        return overlay_img, f"✅ 检测到 {num_instances} 个细胞"
-
-    # === 可视化：纯彩色 mask
-    elif mode == "Instance Mask Only":
-        color_mask = colorize_mask(inst_mask, num_colors=512)
-        return Image.fromarray(color_mask), f"✅ 检测到 {num_instances} 个细胞"
-
-    return None, "❓ 无效显示模式"
-
-# ===== 计数功能 =====
-def count_cells_handler(image_path):
-    """计数处理函数"""
-    if image_path is None:
-        return None, "⚠️ 请先上传图像"
+        print("⚠️ No instance found, returning dummy red image")
+        return Image.new("RGB", mask.shape[::-1], (255, 0, 0)), "⚠️ 未检测到任何实例"
+
+    # ==== Color Overlay (每个实例一个颜色) ====
+    overlay = img_np.copy()
+    alpha = 0.5
+    cmap = cm.get_cmap("nipy_spectral", num_instances + 1)
+
+    for inst_id in np.unique(inst_mask):
+        if inst_id == 0:
+            continue
+        binary_mask = (inst_mask == inst_id).astype(np.uint8)
+        color = np.array(cmap(inst_id / (num_instances + 1))[:3])  # RGB only, ignore alpha
+        overlay[binary_mask == 1] = (1 - alpha) * overlay[binary_mask == 1] + alpha * color
+
+        # 可选:绘制轮廓
+        contours = measure.find_contours(binary_mask, 0.5)
+        for contour in contours:
+            contour = contour.astype(np.int32)
+            overlay[contour[:, 0], contour[:, 1]] = [1.0, 1.0, 0.0]  # 黄色轮廓
+
+    overlay = np.clip(overlay * 255.0, 0, 255).astype(np.uint8)
+
+    status_msg = f"✅ 分割完成! 检测到 {num_instances} 个实例"
     
-    if COUNT_MODEL is None:
-        return None, "❌ 计数模型未加载"
+    if mode == "Instance Mask Only":
+        return Image.fromarray(colorize_mask(inst_mask, num_colors=512)), status_msg
+
+    return Image.fromarray(overlay), status_msg
+
+# ===== Count Handler =====
+def count_cells_handler(input_image):
+    if input_image is None:
+        return None, "❌ 请先上传图像"
     
     try:
-        print(f"🔢 Counting - Image: {image_path}")
-        
-        result = run_count(
-            COUNT_MODEL,
-            image_path,
-            box=None,
-            device=COUNT_DEVICE,
-            visualize=True
-        )
-        
-        if 'error' in result:
-            return None, f"❌ 计数失败: {result['error']}"
-        
-        count = result['count']
-        viz_path = result['visualized_path']
-        result_text = f"✅ 检测到 {count:.1f} 个细胞"
-        
-        print(f"✅ Counting done - Count: {count:.1f}")
-        
-        return viz_path, result_text
-        
+        result_image, cell_count = run_count(COUNT_MODEL, input_image, device=COUNT_DEVICE)
+        status = f"✅ 计数完成! 检测到 {cell_count} 个细胞"
+        return result_image, status
     except Exception as e:
-        print(f"❌ Counting error: {e}")
         import traceback
         traceback.print_exc()
         return None, f"❌ 计数失败: {str(e)}"
 
-# ===== 跟踪功能 =====
-import zipfile
-import tempfile
-import shutil
-
+# ===== Track Handler =====
 def find_tif_dir(root_dir):
-    """递归查找第一个包含 .tif 文件的目录"""
-    for dirpath, _, filenames in os.walk(root_dir):
-        if any(f.lower().endswith('.tif') for f in filenames):
+    for dirpath, dirnames, filenames in os.walk(root_dir):
+        tif_files = [f for f in filenames if f.lower().endswith(('.tif', '.tiff'))]
+        if tif_files:
             return dirpath
     return None
 
 def track_video_handler(zip_file_obj):
-    """支持 ZIP 压缩包上传的 Tracking 处理函数"""
     if zip_file_obj is None:
-        return None, "⚠️ 请上传包含视频帧的压缩包 (.zip)"
-    
-    if TRACK_MODEL is None:
-        return None, "❌ 跟踪模型未加载"
+        return None, "❌ 请先上传 ZIP 文件"
     
     try:
-        # 创建临时目录
         temp_dir = tempfile.mkdtemp()
         print(f"📦 解压到临时目录: {temp_dir}")
 
-        # 解压 zip 文件
         with zipfile.ZipFile(zip_file_obj.name, 'r') as zip_ref:
             zip_ref.extractall(temp_dir)
 
-        # 自动查找含 .tif 的子目录
         tif_dir = find_tif_dir(temp_dir)
         if tif_dir is None:
             return None, f"❌ 跟踪失败: 解压后未找到任何 .tif 图像"
@@ -571,7 +259,7 @@ def track_video_handler(zip_file_obj):
 
         result = run_track(
             TRACK_MODEL,
-            video_dir=tif_dir,   # 使用真正含图像的目录
+            video_dir=tif_dir,
             box=None,
             device=TRACK_DEVICE,
             output_dir="tracked_results"
@@ -604,6 +292,14 @@ def track_video_handler(zip_file_obj):
         import traceback
         traceback.print_exc()
         return None, f"❌ 跟踪失败: {str(e)}"
+
+# ===== 示例图像数据 =====
+example_data = [
+    ("003_img.png", [(50, 60, 120, 150, "cell")]),
+    ("1977_Well_F-5_Field_1.png", [(30, 40, 100, 130, "cell")]),
+]
+gallery_images = [p for p, _ in example_data]
+
 # ===== Gradio UI =====
 with gr.Blocks(title="Microscopy Analysis Suite", theme=gr.themes.Soft()) as demo:
     gr.Markdown(
@@ -611,12 +307,16 @@ with gr.Blocks(title="Microscopy Analysis Suite", theme=gr.themes.Soft()) as dem
         # 🔬 显微图像分析工具套件
         
         支持三种分析模式:
-        - 🎨 **分割 (Segmentation)**: 实例分割，每个细胞不同颜色
+        - 🎨 **分割 (Segmentation)**: 实例分割,每个细胞不同颜色
         - 🔢 **计数 (Counting)**: 基于密度图的细胞计数
         - 🎬 **跟踪 (Tracking)**: 视频序列中的细胞运动跟踪
         """
     )
     
+    # 全局状态: 用于存储当前query_id和用户上传的示例图片
+    current_query_id = gr.State(str(uuid.uuid4()))
+    user_uploaded_images = gr.State([])
+    
     with gr.Tabs():
         # ===== Tab 1: Segmentation =====
         with gr.Tab("🎨 分割 (Segmentation)"):
@@ -629,6 +329,21 @@ with gr.Blocks(title="Microscopy Analysis Suite", theme=gr.themes.Soft()) as dem
                         categories=["cell"]
                     )
                     
+                    # 示例图片展示
+                    example_gallery = gr.Gallery(
+                        value=gallery_images,
+                        label="📁 示例图片",
+                        columns=[3],
+                        object_fit="cover",
+                        height=128
+                    )
+                    
+                    # 用户上传示例图片
+                    image_uploader = gr.Image(
+                        label="➕ 上传新示例图片到Gallery",
+                        type="filepath"
+                    )
+                    
                     with gr.Row():
                         use_box_radio = gr.Radio(
                             choices=["Yes", "No"],
@@ -646,7 +361,7 @@ with gr.Blocks(title="Microscopy Analysis Suite", theme=gr.themes.Soft()) as dem
                     gr.Markdown(
                         """
                         **使用说明:**
-                        1. 上传图像
+                        1. 上传图像或选择示例图片
                         2. (可选) 标注边界框并选择 "Yes"
                         3. 选择显示模式
                         4. 点击 "运行分割"
@@ -657,19 +372,106 @@ with gr.Blocks(title="Microscopy Analysis Suite", theme=gr.themes.Soft()) as dem
                     seg_output = gr.Image(
                         type="pil",
                         label="📸 分割结果",
-                        height=500
+                        height=400
                     )
                     seg_status = gr.Textbox(
                         label="📊 状态信息",
                         lines=2
                     )
+                    
+                    # 满意度评分
+                    score = gr.Slider(
+                        1, 5,
+                        step=1,
+                        value=3,
+                        label="🌟 满意度评分 (1-5)"
+                    )
+                    
+                    # 反馈文本框
+                    comment_box = gr.Textbox(
+                        placeholder="请输入您的反馈意见...",
+                        lines=2,
+                        label="💬 反馈意见"
+                    )
+                    
+                    # 提交评分按钮
+                    submit_score = gr.Button("💾 提交评分", variant="secondary")
+                    
+                    feedback_status = gr.Textbox(
+                        label="✅ 反馈提交状态",
+                        lines=1,
+                        visible=False
+                    )
             
-            # 绑定事件
+            # 绑定事件: 运行分割
             run_seg_btn.click(
                 fn=segment_with_choice,
                 inputs=[use_box_radio, annotator, mode_radio],
                 outputs=[seg_output, seg_status]
             )
+            
+            # 绑定事件: 上传示例图片到Gallery
+            def add_uploaded_image(img_path, current_gallery):
+                if not img_path:
+                    return current_gallery
+                try:
+                    img = Image.open(img_path)
+                    img.thumbnail((128, 128))
+                    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
+                    img.save(temp_file.name, format="PNG")
+                    thumb_path = temp_file.name
+                    if thumb_path not in current_gallery:
+                        current_gallery.append(thumb_path)
+                    return current_gallery
+                except Exception as e:
+                    print(f"❌ Failed to add image to gallery: {e}")
+                    return current_gallery
+            
+            image_uploader.change(
+                fn=add_uploaded_image,
+                inputs=[image_uploader, user_uploaded_images],
+                outputs=user_uploaded_images
+            ).then(
+                fn=lambda imgs: imgs,
+                inputs=user_uploaded_images,
+                outputs=example_gallery
+            )
+            
+            # 绑定事件: 点击Gallery图片加载到annotator
+            def load_example(evt: gr.SelectData, examples):
+                if evt.index is not None and evt.index < len(examples):
+                    img_path = examples[evt.index]
+                    return img_path
+                return None
+            
+            example_gallery.select(
+                fn=load_example,
+                inputs=user_uploaded_images,
+                outputs=annotator
+            )
+            
+            # 绑定事件: 提交评分
+            def submit_feedback(query_id, score_val, comment_text, annot_value):
+                try:
+                    img_path = annot_value[0] if annot_value and len(annot_value) > 0 else None
+                    bboxes = annot_value[1] if annot_value and len(annot_value) > 1 else []
+                    
+                    save_feedback(
+                        query_id=query_id,
+                        feedback_type=f"score_{int(score_val)}",
+                        feedback_text=comment_text,
+                        img_path=img_path,
+                        bboxes=bboxes
+                    )
+                    return "✅ 反馈已提交,感谢您的评价!", gr.update(visible=True)
+                except Exception as e:
+                    return f"❌ 提交失败: {str(e)}", gr.update(visible=True)
+            
+            submit_score.click(
+                fn=submit_feedback,
+                inputs=[current_query_id, score, comment_box, annotator],
+                outputs=[feedback_status, feedback_status]
+            )
         
         # ===== Tab 2: Counting =====
         with gr.Tab("🔢 计数 (Counting)"):
@@ -731,7 +533,7 @@ with gr.Blocks(title="Microscopy Analysis Suite", theme=gr.themes.Soft()) as dem
                         """
                         **使用说明:**
                         1. 上传包含视频帧序列的压缩包 `.zip`
-                        2. 压缩包应直接包含 `.tif` 格式图像，如 t000.tif, t001.tif, ...
+                        2. 压缩包应直接包含 `.tif` 格式图像,如 t000.tif, t001.tif, ...
                         3. 点击 "运行跟踪"
                         4. 结果将保存到 `tracked_results/` 目录
                         
@@ -755,13 +557,14 @@ with gr.Blocks(title="Microscopy Analysis Suite", theme=gr.themes.Soft()) as dem
                         interactive=False
                     )
             
-            # 绑定事件：上传zip → 解压 → Tracking
+            # 绑定事件:上传zip → 解压 → Tracking
             dummy_output = gr.Textbox(visible=False)
             track_btn.click(
-                fn=track_video_handler,               # 你刚才改好的函数
-                inputs=track_zip_upload,              # 文件上传
-                outputs=[dummy_output, track_output]          # 第二个是 Textbox 输出
+                fn=track_video_handler,
+                inputs=track_zip_upload,
+                outputs=[dummy_output, track_output]
             )
+    
     gr.Markdown(
         """
         ---