diff --git a/classifier/embedding_food_classifier_app.py b/classifier/embedding_food_classifier_app.py
new file mode 100644
index 0000000..ff5e715
--- /dev/null
+++ b/classifier/embedding_food_classifier_app.py
@@ -0,0 +1,849 @@
+import os
+import json
+import cv2
+import numpy as np
+import customtkinter as ctk
+from tkinter import filedialog, messagebox
+from PIL import Image, ImageTk
+import torch
+import torch.nn.functional as F
+from typing import List, Optional, Tuple
+from tkinterdnd2 import DND_FILES, TkinterDnD
+import threading
+import time
+import pickle
+import faiss
+from collections import Counter
+
+from net.resnet_embedding import create_resnet50_embedding
+from settings import settings
+
+# 设置customtkinter的外观
+ctk.set_appearance_mode("System")
+ctk.set_default_color_theme("blue")
+
+
+class EmbeddingFoodClassifierApp:
+    def __init__(self, root):
+        self.root = root
+        self.root.title("数字味道-食物识别系统 (Embedding版)")
+        self.root.geometry("1400x800")
+        
+        # 设备设置
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        print(f"使用设备: {self.device}")
+        
+        # 当前上传的图片列表
+        self.uploaded_images = []
+        self.current_results = []
+        
+        # 识别时间记录
+        self.recognition_start_time = None
+        self.recognition_duration = 0
+        
+        # 模型和FAISS索引相关
+        self.model = None
+        self.faiss_index = None
+        self.image_paths = []
+        self.labels = []
+        self.class_info = {}
+        self.class_names = []
+        self.class_to_idx = {}
+        self.idx_to_class = {}
+        
+        # 加载模型和索引
+        self.load_model_and_index()
+        
+        # 创建UI组件
+        self.create_widgets()
+        
+    def load_model_and_index(self):
+        """加载训练好的embedding模型和FAISS索引"""
+        try:
+            # 模型路径
+            model_path = "../model/embedding_20250917_145342/best_embedding_model.pth"
+            
+            # FAISS索引目录
+            index_dir = "../faiss_vector_db/faiss_index"
+            
+            if os.path.exists(model_path) and os.path.exists(index_dir):
+                # 1. 加载embedding模型
+                print("正在加载embedding模型...")
+                self.model = create_resnet50_embedding(
+                    embedding_dim=512,
+                    pretrained=True,
+                    use_internal_preprocess=False
+                )
+                
+                # 加载模型权重
+                checkpoint = torch.load(model_path, map_location=self.device)
+                if isinstance(checkpoint, dict):
+                    if 'model_state_dict' in checkpoint:
+                        self.model.load_state_dict(checkpoint['model_state_dict'])
+                    elif 'state_dict' in checkpoint:
+                        self.model.load_state_dict(checkpoint['state_dict'])
+                    else:
+                        self.model.load_state_dict(checkpoint)
+                else:
+                    self.model.load_state_dict(checkpoint)
+                
+                self.model.to(self.device)
+                self.model.eval()
+                print("Embedding模型加载成功!")
+                
+                # 2. 加载FAISS索引
+                print("正在加载FAISS索引...")
+                
+                # 加载索引文件
+                index_path = os.path.join(index_dir, 'faiss_index.bin')
+                self.faiss_index = faiss.read_index(index_path)
+                print(f"FAISS索引已加载: {self.faiss_index.ntotal} 个向量")
+                
+                # 加载图片路径映射
+                paths_path = os.path.join(index_dir, 'image_paths.pkl')
+                with open(paths_path, 'rb') as f:
+                    self.image_paths = pickle.load(f)
+                
+                # 加载标签映射
+                labels_path = os.path.join(index_dir, 'labels.pkl')
+                with open(labels_path, 'rb') as f:
+                    self.labels = pickle.load(f)
+                
+                # 加载类别信息
+                class_info_path = os.path.join(index_dir, 'class_info.json')
+                with open(class_info_path, 'r', encoding='utf-8') as f:
+                    self.class_info = json.load(f)
+                
+                self.class_names = self.class_info['class_names']
+                self.class_to_idx = self.class_info['class_to_idx']
+                self.idx_to_class = self.class_info['idx_to_class']
+                
+                print(f"索引元数据已加载: {len(self.image_paths)} 张图片, {len(self.class_names)} 个类别")
+                print(f"支持的食物类别: {self.class_names}")
+                
+            else:
+                print("模型文件或索引文件不存在，将使用模拟识别")
+                self.model = None
+                self.faiss_index = None
+                # 设置默认类别（用于模拟）
+                self.class_names = ['回锅肉', '炒细面', '西红柿鸡蛋', '麻辣小面']
+                
+        except Exception as e:
+            print(f"模型或索引加载失败: {e}")
+            self.model = None
+            self.faiss_index = None
+            # 设置默认类别（用于模拟）
+            self.class_names = ['回锅肉', '炒细面', '西红柿鸡蛋', '麻辣小面']
+    
+    def extract_true_class_from_path(self, file_path):
+        """从文件路径中提取真实类别（上一级目录名）"""
+        try:
+            # 标准化路径
+            normalized_path = os.path.normpath(file_path)
+            # 获取目录路径
+            dir_path = os.path.dirname(normalized_path)
+            # 获取上一级目录名（即类别名）
+            true_class = os.path.basename(dir_path)
+            
+            # 检查是否是已知的食物类别
+            if true_class in self.class_names:
+                return true_class
+            else:
+                # 如果不是已知类别，返回None表示未知
+                return None
+                
+        except Exception as e:
+            print(f"提取真实类别失败: {e}")
+            return None
+
+    def load_image_with_chinese_path(self, file_path):
+        """使用支持中文路径的方法加载图片"""
+        try:
+            # 方法1：使用numpy和cv2.imdecode处理中文路径
+            with open(file_path, 'rb') as f:
+                image_data = f.read()
+            
+            # 将字节数据转换为numpy数组
+            nparr = np.frombuffer(image_data, np.uint8)
+            
+            # 使用cv2.imdecode解码图片
+            image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+            
+            if image is not None:
+                return image
+            
+        except Exception as e:
+            print(f"加载图片失败: {e}")
+            return None
+    
+    def create_widgets(self):
+        """创建UI组件"""
+        # 主框架
+        self.main_frame = ctk.CTkFrame(self.root)
+        self.main_frame.pack(fill="both", expand=True, padx=15, pady=15)
+        
+        # 左侧框架 - 图片上传区域
+        self.left_frame = ctk.CTkFrame(self.main_frame, width=600)
+        self.left_frame.pack(side="left", fill="both", expand=True, padx=(0, 10), pady=0)
+        self.left_frame.pack_propagate(False)
+        
+        # 左侧标题
+        self.left_title = ctk.CTkLabel(
+            self.left_frame,
+            text="图片上传区域 (Embedding相似度检索)",
+            font=("Arial", 16, "bold")
+        )
+        self.left_title.pack(pady=(15, 10))
+        
+        # 拖拽上传区域
+        self.upload_frame = ctk.CTkFrame(self.left_frame, fg_color=("gray90", "gray20"))
+        self.upload_frame.pack(fill="x", padx=15, pady=(0, 10), ipady=50)
+        
+        # 拖拽提示标签
+        self.upload_label = ctk.CTkLabel(
+            self.upload_frame,
+            text="拖拽图片到这里\n或点击下方按钮选择图片\n支持多图片上传\n基于特征相似度识别",
+            font=("Arial", 14),
+            text_color=("gray40", "gray60")
+        )
+        self.upload_label.pack(expand=True)
+        
+        # 绑定拖放事件
+        self.upload_frame.drop_target_register(DND_FILES)
+        self.upload_frame.dnd_bind('<<Drop>>', self.handle_drop)
+        self.upload_frame.bind('<Enter>', self.on_drag_enter)
+        self.upload_frame.bind('<Leave>', self.on_drag_leave)
+        
+        # 按钮区域
+        self.button_frame = ctk.CTkFrame(self.left_frame)
+        self.button_frame.pack(fill="x", padx=15, pady=(0, 10))
+        
+        # 选择图片按钮
+        self.select_button = ctk.CTkButton(
+            self.button_frame,
+            text="选择图片",
+            command=self.select_images,
+            width=120,
+            height=35
+        )
+        self.select_button.pack(side="left", padx=(10, 5), pady=10)
+        
+        # 清空按钮
+        self.clear_button = ctk.CTkButton(
+            self.button_frame,
+            text="清空图片",
+            command=self.clear_images,
+            width=120,
+            height=35,
+            fg_color="gray",
+            hover_color="darkgray"
+        )
+        self.clear_button.pack(side="left", padx=5, pady=10)
+        
+        # 识别按钮
+        self.recognize_button = ctk.CTkButton(
+            self.button_frame,
+            text="开始识别",
+            command=self.start_recognition,
+            width=120,
+            height=35,
+            fg_color="green",
+            hover_color="darkgreen"
+        )
+        self.recognize_button.pack(side="right", padx=(5, 10), pady=10)
+        self.recognize_button.configure(state="disabled")
+        
+        # 已上传图片显示区域
+        self.images_display_frame = ctk.CTkScrollableFrame(
+            self.left_frame,
+            label_text="已上传的图片"
+        )
+        self.images_display_frame.pack(fill="both", expand=True, padx=15, pady=(0, 15))
+        
+        # 右侧框架 - 识别结果区域
+        self.right_frame = ctk.CTkFrame(self.main_frame, width=700)
+        self.right_frame.pack(side="right", fill="both", expand=True, padx=(10, 0), pady=0)
+        self.right_frame.pack_propagate(False)
+        
+        # 右侧标题
+        self.right_title = ctk.CTkLabel(
+            self.right_frame,
+            text="识别结果 (基于特征相似度)",
+            font=("Arial", 16, "bold")
+        )
+        self.right_title.pack(pady=(15, 10))
+        
+        # 统计信息框架
+        self.stats_frame = ctk.CTkFrame(self.right_frame)
+        self.stats_frame.pack(fill="x", padx=15, pady=(0, 10))
+        
+        # 统计标签
+        self.stats_label = ctk.CTkLabel(
+            self.stats_frame,
+            text="总图片: 0 | 已识别: 0 | 平均准确率: 0%",
+            font=("Arial", 12)
+        )
+        self.stats_label.pack(pady=10)
+        
+        # 识别结果显示区域
+        self.results_display_frame = ctk.CTkScrollableFrame(
+            self.right_frame,
+            label_text="识别详情 (相似度排序)"
+        )
+        self.results_display_frame.pack(fill="both", expand=True, padx=15, pady=(0, 15))
+        
+    def select_images(self):
+        """选择图片文件"""
+        file_paths = filedialog.askopenfilenames(
+            title="选择图片文件",
+            filetypes=[
+                ("图像文件", "*.jpg *.jpeg *.png *.bmp *.gif"),
+                ("JPEG文件", "*.jpg *.jpeg"),
+                ("PNG文件", "*.png"),
+                ("所有文件", "*.*")
+            ]
+        )
+        
+        if file_paths:
+            for file_path in file_paths:
+                self.add_image(file_path)
+    
+    def handle_drop(self, event):
+        """处理拖拽文件"""
+        files = event.data.split()
+        for file_path in files:
+            # 清理文件路径
+            file_path = file_path.strip('{}').strip('"')
+            file_path = os.path.normpath(file_path)
+            
+            # 检查是否为图片文件
+            valid_extensions = ('.jpg', '.jpeg', '.png', '.bmp', '.gif')
+            if file_path.lower().endswith(valid_extensions):
+                self.add_image(file_path)
+    
+    def on_drag_enter(self, event):
+        """拖拽进入时的视觉反馈"""
+        self.upload_frame.configure(fg_color=("gray80", "gray30"))
+        self.upload_label.configure(text="释放鼠标上传图片")
+    
+    def on_drag_leave(self, event):
+        """拖拽离开时恢复正常"""
+        self.upload_frame.configure(fg_color=("gray90", "gray20"))
+        self.upload_label.configure(text="拖拽图片到这里\n或点击下方按钮选择图片\n支持多图片上传\n基于特征相似度识别")
+    
+    def add_image(self, file_path):
+        """添加图片到上传列表"""
+        try:
+            # 检查文件是否存在
+            if not os.path.exists(file_path):
+                messagebox.showerror("错误", f"文件不存在: {file_path}")
+                return
+            
+            # 检查是否已经添加过
+            if file_path in [img['path'] for img in self.uploaded_images]:
+                messagebox.showinfo("提示", "该图片已经添加过了")
+                return
+            
+            # 使用支持中文路径的方法加载图片
+            image = self.load_image_with_chinese_path(file_path)
+            if image is None:
+                messagebox.showerror("错误", f"无法读取图片: {file_path}")
+                return
+            
+            # 从文件路径中提取真实类别（上一级目录名）
+            true_class = self.extract_true_class_from_path(file_path)
+            
+            # 添加到列表
+            image_info = {
+                'path': file_path,
+                'name': os.path.basename(file_path),
+                'image': image,
+                'true_class': true_class,
+                'recognized': False,
+                'result': None
+            }
+            self.uploaded_images.append(image_info)
+            
+            # 更新显示
+            self.update_images_display()
+            self.update_recognize_button()
+            
+        except Exception as e:
+            messagebox.showerror("错误", f"添加图片时出错: {str(e)}")
+    
+    def update_images_display(self):
+        """更新已上传图片的显示"""
+        # 清空当前显示
+        for widget in self.images_display_frame.winfo_children():
+            widget.destroy()
+        
+        # 显示每张图片
+        for i, img_info in enumerate(self.uploaded_images):
+            # 创建图片框架
+            img_frame = ctk.CTkFrame(self.images_display_frame)
+            img_frame.pack(fill="x", padx=5, pady=5)
+            
+            # 缩放图片用于显示
+            display_image = self.resize_image_for_display(img_info['image'], 100, 100)
+            display_image = cv2.cvtColor(display_image, cv2.COLOR_BGR2RGB)
+            pil_image = Image.fromarray(display_image)
+            tk_image = ImageTk.PhotoImage(pil_image)
+            
+            # 图片标签（可点击预览）
+            img_label = ctk.CTkLabel(img_frame, image=tk_image, text="")
+            img_label.image = tk_image  # 保持引用
+            img_label.pack(side="left", padx=10, pady=10)
+            img_label.bind("<Button-1>", lambda e, idx=i: self.preview_image(idx))
+            
+            # 信息框架
+            info_frame = ctk.CTkFrame(img_frame)
+            info_frame.pack(side="left", fill="both", expand=True, padx=10, pady=10)
+            
+            # 文件名
+            name_label = ctk.CTkLabel(
+                info_frame,
+                text=f"文件名: {img_info['name']}",
+                anchor="w"
+            )
+            name_label.pack(fill="x", padx=5, pady=2)
+            
+            # 状态和真实类别
+            if img_info['recognized'] and img_info.get('result'):
+                result = img_info['result']
+                is_correct = result.get('is_correct')
+                if is_correct is True:
+                    status = "已识别 ✓"
+                    status_color = "green"
+                elif is_correct is False:
+                    status = "已识别 ✗"
+                    status_color = "red"
+                else:
+                    status = "已识别 ?"
+                    status_color = "orange"
+            else:
+                status = "未识别"
+                status_color = None
+            
+            status_label = ctk.CTkLabel(
+                info_frame,
+                text=f"状态: {status}",
+                anchor="w",
+                text_color=status_color
+            )
+            status_label.pack(fill="x", padx=5, pady=2)
+            
+            # 显示真实类别（如果有）
+            true_class = img_info.get('true_class')
+            if true_class:
+                true_class_label = ctk.CTkLabel(
+                    info_frame,
+                    text=f"真实类别: {true_class}",
+                    anchor="w",
+                    font=("Arial", 10)
+                )
+                true_class_label.pack(fill="x", padx=5, pady=1)
+            
+            # 删除按钮
+            delete_button = ctk.CTkButton(
+                img_frame,
+                text="删除",
+                command=lambda idx=i: self.remove_image(idx),
+                width=60,
+                height=30,
+                fg_color="red",
+                hover_color="darkred"
+            )
+            delete_button.pack(side="right", padx=10, pady=10)
+    
+    def preview_image(self, index):
+        """预览图片"""
+        if index >= len(self.uploaded_images):
+            return
+        
+        img_info = self.uploaded_images[index]
+        
+        # 创建预览窗口
+        preview_window = ctk.CTkToplevel(self.root)
+        preview_window.title(f"预览 - {img_info['name']}")
+        preview_window.geometry("800x600")
+        
+        # 设置窗口属性，确保在主窗口上方
+        preview_window.transient(self.root)  # 设置为主窗口的子窗口
+        preview_window.grab_set()  # 设置为模态窗口
+        preview_window.lift()  # 提升到最前面
+        preview_window.focus_set()  # 设置焦点
+        
+        # 居中显示
+        preview_window.update_idletasks()
+        x = (preview_window.winfo_screenwidth() // 2) - (800 // 2)
+        y = (preview_window.winfo_screenheight() // 2) - (600 // 2)
+        preview_window.geometry(f"800x600+{x}+{y}")
+        
+        # 显示图片
+        display_image = self.resize_image_for_display(img_info['image'], 750, 550)
+        display_image = cv2.cvtColor(display_image, cv2.COLOR_BGR2RGB)
+        pil_image = Image.fromarray(display_image)
+        tk_image = ImageTk.PhotoImage(pil_image)
+        
+        img_label = ctk.CTkLabel(preview_window, image=tk_image, text="")
+        img_label.image = tk_image
+        img_label.pack(expand=True, padx=20, pady=20)
+    
+    def remove_image(self, index):
+        """删除图片"""
+        if index < len(self.uploaded_images):
+            self.uploaded_images.pop(index)
+            self.update_images_display()
+            self.update_recognize_button()
+            self.update_results_display()
+    
+    def clear_images(self):
+        """清空所有图片"""
+        if self.uploaded_images:
+            result = messagebox.askyesno("确认", "确定要清空所有图片吗？")
+            if result:
+                self.uploaded_images.clear()
+                self.current_results.clear()
+                # 重置识别时间
+                self.recognition_start_time = None
+                self.recognition_duration = 0
+                self.update_images_display()
+                self.update_recognize_button()
+                self.update_results_display()
+                self.update_stats()
+    
+    def update_recognize_button(self):
+        """更新识别按钮状态"""
+        if self.uploaded_images:
+            self.recognize_button.configure(state="normal")
+        else:
+            self.recognize_button.configure(state="disabled")
+    
+    def start_recognition(self):
+        """开始识别"""
+        if not self.uploaded_images:
+            messagebox.showinfo("提示", "请先上传图片")
+            return
+        
+        # 记录识别开始时间
+        self.recognition_start_time = time.time()
+        
+        # 在新线程中执行识别，避免界面卡顿
+        self.recognize_button.configure(state="disabled", text="识别中...")
+        threading.Thread(target=self.recognize_images, daemon=True).start()
+    
+    def recognize_images(self):
+        """识别所有图片"""
+        try:
+            self.current_results.clear()
+            
+            for i, img_info in enumerate(self.uploaded_images):
+                # 使用embedding相似度识别
+                if self.model is not None and self.faiss_index is not None:
+                    # 使用真实的embedding模型和FAISS索引
+                    predicted_class, confidence, similar_images = self.predict_with_embedding(img_info['image'])
+                else:
+                    # 模拟预测结果
+                    predicted_class = np.random.choice(self.class_names)
+                    confidence = np.random.uniform(0.6, 0.95)
+                    similar_images = []
+                
+                # 自动判断识别是否正确
+                true_class = img_info.get('true_class')
+                is_correct = None
+                if true_class is not None:
+                    is_correct = (predicted_class == true_class)
+                
+                # 保存结果
+                result = {
+                    'image_index': i,
+                    'image_name': img_info['name'],
+                    'predicted_class': predicted_class,
+                    'confidence': confidence,
+                    'true_class': true_class,
+                    'is_correct': is_correct,
+                    'similar_images': similar_images  # 相似图片列表
+                }
+                
+                self.current_results.append(result)
+                img_info['recognized'] = True
+                img_info['result'] = result
+                
+                # 更新UI（在主线程中）
+                self.root.after(0, self.update_progress, i + 1, len(self.uploaded_images))
+            
+            # 识别完成，更新UI
+            self.root.after(0, self.recognition_completed)
+            
+        except Exception as e:
+            self.root.after(0, lambda: messagebox.showerror("错误", f"识别过程中出错: {str(e)}"))
+            self.root.after(0, self.recognition_completed)
+    
+    def predict_with_embedding(self, image, k=5):
+        """使用embedding模型和FAISS索引进行预测"""
+        try:
+            # 将OpenCV图像转换为PIL图像
+            image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            pil_image = Image.fromarray(image_rgb)
+            
+            # 提取查询图片的特征向量
+            query_embedding = self.model.extract_embedding(pil_image, normalize=True)
+            query_embedding = query_embedding.reshape(1, -1).astype(np.float32)
+            
+            # 在FAISS索引中搜索最相似的k张图片
+            scores, indices = self.faiss_index.search(query_embedding, k)
+            
+            # 收集相似图片的类别
+            similar_classes = []
+            similar_images = []
+            
+            for i in range(k):
+                if i < len(indices[0]) and indices[0][i] < len(self.labels):
+                    idx = indices[0][i]
+                    score = scores[0][i]
+                    class_idx = self.labels[idx]
+                    class_name = self.class_names[class_idx]
+                    image_path = self.image_paths[idx]
+                    
+                    similar_classes.append(class_name)
+                    similar_images.append({
+                        'path': image_path,
+                        'class': class_name,
+                        'score': float(score)
+                    })
+            
+            # 使用投票机制确定最终预测类别
+            if similar_classes:
+                class_counts = Counter(similar_classes)
+                predicted_class = class_counts.most_common(1)[0][0]
+                
+                # 计算置信度（基于最高相似度分数和投票比例）
+                max_score = float(scores[0][0]) if len(scores[0]) > 0 else 0.0
+                vote_ratio = class_counts[predicted_class] / len(similar_classes)
+                confidence = max_score * vote_ratio
+                
+                return predicted_class, confidence, similar_images
+            else:
+                # 如果没有找到相似图片，随机选择一个类别
+                predicted_class = np.random.choice(self.class_names)
+                confidence = 0.1
+                return predicted_class, confidence, []
+                
+        except Exception as e:
+            print(f"Embedding预测出错: {e}")
+            # 返回随机结果作为备选
+            predicted_class = np.random.choice(self.class_names)
+            confidence = np.random.uniform(0.1, 0.3)
+            return predicted_class, confidence, []
+    
+    def update_progress(self, current, total):
+        """更新识别进度"""
+        self.recognize_button.configure(text=f"识别中... ({current}/{total})")
+        self.update_images_display()
+        self.update_results_display()
+    
+    def recognition_completed(self):
+        """识别完成"""
+        # 计算识别耗时
+        if self.recognition_start_time is not None:
+            self.recognition_duration = time.time() - self.recognition_start_time
+        
+        self.recognize_button.configure(state="normal", text="开始识别")
+        self.update_stats()
+        messagebox.showinfo("完成", f"所有图片识别完成！识别耗时: {self.recognition_duration:.2f}秒")
+    
+    def resize_image_for_display(self, image, max_width, max_height):
+        """调整图片大小用于显示"""
+        height, width = image.shape[:2]
+        scale = min(max_width / width, max_height / height)
+        
+        if scale < 1:
+            new_width = int(width * scale)
+            new_height = int(height * scale)
+            return cv2.resize(image, (new_width, new_height))
+        
+        return image
+    
+    def update_results_display(self):
+        """更新识别结果显示"""
+        # 清空当前显示
+        for widget in self.results_display_frame.winfo_children():
+            widget.destroy()
+        
+        if not self.current_results:
+            no_result_label = ctk.CTkLabel(
+                self.results_display_frame,
+                text="暂无识别结果",
+                font=("Arial", 14),
+                text_color="gray"
+            )
+            no_result_label.pack(pady=20)
+            return
+        
+        # 显示每个识别结果
+        for i, result in enumerate(self.current_results):
+            # 结果框架
+            result_frame = ctk.CTkFrame(self.results_display_frame)
+            result_frame.pack(fill="x", padx=5, pady=5)
+            
+            # 获取原始图片
+            img_info = self.uploaded_images[result['image_index']]
+            display_image = self.resize_image_for_display(img_info['image'], 120, 120)
+            display_image = cv2.cvtColor(display_image, cv2.COLOR_BGR2RGB)
+            pil_image = Image.fromarray(display_image)
+            tk_image = ImageTk.PhotoImage(pil_image)
+            
+            # 图片标签
+            img_label = ctk.CTkLabel(result_frame, image=tk_image, text="")
+            img_label.image = tk_image
+            img_label.pack(side="left", padx=10, pady=10)
+            img_label.bind("<Button-1>", lambda e, idx=result['image_index']: self.preview_image(idx))
+            
+            # 信息框架
+            info_frame = ctk.CTkFrame(result_frame)
+            info_frame.pack(side="left", fill="both", expand=True, padx=10, pady=10)
+            
+            # 文件名
+            name_label = ctk.CTkLabel(
+                info_frame,
+                text=f"文件: {result['image_name']}",
+                anchor="w",
+                font=("Arial", 12, "bold")
+            )
+            name_label.pack(fill="x", padx=5, pady=2)
+            
+            # 识别结果标题
+            result_title_label = ctk.CTkLabel(
+                info_frame,
+                text="识别结果 (基于相似度):",
+                anchor="w",
+                font=("Arial", 11)
+            )
+            result_title_label.pack(fill="x", padx=5, pady=(2, 0))
+            
+            # 识别结果内容（大字体、加粗，颜色根据正确性决定）
+            is_correct = result.get('is_correct')
+            if is_correct is True:
+                result_color = "green"  # 识别正确显示绿色
+            elif is_correct is False:
+                result_color = "red"    # 识别错误显示红色
+            else:
+                result_color = "orange" # 无法判断显示橙色
+            
+            result_content_label = ctk.CTkLabel(
+                info_frame,
+                text=result['predicted_class'],
+                anchor="w",
+                font=("Arial", 18, "bold"),
+                text_color=result_color
+            )
+            result_content_label.pack(fill="x", padx=5, pady=(0, 2))
+            
+            # 置信度
+            confidence_label = ctk.CTkLabel(
+                info_frame,
+                text=f"相似度得分: {result['confidence']:.3f}",
+                anchor="w",
+                font=("Arial", 11)
+            )
+            confidence_label.pack(fill="x", padx=5, pady=2)
+            
+            # 真实类别
+            true_class = result.get('true_class')
+            if true_class is not None:
+                true_class_label = ctk.CTkLabel(
+                    info_frame,
+                    text=f"真实类别: {true_class}",
+                    anchor="w",
+                    font=("Arial", 11)
+                )
+                true_class_label.pack(fill="x", padx=5, pady=2)
+            
+            # 自动判断结果
+            is_correct = result.get('is_correct')
+            if is_correct is not None:
+                if is_correct:
+                    status_text = "✓ 识别正确"
+                    status_color = "green"
+                else:
+                    status_text = "✗ 识别错误"
+                    status_color = "red"
+                
+                status_label = ctk.CTkLabel(
+                    info_frame,
+                    text=status_text,
+                    anchor="w",
+                    font=("Arial", 12, "bold"),
+                    text_color=status_color
+                )
+                status_label.pack(fill="x", padx=5, pady=5)
+            else:
+                # 如果无法自动判断，显示未知状态
+                status_label = ctk.CTkLabel(
+                    info_frame,
+                    text="? 无法自动判断（路径中未包含已知类别）",
+                    anchor="w",
+                    font=("Arial", 11),
+                    text_color="orange"
+                )
+                status_label.pack(fill="x", padx=5, pady=5)
+            
+            # 显示相似图片信息（如果有）
+            similar_images = result.get('similar_images', [])
+            if similar_images:
+                similar_label = ctk.CTkLabel(
+                    info_frame,
+                    text=f"基于前{len(similar_images)}张最相似图片的投票结果",
+                    anchor="w",
+                    font=("Arial", 10),
+                    text_color="gray"
+                )
+                similar_label.pack(fill="x", padx=5, pady=(5, 2))
+                
+                # 显示前3张最相似的图片信息
+                for j, sim_img in enumerate(similar_images[:3]):
+                    sim_info = f"  {j+1}. {sim_img['class']} (相似度: {sim_img['score']:.3f})"
+                    sim_info_label = ctk.CTkLabel(
+                        info_frame,
+                        text=sim_info,
+                        anchor="w",
+                        font=("Arial", 9),
+                        text_color="gray"
+                    )
+                    sim_info_label.pack(fill="x", padx=15, pady=1)
+    
+    def update_stats(self):
+        """更新统计信息"""
+        total_images = len(self.uploaded_images)
+        recognized_images = len(self.current_results)
+        
+        # 计算准确率（基于自动判断的结果）
+        auto_judged_results = [r for r in self.current_results if r.get('is_correct') is not None]
+        if auto_judged_results:
+            correct_count = sum(1 for r in auto_judged_results if r['is_correct'])
+            accuracy = (correct_count / len(auto_judged_results)) * 100
+            
+            # 构建统计文本，包含识别时间
+            if self.recognition_duration > 0:
+                stats_text = f"总图片: {total_images} | 已识别: {recognized_images} | 可判断: {len(auto_judged_results)} | 识别时间: {self.recognition_duration:.2f}秒 | 平均准确率: {accuracy:.1f}%"
+            else:
+                stats_text = f"总图片: {total_images} | 已识别: {recognized_images} | 可判断: {len(auto_judged_results)} | 平均准确率: {accuracy:.1f}%"
+        else:
+            # 如果没有可自动判断的结果
+            if self.recognition_duration > 0:
+                stats_text = f"总图片: {total_images} | 已识别: {recognized_images} | 可判断: 0 | 识别时间: {self.recognition_duration:.2f}秒 | 平均准确率: 0%"
+            else:
+                stats_text = f"总图片: {total_images} | 已识别: {recognized_images} | 可判断: 0 | 平均准确率: 0%"
+        
+        self.stats_label.configure(text=stats_text)
+
+
+def main():
+    # 创建支持拖放的窗口
+    root = TkinterDnD.Tk()
+    app = EmbeddingFoodClassifierApp(root)
+    root.mainloop()
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file