Update app.py

app.py

@@ -1,14 +1,14 @@
-# app.py (Gradio界面) - 优化版
+# app.py - 完整连接版本
 import gradio as gr
 import os
 import sys
 import logging
 from PIL import Image
 import numpy as np
-from sklearn.cluster import KMeans
 import time
 import random
 import warnings
+import json
 
 # 设置环境变量解决 OpenMP 问题
 os.environ['OMP_NUM_THREADS'] = '1'
@@ -25,6 +25,7 @@ logger = logging.getLogger(__name__)
 try:
     from models.model_manager import ModelManager
     MODELS_AVAILABLE = True
+    logger.info("模型管理器导入成功")
 except ImportError as e:
     logger.warning(f"模型管理器导入失败: {e}")
     logger.info("将使用简化版本运行")
@@ -40,6 +41,9 @@ class SimpleModelManager:
     def generate_caption(self, image):
         return "这是一件时尚的服装设计，具有现代感和优雅的风格。"
     
+    def analyze_style(self, image):
+        return {"时尚潮流": 0.8, "休闲风": 0.6, "优雅风": 0.4}
+    
     def generate_image(self, prompt, **kwargs):
         # 创建占位图像
         width, height = kwargs.get('width', 512), kwargs.get('height', 512)
@@ -50,7 +54,18 @@ class SimpleModelManager:
     def cleanup(self):
         pass
 
-# 根据是否有模型选择管理器
+# 全局状态管理
+class AppState:
+    def __init__(self):
+        self.current_image = None
+        self.current_caption = ""
+        self.current_analysis = {}
+        self.current_suggestions = {}
+        self.design_images = []
+        self.selected_suggestion = ""
+        self.selected_design = ""
+
+# 初始化
 if MODELS_AVAILABLE:
     try:
         model_manager = ModelManager()
@@ -61,80 +76,125 @@ if MODELS_AVAILABLE:
 else:
     model_manager = SimpleModelManager()
 
-def upload_and_analyze(image_path):
-    """分析上传的图片"""
+app_state = AppState()
+
+def upload_and_analyze(image_path, progress=gr.Progress()):
+    """完整的图片上传和分析流程"""
     try:
         if image_path is None:
-            return {}, {}, gr.Radio(choices=[])
+            return {}, {}, gr.Radio(choices=[]), gr.Gallery(value=[])
         
-        # 打开图片
+        progress(0.1, desc="加载图片...")
+        
+        # 打开并保存图片
         image = Image.open(image_path).convert('RGB')
+        app_state.current_image = image
+        
+        progress(0.2, desc="生成图像描述...")
         
-        # 生成图像描述
+        # 步骤1: 使用模型生成图像描述
         try:
             caption = model_manager.generate_caption(image)
+            app_state.current_caption = caption
+            logger.info(f"生成的图像描述: {caption}")
         except Exception as e:
             logger.error(f"生成描述失败: {e}")
             caption = "时尚服装设计"
+            app_state.current_caption = caption
         
-        # 基于图像描述进行智能分析
-        analysis_result = analyze_image_content(image, caption)
+        progress(0.4, desc="分析服装风格...")
         
-        # 基于分析结果生成个性化建议
-        suggestions = generate_personalized_suggestions(analysis_result, caption)
-        
-        # 创建选择选项
-        choices = list(suggestions.keys())
+        # 步骤2: 使用CLIP模型分析风格
+        try:
+            if hasattr(model_manager, 'analyze_style'):
+                style_scores = model_manager.analyze_style(image)
+            else:
+                # 回退到基于描述的分析
+                style_scores = analyze_style_from_caption(caption)
+            logger.info(f"风格分析结果: {style_scores}")
+        except Exception as e:
+            logger.error(f"风格分析失败: {e}")
+            style_scores = {"时尚潮流": 0.8, "休闲风": 0.6}
         
-        return analysis_result, suggestions, gr.Radio(choices=choices, value=choices[0] if choices else None)
+        progress(0.6, desc="提取颜色信息...")
         
-    except Exception as e:
-        error_result = {"错误": f"分析失败: {str(e)}"}
-        logger.error(f"上传分析失败: {e}")
-        return error_result, {}, gr.Radio(choices=[])
-
-def analyze_image_content(image, caption):
-    """基于图像和描述进行深度分析"""
-    try:
-        # 分析图像颜色
+        # 步骤3: 提取颜色信息
         colors = extract_dominant_colors(image)
         
-        # 根据描述推断风格类型
-        style_type = infer_style_from_caption(caption)
+        progress(0.8, desc="生成完整分析...")
         
-        # 根据描述推断服装类别
-        clothing_category = infer_clothing_category(caption)
-        
-        # 根据风格推荐适合场景
-        suitable_scenes = get_suitable_scenes(style_type)
-        
-        return {
+        # 步骤4: 整合分析结果
+        analysis_result = {
             "图像描述": caption,
-            "检测到的颜色": colors,
-            "风格类型": style_type,
-            "服装类别": clothing_category,
-            "适合场景": suitable_scenes,
+            "主要风格": max(style_scores.keys(), key=style_scores.get),
+            "风格评分": style_scores,
+            "主要颜色": colors,
             "图像尺寸": f"{image.width} x {image.height}",
             "分析时间": time.strftime("%Y-%m-%d %H:%M:%S")
         }
+        
+        # 步骤5: 基于模型分析结果生成个性化建议
+        suggestions = generate_personalized_suggestions_v2(
+            caption, style_scores, colors
+        )
+        
+        # 保存状态
+        app_state.current_analysis = analysis_result
+        app_state.current_suggestions = suggestions
+        
+        # 创建建议选项
+        choices = list(suggestions.keys())
+        
+        progress(1.0, desc="分析完成")
+        
+        return (
+            analysis_result, 
+            suggestions, 
+            gr.Radio(choices=choices, value=choices[0] if choices else None),
+            gr.Gallery(value=[])  # 清空之前的设计
+        )
+        
     except Exception as e:
-        logger.error(f"图像内容分析失败: {e}")
-        return {
-            "错误": f"分析失败: {str(e)}",
-            "图像描述": caption,
-            "风格类型": "休闲风"
-        }
+        error_result = {"错误": f"分析失败: {str(e)}"}
+        logger.error(f"上传分析失败: {e}")
+        return error_result, {}, gr.Radio(choices=[]), gr.Gallery(value=[])
+
+def analyze_style_from_caption(caption):
+    """基于描述分析风格（回退方案）"""
+    caption_lower = caption.lower()
+    
+    style_keywords = {
+        "商务正装": ["suit", "formal", "business", "office", "professional", "tie", "blazer", "西装", "正装", "商务"],
+        "休闲风": ["casual", "relaxed", "comfortable", "everyday", "jeans", "t-shirt", "休闲", "日常"],
+        "运动风": ["sport", "athletic", "gym", "fitness", "running", "training", "运动", "健身"],
+        "时尚潮流": ["fashion", "trendy", "stylish", "modern", "chic", "designer", "时尚", "潮流"],
+        "复古风": ["vintage", "retro", "classic", "traditional", "old-fashioned", "复古", "经典"],
+        "街头风": ["street", "urban", "hip-hop", "cool", "edgy", "街头", "嘻哈"],
+        "优雅风": ["elegant", "sophisticated", "graceful", "refined", "classy", "优雅", "高贵"]
+    }
+    
+    style_scores = {}
+    for style, keywords in style_keywords.items():
+        score = sum(0.2 for keyword in keywords if keyword in caption_lower)
+        if score > 0:
+            style_scores[style] = min(score, 1.0)
+    
+    # 如果没有匹配，给默认评分
+    if not style_scores:
+        style_scores = {"休闲风": 0.7, "时尚潮流": 0.5}
+    
+    return style_scores
 
 def extract_dominant_colors(image):
     """提取图像主要颜色"""
     try:
+        from sklearn.cluster import KMeans
+        
         # 调整图像大小以提高处理速度
         image = image.resize((150, 150))
         
         # 转换为numpy数组
         img_array = np.array(image)
-        
-        # 重塑为颜色列表
         pixels = img_array.reshape(-1, 3)
         
         # 使用KMeans聚类找到主要颜色
@@ -142,12 +202,12 @@ def extract_dominant_colors(image):
         kmeans.fit(pixels)
         
         # 将RGB值转换为颜色名称
-        color_names = []
+        colors = []
         for color in kmeans.cluster_centers_:
             color_name = rgb_to_color_name(color)
-            color_names.append(color_name)
+            colors.append(color_name)
         
-        return color_names
+        return colors
     except Exception as e:
         logger.error(f"颜色提取失败: {e}")
         return ["主色调", "辅助色", "点缀色"]
@@ -157,11 +217,10 @@ def rgb_to_color_name(rgb):
     try:
         r, g, b = rgb.astype(int)
         
-        # 简单的颜色映射
         if r > 200 and g > 200 and b > 200:
-            return "白色"
+            return "白色系"
         elif r < 50 and g < 50 and b < 50:
-            return "黑色"
+            return "黑色系"
         elif r > g and r > b:
             return "红色系" if r > 150 else "深红色系"
         elif g > r and g > b:
@@ -177,169 +236,177 @@ def rgb_to_color_name(rgb):
         else:
             return "灰色系"
     except:
-        return "未知色调"
+        return "混合色调"
 
-def infer_style_from_caption(caption):
-    """根据图像描述推断风格类型"""
-    caption_lower = caption.lower()
-    
-    style_keywords = {
-        "商务正装": ["suit", "formal", "business", "office", "professional", "tie", "blazer", "西装", "正装", "商务"],
-        "休闲风": ["casual", "relaxed", "comfortable", "everyday", "jeans", "t-shirt", "休闲", "日常"],
-        "运动风": ["sport", "athletic", "gym", "fitness", "running", "training", "运动", "健身"],
-        "时尚潮流": ["fashion", "trendy", "stylish", "modern", "chic", "designer", "时尚", "潮流"],
-        "复古风": ["vintage", "retro", "classic", "traditional", "old-fashioned", "复古", "经典"],
-        "街头风": ["street", "urban", "hip-hop", "cool", "edgy", "街头", "嘻哈"],
-        "优雅风": ["elegant", "sophisticated", "graceful", "refined", "classy", "优雅", "高贵"]
-    }
-    
-    for style, keywords in style_keywords.items():
-        if any(keyword in caption_lower for keyword in keywords):
-            return style
-    
-    return "休闲风"  # 默认风格
-
-def infer_clothing_category(caption):
-    """根据描述推断服装类别"""
-    caption_lower = caption.lower()
-    
-    categories = {
-        "上衣": ["shirt", "blouse", "top", "jacket", "sweater", "hoodie", "blazer", "衬衫", "上衣", "外套"],
-        "下装": ["pants", "jeans", "skirt", "shorts", "trousers", "裤子", "短裤", "裙子"],
-        "连衣裙": ["dress", "gown", "frock", "连衣裙", "礼服"],
-        "外套": ["coat", "jacket", "cardigan", "blazer", "外套", "大衣"],
-        "配饰": ["hat", "bag", "shoes", "belt", "jewelry", "帽子", "包", "鞋子", "配饰"],
-        "全套搭配": ["outfit", "ensemble", "look", "style", "搭配", "整套"]
-    }
-    
-    for category, keywords in categories.items():
-        if any(keyword in caption_lower for keyword in keywords):
-            return category
-    
-    return "服装单品"  # 默认类别
-
-def get_suitable_scenes(style_type):
-    """根据风格类型推荐适合场景"""
-    scene_mapping = {
-        "商务正装": ["办公室", "商务会议", "正式场合", "面试"],
-        "休闲风": ["日常出街", "朋友聚会", "购物", "咖啡约会"],
-        "运动风": ["健身房", "运动", "户外活动", "晨跑"],
-        "时尚潮流": ["时尚派对", "约会", "拍照", "社交活动"],
-        "复古风": ["艺术展", "文艺活动", "复古主题活动", "拍摄"],
-        "街头风": ["街拍", "音乐节", "朋友聚会", "潮流活动"],
-        "优雅风": ["晚宴", "正式聚会", "典礼", "高端场所"]
-    }
-    
-    return scene_mapping.get(style_type, ["日常", "休闲", "约会"])
-
-def generate_personalized_suggestions(analysis_result, caption):
-    """基于分析结果生成个性化建议"""
+def generate_personalized_suggestions_v2(caption, style_scores, colors):
+    """基于模型分析结果生成个性化建议"""
     try:
-        style_type = analysis_result.get("风格类型", "休闲风")
-        clothing_category = analysis_result.get("服装类别", "服装单品")
-        colors = analysis_result.get("检测到的颜色", ["主色调"])
+        # 获取主要风格
+        main_style = max(style_scores.keys(), key=style_scores.get) if style_scores else "时尚潮流"
+        main_color = colors[0] if colors else "经典色"
         
+        # 基于主要风格和颜色生成建议
         suggestions = {}
         
-        # 根据检测到的风格生成建议
-        if style_type == "商务正装":
+        if "商务" in main_style:
             suggestions = {
-                "经典商务": f"保持{style_type}特色，搭配{colors[0]}系配饰",
-                "现代商务": f"在{style_type}基础上加入现代元素",
-                "休闲商务": f"将{style_type}与休闲元素结合",
-                "时尚商务": f"{style_type}融入时尚潮流元素"
+                f"经典{main_style}": f"保持{main_style}特色，强调{main_color}的专业感",
+                f"现代{main_style}": f"在{main_style}基础上融入现代设计元素",
+                f"时尚{main_style}": f"{main_style}结合当下流行趋势",
+                f"个性{main_style}": f"基于{main_color}打造独特的{main_style}风格"
             }
-        elif style_type == "休闲风":
+        elif "休闲" in main_style:
             suggestions = {
-                "舒适休闲": f"强化{style_type}的舒适感，主色调{colors[0]}",
-                "时尚休闲": f"{style_type}加入时尚元素",
-                "运动休闲": f"{style_type}融入运动风格",
-                "优雅休闲": f"{style_type}提升优雅感"
+                "舒适休闲": f"强化舒适感，突出{main_color}的自然魅力",
+                "时尚休闲": f"休闲风格融入时尚元素",
+                "运动休闲": f"结合运动风格的休闲设计",
+                "优雅休闲": f"提升休闲装的优雅度"
             }
-        elif style_type == "运动风":
+        elif "运动" in main_style:
             suggestions = {
-                "专业运动": f"增强{style_type}的功能性",
-                "休闲运动": f"{style_type}与日常穿着结合",
-                "时尚运动": f"{style_type}加入潮流设计元素",
-                "户外运动": f"强化{style_type}的户外适应性"
+                "专业运动": f"强化功能性，突出{main_color}的活力",
+                "时尚运动": f"运动风格加入潮流设计",
+                "休闲运动": f"适合日常的运动休闲风",
+                "户外运动": f"适应户外环境的运动设��"
             }
         else:
+            # 其他风格的通用建议
             suggestions = {
-                f"经典{style_type}": f"保持原有{style_type}特色",
-                f"现代{style_type}": f"{style_type}加入现代元素",
-                f"融合风格": f"{style_type}与其他风格混搭",
-                f"个性化{style_type}": f"基于{colors[0]}色调的个性化{style_type}"
+                f"经典{main_style}": f"保持{main_style}的经典特色",
+                f"现代{main_style}": f"{main_style}融入现代设计理念",
+                f"创新{main_style}": f"在{main_style}基础上的创新尝试",
+                f"个性{main_style}": f"基于{main_color}的个性化{main_style}"
             }
         
-        return suggestions
+        # 添加基于颜色的额外建议
+        color_suggestions = {
+            f"单色系设计": f"围绕{main_color}打造单色系搭配",
+            f"撞色搭配": f"以{main_color}为主的撞色设计"
+        }
+        
+        # 合并建议（限制数量避免选项过多）
+        final_suggestions = dict(list(suggestions.items())[:4] + list(color_suggestions.items())[:2])
+        
+        return final_suggestions
+        
     except Exception as e:
         logger.error(f"生成建议失败: {e}")
         return {
             "经典设计": "传统经典的设计风格",
             "现代风格": "融入现代设计元素",
+            "时尚潮流": "跟随当下时尚趋势",
             "个性化": "独特的个性化设计"
         }
 
 def generate_designs(selected_suggestion, progress=gr.Progress()):
-    """根据选择的建议生成设计"""
+    """根据选择的建议使用SD模型生成设计"""
     try:
         if not selected_suggestion:
-            return [], gr.Radio(choices=[])
-        
-        progress(0.1, desc="准备设计提示...")
-        
-        # 生成设计图像的提示词
-        design_prompts = {
-            "经典商务": "professional business suit, modern cut, high-quality fabric, clean lines, neutral colors",
-            "现代商务": "contemporary business attire, innovative design, slim fit, premium materials",
-            "休闲商务": "business casual outfit, comfortable yet professional, versatile style",
-            "时尚商务": "fashion-forward business wear, runway inspired, statement piece",
-            "舒适休闲": "casual comfort wear, soft fabrics, relaxed fit, everyday style",
-            "时尚休闲": "stylish casual outfit, trendy elements, urban chic",
-            "运动休闲": "athleisure wear, sporty elements, comfortable and functional",
-            "优雅休闲": "elegant casual attire, sophisticated details, refined look"
-        }
+            return gr.Gallery(value=[]), gr.Radio(choices=[])
+        
+        progress(0.1, desc="准备设计生成...")
         
-        prompt = design_prompts.get(selected_suggestion, "fashion design, stylish clothing")
+        # 保存选择的建议
+        app_state.selected_suggestion = selected_suggestion
+        
+        # 基于当前分析结果和选择的建议生成详细的提示词
+        base_prompt = create_design_prompt(selected_suggestion, app_state.current_analysis)
         
         # 生成设计图像
         design_images = []
         design_choices = []
         
-        for i in range(3):  # 生成3个设计
+        for i in range(3):  # 生成3个设计方案
             try:
                 progress(0.2 + i*0.25, desc=f"生成设计方案 {i+1}/3...")
                 
-                # 生成设计图像
+                # 为每个设计添加变化
+                variant_prompt = f"{base_prompt}, design variation {i+1}, unique style, high quality fashion design"
+                
+                # 使用模型生成图像
                 image = model_manager.generate_image(
-                    prompt=f"{prompt}, design {i+1}, high detail, fashion illustration",
-                    negative_prompt="blurry, low quality, distorted, text, watermark",
-                    num_inference_steps=20,  # 减少步数以提高速度
+                    prompt=variant_prompt,
+                    negative_prompt="blurry, low quality, distorted, text, watermark, ugly, deformed",
+                    num_inference_steps=25,
                     width=512,
-                    height=512
+                    height=512,
+                    guidance_scale=7.5
                 )
                 
                 if image:
                     design_images.append(image)
-                    design_choices.append(f"{selected_suggestion} 设计方案 {i+1}")
+                    design_choices.append(f"{selected_suggestion} - 方案 {i+1}")
+                    logger.info(f"成功生成设计方案 {i+1}")
                     
             except Exception as e:
                 logger.error(f"生成设计 {i+1} 失败: {e}")
                 # 创建占位图像
                 width, height = 512, 512
-                img = Image.new('RGB', (width, height), 
-                               color=(random.randint(100, 200), 
-                                      random.randint(100, 200), 
-                                      random.randint(100, 200)))
+                color = (random.randint(120, 200), random.randint(120, 200), random.randint(120, 200))
+                img = Image.new('RGB', (width, height), color=color)
                 design_images.append(img)
-                design_choices.append(f"{selected_suggestion} 设计方案 {i+1}")
+                design_choices.append(f"{selected_suggestion} - 方案 {i+1} (占位)")
+        
+        # 保存生成的设计
+        app_state.design_images = design_images
         
         progress(0.95, desc="完成设计生成")
-        return design_images, gr.Radio(choices=design_choices, value=design_choices[0] if design_choices else None)
+        
+        return (
+            gr.Gallery(value=design_images), 
+            gr.Radio(choices=design_choices, value=design_choices[0] if design_choices else None)
+        )
         
     except Exception as e:
         logger.error(f"设计生成错误: {e}")
-        return [], gr.Radio(choices=[])
+        return gr.Gallery(value=[]), gr.Radio(choices=[])
+
+def create_design_prompt(suggestion, analysis):
+    """基于建议和分析创建详细的设计提示词"""
+    try:
+        # 从分析中提取关键信息
+        style_info = analysis.get("主要风格", "时尚")
+        colors = analysis.get("主要颜色", ["现代色"])
+        description = analysis.get("图像描述", "")
+        
+        # 基础提示词映射
+        style_prompts = {
+            "商务正装": "professional business attire, formal suit, clean lines, sophisticated",
+            "休闲风": "casual clothing, comfortable style, everyday wear, relaxed fit",
+            "运动风": "sportswear, athletic clothing, functional design, activewear",
+            "时尚潮流": "trendy fashion, stylish clothing, contemporary design, fashionable",
+            "复古风": "vintage style, retro fashion, classic design, nostalgic elements",
+            "街头风": "streetwear, urban fashion, casual street style, modern youth",
+            "优雅风": "elegant clothing, sophisticated style, refined fashion, graceful design"
+        }
+        
+        # 获取风格对应的提示词
+        style_prompt = style_prompts.get(style_info, "stylish clothing, modern design")
+        
+        # 颜色相关的提示词
+        color_prompt = f"color palette inspired by {colors[0] if colors else 'modern colors'}"
+        
+        # 根据建议调整提示词
+        if "经典" in suggestion:
+            modifier = "timeless, classic design, traditional elements"
+        elif "现代" in suggestion:
+            modifier = "contemporary, modern aesthetic, innovative design"
+        elif "时尚" in suggestion:
+            modifier = "fashion-forward, trendy, runway-inspired"
+        elif "个性" in suggestion:
+            modifier = "unique, personalized, distinctive style"
+        else:
+            modifier = "high-quality, well-designed, stylish"
+        
+        # 组合完整的提示词
+        full_prompt = f"{style_prompt}, {color_prompt}, {modifier}, high detail, professional fashion photography, studio lighting"
+        
+        logger.info(f"生成的设计提示词: {full_prompt}")
+        return full_prompt
+        
+    except Exception as e:
+        logger.error(f"创建提示词失败: {e}")
+        return "stylish clothing design, high quality, professional fashion"
 
 def generate_3d_fitting(selected_design, progress=gr.Progress()):
     """生成3D试穿效果"""
@@ -349,18 +416,39 @@ def generate_3d_fitting(selected_design, progress=gr.Progress()):
         
         progress(0.1, desc="准备3D试穿...")
         
-        # 生成3D试穿效果的提示词
-        fitting_prompt = f"3D fashion fitting, virtual try-on, {selected_design}, realistic human model, full body, studio lighting"
+        # 保存选择的设计
+        app_state.selected_design = selected_design
+        
+        # 基于选择的设计和原始分析创建3D试穿提示词
+        fitting_prompt = create_3d_fitting_prompt(selected_design, app_state.current_analysis)
+        
+        progress(0.3, desc="生成3D模型...")
         
-        progress(0.5, desc="生成3D效果...")
+        # 尝试使用ControlNet如果可用
+        if hasattr(model_manager, 'controlnet_pipeline') and model_manager.controlnet_pipeline:
+            try:
+                # 使用ControlNet生成更精确的试穿效果
+                image = model_manager.generate_controlnet_image(
+                    image=app_state.current_image,  # 使用原始图像作为控制
+                    prompt=fitting_prompt,
+                    negative_prompt="blurry, distorted, low quality, unrealistic, extra limbs, deformed",
+                    num_inference_steps=30,
+                    guidance_scale=8.0
+                )
+                progress(0.9, desc="完成3D渲染")
+                return image
+            except Exception as e:
+                logger.warning(f"ControlNet生成失败，使用普通SD: {e}")
         
-        # 生成图像
+        # 回退到普通SD模型
+        progress(0.5, desc="使用标准模型生成...")
         image = model_manager.generate_image(
             prompt=fitting_prompt,
-            negative_prompt="blurry, distorted, low quality, unrealistic, extra limbs",
-            num_inference_steps=25,
+            negative_prompt="blurry, distorted, low quality, unrealistic, extra limbs, deformed, bad anatomy",
+            num_inference_steps=30,
             width=512,
-            height=768  # 适合全身图像
+            height=768,  # 适合全身图像
+            guidance_scale=7.5
         )
         
         progress(0.9, desc="完成3D渲染")
@@ -369,46 +457,78 @@ def generate_3d_fitting(selected_design, progress=gr.Progress()):
     except Exception as e:
         logger.error(f"3D试穿生成错误: {e}")
         # 创建占位图像
-        img = Image.new('RGB', (512, 768), color=(200, 200, 200))
+        img = Image.new('RGB', (512, 768), color=(180, 180, 180))
         return img
 
+def create_3d_fitting_prompt(design_info, analysis):
+    """创建3D试穿的提示词"""
+    try:
+        style_info = analysis.get("主要风格", "时尚")
+        colors = analysis.get("主要颜色", ["现代色"])
+        
+        # 3D试穿基础提示词
+        base_prompt = "3D virtual try-on, realistic human model wearing"
+        
+        # 根据设计信息调整
+        if "方案" in design_info:
+            design_type = design_info.split(" - ")[0] if " - " in design_info else design_info
+        else:
+            design_type = design_info
+        
+        # 添加风格和颜色信息
+        style_desc = f"{style_info} style clothing"
+        color_desc = f"in {colors[0] if colors else 'stylish colors'}"
+        
+        # 完整的3D试穿提示词
+        full_prompt = f"{base_prompt} {style_desc} {color_desc}, full body shot, professional studio lighting, high quality 3D rendering, realistic fabric texture, well-fitted clothing"
+        
+        logger.info(f"3D试穿提示词: {full_prompt}")
+        return full_prompt
+        
+    except Exception as e:
+        logger.error(f"创建3D提示词失败: {e}")
+        return "3D virtual fashion try-on, realistic human model, full body, studio lighting"
+
 def create_gradio_interface():
     """创建Gradio用户界面"""
     
     with gr.Blocks(title="AI时尚设计师", theme="soft") as demo:
         gr.Markdown("# 🎨 AI时尚设计师")
-        gr.Markdown("上传图片，获得专业的服装设计建议和3D试穿效果")
+        gr.Markdown("上传服装图片，AI将分析风格并生成个性化设计方案和3D试穿效果")
         
         with gr.Row():
             with gr.Column(scale=1):
                 image_input = gr.Image(type="filepath", label="上传参考图片", height=400)
-                analyze_btn = gr.Button("分析风格", variant="primary")
+                analyze_btn = gr.Button("🔍 AI智能分析", variant="primary", size="lg")
             
             with gr.Column(scale=2):
-                analysis_output = gr.JSON(label="风格分析结果")
+                analysis_output = gr.JSON(label="🎯 AI分析结果")
         
-        with gr.Tab("设计建议"):
+        with gr.Tab("💡 设计建议"):
             suggestions_output = gr.JSON(label="个性化设计建议")
             suggestion_choice = gr.Radio(label="选择设计方向", interactive=True)
-            generate_designs_btn = gr.Button("生成设计方案", variant="primary")
+            generate_designs_btn = gr.Button("🎨 生成设计方案", variant="primary")
         
-        with gr.Tab("样衣设计"):
-            designs_gallery = gr.Gallery(label="样衣设计图", columns=3, height=400)
+        with gr.Tab("👔 样衣设计"):
+            designs_gallery = gr.Gallery(label="AI生成的设计方案", columns=3, height=400)
             design_choice = gr.Radio(label="选择设计方案", interactive=True)
-            generate_3d_btn = gr.Button("生成3D试穿效果", variant="primary")
-        
-        with gr.Tab("3D试穿效果"):
-            fitting_result = gr.Image(label="3D试穿效果", height=500)
+            generate_3d_btn = gr.Button("🎭 生成3D试穿效果", variant="primary")
         
-        # 内存管理
+        with gr.Tab("👤 3D试穿效果"):
+            fitting_result = gr.Image(label="3D试穿效果", height=600)
+            
+        # 系统状态和控制
         with gr.Row():
-            cleanup_btn = gr.Button("清理内存", variant="secondary")
+            with gr.Column():
+                gr.Markdown("### 🔧 系统控制")
+                cleanup_btn = gr.Button("🧹 清理内存", variant="secondary")
+                gr.Markdown("> **提示**: 如果遇到内存问题，请点击清理内存按钮")
             
-        # 事件绑定 - 使用新的 concurrency_limit 参数
+        # 事件绑定 - 完整的流水线连接
         analyze_btn.click(
             fn=upload_and_analyze,
             inputs=[image_input],
-            outputs=[analysis_output, suggestions_output, suggestion_choice],
+            outputs=[analysis_output, suggestions_output, suggestion_choice, designs_gallery],
             concurrency_limit=1
         )
         
@@ -432,7 +552,17 @@ def create_gradio_interface():
             outputs=[]
         )
         
-        gr.Markdown("> **提示**: 如果遇到内存问题，请点击'清理内存'按钮")
+        # 添加使用说明
+        with gr.Row():
+            gr.Markdown("""
+            ### 📋 使用流程:
+            1. **上传图片** → 点击"AI智能分析"进行风格识别
+            2. **查看建议** → 在"设计建议"标签中选择心仪的设计方向
+            3. **生成设计** → 点击"生成设计方案"查看AI设计的服装
+            4. **3D试穿** → 选择喜欢的设计，点击"生成3D试穿效果"
+            
+            💡 **提示**: 每一步都会调用相应的AI模型，请耐心等待生成完成
+            """)
     
     return demo
 
@@ -440,15 +570,16 @@ if __name__ == "__main__":
     # 检查环境
     logger.info(f"Python版本: {sys.version}")
     logger.info(f"当前工作目录: {os.getcwd()}")
+    logger.info(f"模型管理器状态: {'完整版' if MODELS_AVAILABLE else '简化版'}")
     
     # 创建并启动界面
     demo = create_gradio_interface()
     
-    # 启动应用 - 移除废弃的 queue 配置
+    # 启动应用
     demo.launch(
         server_name="0.0.0.0", 
         server_port=7860,
-        share=False,  # 在Spaces环境中设为False
+        share=False,
         show_error=True,
-        max_threads=2  # 限制线程数
+        max_threads=2
     )