第一次

PycharmProjects/app.py

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+import streamlit as st
+from templates.user_info import show_user_info_form
+from templates.recommendations import show_recommendations_tab
+from templates.recommendations import show_virtual_tryon_tab
+
+# 页面设置
+st.set_page_config(page_title="智能穿搭助手", page_icon="👗", layout="wide")
+
+# 标题和介绍
+st.title("👗 智能穿搭助手")
+
+# 初始化session state
+if 'user_info' not in st.session_state:
+    st.session_state.user_info = {}
+if 'recommendations' not in st.session_state:
+    st.session_state.recommendations = None
+
+# 侧边栏 - 用户信息收集
+show_user_info_form()
+
+# 主界面
+tab1, tab2 = st.tabs(["穿搭推荐", "虚拟试衣"])
+
+with tab1:
+    show_recommendations_tab()
+
+with tab2:
+    show_virtual_tryon_tab()
+
+if __name__ == "__main__":
+    st.write("")

PycharmProjects/config.py

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+import os
+from dotenv import load_dotenv
+
+# 中国主要城市中英文对照表
+CITY_NAME_MAPPING = {
+    # 直辖市
+    "beijing": "北京", "peking": "北京",
+    "shanghai": "上海",
+    # ... (保持原有的城市映射不变)
+}
+
+# 加载环境变量
+load_dotenv()
+MOONSHOT_API_KEY = os.getenv("MOONSHOT_API_KEY")
+WEATHER_API_KEY = os.getenv("WEATHER_API_KEY")

PycharmProjects/templates/recommendations.py

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+import streamlit as st
+import requests
+from PIL import Image
+
+from utils.weather import get_weather_data
+from utils.outfits import generate_sample_outfits
+from utils.outfits import generate_fallback_outfits
+from utils.image_processing import process_image_for_try_on
+from config import MOONSHOT_API_KEY
+
+
+def show_recommendations_tab():
+    st.header("智能穿搭推荐")
+
+    with st.form("scene_form"):
+        col1, col2 = st.columns(2)
+        with col1:
+            scene_input = st.text_input("描述你的场景需求",
+                                        placeholder="例如: 今天要和朋友们去公园野餐")
+        with col2:
+            city_input = st.text_input("输入城市名称",
+                                       placeholder="例如: 北京",
+                                       value="北京")
+
+        use_weather = st.checkbox("获取当地天气信息", value=True)
+        submitted = st.form_submit_button("获取穿搭建议")
+
+        if submitted and scene_input:
+            with st.spinner("正在生成穿搭建议..."):
+                try:
+                    weather_info = ""
+                    if use_weather and city_input:
+                        weather_data = get_weather_data(city_input)
+                        if weather_data:
+                            weather_info = f"""
+                            ## 当前天气情况
+                            - 城市: {weather_data['city']}
+                            - 天气状况: {weather_data['description']}
+                            - 温度: {weather_data['temp']}°C
+                            - 湿度: {weather_data['humidity']}%
+                            """
+                            st.markdown(weather_info)
+
+                    prompt = build_prompt(scene_input, weather_info if use_weather else "")
+                    response = call_moonshot_api(prompt)
+
+                    if response:
+                        st.session_state.recommendations = response
+                        st.success("穿搭建议生成成功！")
+
+                        # 新增：根据建议生成图片
+                        outfit_image = generate_sample_outfits(response)
+                        if outfit_image:
+                            st.image(outfit_image, caption="推荐穿搭可视化", width=300)
+                            st.session_state.outfit_image = outfit_image
+                        else:
+                            st.warning("无法生成穿搭图片，但文字建议有效")
+                    else:
+                        st.error("获取穿搭建议失败")
+
+                except Exception as e:
+                    st.error(f"获取建议时出错: {str(e)}")
+
+    # recommendations.py 修改后的显示部分
+    if st.session_state.recommendations:
+        st.markdown("### 您的专属穿搭建议")
+        st.markdown(st.session_state.recommendations)
+
+        st.markdown("### 搭配示例")
+        try:
+            sample_images = generate_sample_outfits(
+                st.session_state.recommendations,
+                save_to_local=True
+            )
+
+            if sample_images:
+                # 确保只显示实际生成的图片数量
+                num_images = min(3, len(sample_images))
+                cols = st.columns(num_images)
+
+                for i in range(num_images):
+                    with cols[i]:
+                        st.image(
+                            sample_images[i],
+                            use_container_width=True,
+                            caption=f"穿搭方案 {i + 1}"
+                        )
+                        st.success(f"示例 {i + 1}")
+            else:
+                st.warning("无法生成任何穿搭示例图片")
+
+        except Exception as e:
+            st.error(f"生成穿搭示例时出错: {str(e)}")
+            # 显示回退方案
+            fallback_images = generate_fallback_outfits(save_to_local=True)
+            cols = st.columns(3)
+            for i, col in enumerate(cols):
+                col.image(
+                    fallback_images[i],
+                    use_container_width=True,
+                    caption=f"穿搭方案 {i + 1} (示例)"
+                )
+
+
+
+def build_prompt(scene_input, weather_info):
+    return f"""
+    你是一位专业的时尚穿搭助手，请根据以下信息为用户提供穿搭建议:
+
+    ### 用户信息
+    {st.session_state.user_info}
+
+    ### 场景描述
+    {scene_input}
+
+    ### 天气信息
+    {weather_info if weather_info else "用户未提供天气信息"}
+
+    ### 请提供以下内容:
+    1. 适合该场景的3套穿搭方案
+    2. 每套方案的风格特点和适用场合说明
+    3. 颜色搭配建议
+    4. 材质和面料选择建议
+    5. 购买建议和品牌推荐(可选)
+    """
+
+
+def call_moonshot_api(prompt):
+    headers = {
+        "Authorization": f"Bearer {MOONSHOT_API_KEY}",
+        "Content-Type": "application/json"
+    }
+
+    data = {
+        "model": "moonshot-v1-128k",
+        "messages": [
+            {
+                "role": "system",
+                "content": "你是一位专业的时尚顾问，擅长根据用户特征、场景和天气状况提供个性化的穿搭建议。"
+            },
+            {
+                "role": "user",
+                "content": prompt
+            }
+        ],
+        "temperature": 0.7,
+        "max_tokens": 2000
+    }
+
+    response = requests.post(
+        "https://api.moonshot.cn/v1/chat/completions",
+        headers=headers,
+        json=data
+    )
+
+    if response.status_code == 200:
+        result = response.json()
+        return result["choices"][0]["message"]["content"]
+    else:
+        st.error(f"API调用失败: {response.text}")
+        return None
+
+
+def show_virtual_tryon_tab():
+    st.header("虚拟试衣间")
+    st.info("此功能正在开发中，即将推出!")
+
+    uploaded_file = st.file_uploader("上传你的照片进行虚拟试衣", type=["jpg", "png", "jpeg"])
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption="上传的照片", use_container_width=True)
+
+        gray, edges = process_image_for_try_on(image)
+        col1, col2 = st.columns(2)
+        col1.image(gray, caption="灰度处理", use_container_width=True, channels="GRAY")
+        col2.image(edges, caption="边缘检测", use_container_width=True, channels="GRAY")

PycharmProjects/templates/user_info.py

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+import streamlit as st
+
+def show_user_info_form():
+    with st.sidebar:
+        st.header("个人信息")
+        with st.form("user_info_form"):
+            gender = st.selectbox("性别", ["女", "男", "其他"])
+            age = st.slider("年龄", 10, 80, 25)
+            height = st.number_input("身高(cm)", 100, 250, 165)
+            weight = st.number_input("体重(kg)", 30, 150, 55)
+            skin_tone = st.selectbox("肤色", ["白皙", "自然", "小麦色", "深色"])
+            style_preference = st.multiselect("风格偏好", ["休闲", "正式", "运动", "甜美", "复古", "街头", "商务"])
+            submitted = st.form_submit_button("保存信息")
+
+            if submitted:
+                st.session_state.user_info = {
+                    "gender": gender,
+                    "age": age,
+                    "height": height,
+                    "weight": weight,
+                    "skin_tone": skin_tone,
+                    "style_preference": style_preference
+                }
+                st.success("个人信息已保存!")

PycharmProjects/utils/__init__.py

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+from .weather import get_weather_data
+from .outfits import generate_sample_outfits
+from .image_processing import process_image_for_try_on

PycharmProjects/utils/image_processing.py

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+import cv2
+import numpy as np
+from PIL import Image
+
+def process_image_for_try_on(image):
+    """处理上传的试衣图片"""
+    img_array = np.array(image)
+    gray = cv2.cvtColor(img_array, cv2.COLOR_BGR2GRAY)
+    edges = cv2.Canny(gray, 100, 200)
+    return gray, edges

PycharmProjects/utils/outfits.py

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+import numpy as np
+import matplotlib.pyplot as plt
+import cv2
+from io import BytesIO
+from PIL import Image
+import streamlit as st
+
+try:
+    import tensorflow as tf
+    from tensorflow.keras.datasets import fashion_mnist
+
+    HAS_TF = True
+except ImportError:
+    HAS_TF = False
+
+import os
+from datetime import datetime
+
+
+def map_keyword_to_indices(text, class_names):
+    """私有方法：从推荐文本解析服装类别索引"""
+    keyword_map = {
+        # 中文关键词映射
+        "t恤": 0, "衬衫": 6, "外套": 4, "套头衫": 2,
+        "裤子": 1, "牛仔裤": 1,
+        "连衣裙": 3, "裙子": 3,
+        "凉鞋": 5, "运动鞋": 7, "短靴": 9, "靴子": 9
+    }
+
+    matched_indices = []
+    text_lower = text.lower()
+    for keyword, idx in keyword_map.items():
+        if keyword in text_lower:
+            matched_indices.append(idx)
+    return list(set(matched_indices))  # 去重
+
+
+# outfits.py 修改后的代码
+def generate_sample_outfits(recommendation_text, save_to_local=False, output_dir="C:/Users/Administrator/Desktop"):
+    """生成示例穿搭图片（包含连衣裙选项）并可选保存到本地"""
+    outfits = []
+
+    # 创建输出目录
+    if save_to_local and not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    if HAS_TF:
+        try:
+            (train_images, train_labels), _ = fashion_mnist.load_data()
+            class_names = ['T恤', '裤子', '套头衫', '连衣裙', '外套',
+                           '凉鞋', '衬衫', '运动鞋', '包', '短靴']
+
+            # 从推荐文本中提取服装类别索引
+            matched_indices = map_keyword_to_indices(recommendation_text, class_names)
+
+            # 根据匹配结果生成图片
+            if not matched_indices:
+                st.warning("无法从推荐文本中识别有效服装类别")
+                return generate_fallback_outfits(save_to_local, output_dir)
+
+            # 生成3套穿搭方案
+            for i in range(3):
+                try:
+                    # 随机决定是生成连衣裙组合还是上下装组合
+                    use_dress = np.random.choice([True, False]) and (3 in matched_indices)
+
+                    if use_dress:
+                        # 连衣裙+鞋子组合
+                        shoes_indices = [i for i in matched_indices if i in [5, 7, 9]]
+                        if not shoes_indices:
+                            continue
+
+                        shoes_idx = np.random.choice(shoes_indices)
+                        dress_img = train_images[np.random.choice(np.where(train_labels == 3)[0])]
+                        shoes_img = train_images[np.random.choice(np.where(train_labels == shoes_idx)[0])]
+
+                        dress_img = cv2.resize(dress_img, (128, 256), interpolation=cv2.INTER_NEAREST)
+                        shoes_img = cv2.resize(shoes_img, (128, 128), interpolation=cv2.INTER_NEAREST)
+                        combined = np.vstack([dress_img, shoes_img])
+                        combined_rgb = np.stack([combined] * 3, axis=-1)
+
+                        title = f"{class_names[3]}+{class_names[shoes_idx]}"
+                    else:
+                        # 上下装+鞋子组合
+                        top_indices = [i for i in matched_indices if i in [0, 2, 4, 6]]
+                        bottom_indices = [i for i in matched_indices if i in [1]]
+                        shoes_indices = [i for i in matched_indices if i in [5, 7, 9]]
+
+                        if not top_indices or not bottom_indices or not shoes_indices:
+                            continue
+
+                        top_idx = np.random.choice(top_indices)
+                        bottom_idx = np.random.choice(bottom_indices)
+                        shoes_idx = np.random.choice(shoes_indices)
+
+                        top_img = train_images[np.random.choice(np.where(train_labels == top_idx)[0])]
+                        bottom_img = train_images[np.random.choice(np.where(train_labels == bottom_idx)[0])]
+                        shoes_img = train_images[np.random.choice(np.where(train_labels == shoes_idx)[0])]
+
+                        top_img = cv2.resize(top_img, (128, 128), interpolation=cv2.INTER_NEAREST)
+                        bottom_img = cv2.resize(bottom_img, (128, 128), interpolation=cv2.INTER_NEAREST)
+                        shoes_img = cv2.resize(shoes_img, (128, 128), interpolation=cv2.INTER_NEAREST)
+                        combined = np.vstack([top_img, bottom_img, shoes_img])
+                        combined_rgb = np.stack([combined] * 3, axis=-1)
+
+                        title = f"{class_names[top_idx]}+{class_names[bottom_idx]}+{class_names[shoes_idx]}"
+
+                    # 直接使用PIL保存图片，避免plt保存问题
+                    img = Image.fromarray(combined_rgb)
+
+                    if save_to_local:
+                        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+                        filename = f"{output_dir}/outfit_{timestamp}_{i}.png"
+                        img.save(filename, format='PNG', quality=95)
+
+                    outfits.append(img)
+
+                except Exception as e:
+                    st.warning(f"生成第{i + 1}套穿搭图片时出错: {str(e)}")
+                    outfits.append(generate_single_fallback_outfit(i + 1, save_to_local, output_dir))
+                    continue
+
+            return outfits if outfits else generate_fallback_outfits(save_to_local, output_dir)
+
+        except Exception as e:
+            st.warning(f"生成服装图片出错: {str(e)}，使用文字替代")
+            return generate_fallback_outfits(save_to_local, output_dir)
+
+    return generate_fallback_outfits(save_to_local, output_dir)
+
+
+def generate_single_fallback_outfit(index, save_to_local=False, output_dir=None):
+    """生成单个回退的文字穿搭方案"""
+    fig, ax = plt.subplots(figsize=(3, 4))
+    ax.set_facecolor('#f0f2f6')
+    ax.text(0.5, 0.5, f"穿搭方案 {index}\n(图片生成失败)",
+            ha='center', va='center', fontsize=10)
+    ax.axis('off')
+
+    buf = BytesIO()
+    fig.savefig(buf, format='png', bbox_inches='tight', pad_inches=0.1, dpi=100)
+    buf.seek(0)
+    img = Image.open(buf)
+    plt.close(fig)
+
+    if save_to_local and output_dir:
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        filename = f"{output_dir}/outfit_{timestamp}_{index}_fallback.png"
+        img.save(filename)
+
+    return img
+
+
+def generate_fallback_outfits(save_to_local=False, output_dir="C:/Users/Administrator/Desktop"):
+    """生成回退的文字穿搭方案"""
+    outfits = []
+    for i in range(3):
+        outfits.append(generate_single_fallback_outfit(i + 1, save_to_local, output_dir))
+    return outfits

PycharmProjects/utils/weather.py

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+import requests
+from config import CITY_NAME_MAPPING, WEATHER_API_KEY
+
+def get_weather_data(city, api_key=WEATHER_API_KEY):
+    """获取天气数据"""
+    base_url = "http://api.openweathermap.org/data/2.5/weather"
+    params = {
+        "q": city,
+        "appid": api_key,
+        "units": "metric",
+        "lang": "zh_cn"
+    }
+
+    try:
+        response = requests.get(base_url, params=params, timeout=10)
+        data = response.json()
+
+        if response.status_code == 200:
+            normalized_input = city.lower().replace(" ", "")
+            chinese_name = CITY_NAME_MAPPING.get(normalized_input, data.get("name", city))
+
+            return {
+                "city": chinese_name,
+                "temp": data["main"]["temp"],
+                "humidity": data["main"]["humidity"],
+                "description": data["weather"][0]["description"],
+                "icon": data["weather"][0]["icon"]
+            }
+        else:
+            return None
+
+    except Exception as e:
+        print(f"获取天气数据时出错: {str(e)}")
+        return None