Revert^2 "feat: 完整集成JWLLL搜索推荐系统到Merge项目"
This reverts commit 8c2ae427041141a7dfc6f7b1ca1a16e713003130.
Reason for revert: <回退功能，command由jwl实现>
Change-Id: I08cf7f6de082d6a837aa3e59f68787dbf9d4d1e1
diff --git a/Merge/back_jwlll/README.md b/Merge/back_jwlll/README.md
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+++ b/Merge/back_jwlll/README.md
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+# JWLLL 搜索推荐算法服务
+
+这个模块包含了 JWLLL 的搜索推荐算法服务，提供以下功能：
+
+## 主要功能
+
+1. **智能搜索**
+ - 支持中文分词
+ - 拼音搜索
+ - 语义关联搜索
+ - TF-IDF 向量相似度
+ - Word2Vec 语义扩展
+
+2. **推荐算法**
+ - 基于标签的内容推荐
+ - 协同过滤推荐
+ - 个性化推荐
+
+3. **帖子管理**
+ - 帖子详情查看
+ - 点赞/取消点赞
+ - 评论功能
+ - 帖子上传
+
+## API 接口
+
+- `POST /search` - 搜索内容
+- `GET /user_tags` - 获取用户标签
+- `POST /recommend_tags` - 标签推荐
+- `POST /user_based_recommend` - 协同过滤推荐
+- `GET /post/<id>` - 获取帖子详情
+- `POST /like` - 点赞帖子
+- `POST /unlike` - 取消点赞
+- `POST /comment` - 添加评论
+- `GET /comments/<post_id>` - 获取评论
+- `POST /upload` - 上传帖子
+
+## 部署说明
+
+1. 确保安装了所有依赖：
+ ```bash
+ pip install -r requirements.txt
+ ```
+
+2. 配置数据库连接：
+ - 修改 `config.py` 中的数据库配置
+
+3. 启动服务：
+ ```bash
+ python app.py
+ ```
+
+4. 服务将在 http://127.0.0.1:5000 启动
+
+## 配置文件
+
+- `semantic_config.json` - 语义映射配置
+- `models/chinese_word2vec.bin` - Word2Vec 模型文件（可选）
+
+## 注意事项
+
+- Word2Vec 模型文件较大，如果没有可以禁用该功能
+- 确保数据库中有测试数据
+- 默认用户ID为 '3'，请确保数据库中存在该用户
diff --git a/Merge/back_jwlll/app.py b/Merge/back_jwlll/app.py
new file mode 100644
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+++ b/Merge/back_jwlll/app.py
@@ -0,0 +1,1076 @@
+# main_online.py
+# 搜索推荐算法服务的主入口
+
+import json
+import numpy as np
+import difflib
+from flask import Flask, request, jsonify, Response
+import pymysql
+import jieba
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+import pypinyin
+from flask_cors import CORS
+import re
+import Levenshtein
+import os
+import logging
+
+# 设置日志
+logging.basicConfig(
+ level=logging.INFO,
+ format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger("allpt-search")
+
+# 导入Word2Vec辅助模块
+try:
+ from word2vec_helper import get_word2vec_helper, expand_query, get_similar_words
+ WORD2VEC_ENABLED = True
+ logger.info("Word2Vec模块已加载")
+except ImportError as e:
+ logger.warning(f"Word2Vec模块加载失败: {e}，将使用传统搜索")
+ WORD2VEC_ENABLED = False
+
+# 数据库配置
+DB_CONFIG = {
+ "host": "10.126.59.25",
+ "port": 3306,
+ "user": "root",
+ "password": "123456",
+ "database": "redbook",
+ "charset": "utf8mb4"
+}
+
+def get_db_conn():
+ return pymysql.connect(**DB_CONFIG)
+
+def get_pinyin(text):
+ # 返回字符串的全拼音（不带声调，全部小写），支持英文直接返回
+ if not text:
+ return ""
+ import re
+ # 如果全是英文，直接返回小写
+ if re.fullmatch(r'[a-zA-Z]+', text):
+ return text.lower()
+ return ''.join([p[0] for p in pypinyin.pinyin(text, style=pypinyin.NORMAL)])
+
+def get_pinyin_initials(text):
+ # 返回字符串的首字母拼音（全部小写），支持英文直接返回
+ if not text:
+ return ""
+ import re
+ if re.fullmatch(r'[a-zA-Z]+', text):
+ return text.lower()
+ return ''.join([p[0][0] for p in pypinyin.pinyin(text, style=pypinyin.NORMAL)])
+
+# 新增词语相似度计算函数
+def word_similarity(word1, word2):
+ """计算两个词的相似度，支持拼音匹配"""
+ # 直接匹配
+ if word1 == word2:
+ return 1.0
+
+ # 拼音匹配
+ if get_pinyin(word1) == get_pinyin(word2):
+ return 0.9
+
+ # 拼音首字母匹配
+ if get_pinyin_initials(word1) == get_pinyin_initials(word2):
+ return 0.7
+
+ # 字符串相似度
+ return difflib.SequenceMatcher(None, word1, word2).ratio()
+
+def semantic_title_similarity(query, title):
+ """计算查询词与标题的语义相似度"""
+ # 分词
+ query_words = list(jieba.cut(query))
+ title_words = list(jieba.cut(title))
+
+ if not query_words or not title_words:
+ return 0.0
+
+ # 计算每个查询词与标题词的最大相似度
+ max_similarities = []
+ key_matches = 0 # 关键词精确匹配数量
+
+ for q_word in query_words:
+ if len(q_word.strip()) <= 1: # 忽略单字，减少噪音
+ continue
+
+ word_sims = [word_similarity(q_word, t_word) for t_word in title_words]
+ if word_sims:
+ max_sim = max(word_sims)
+ max_similarities.append(max_sim)
+ if max_sim > 0.85: # 认为是关键词匹配
+ key_matches += 1
+
+ if not max_similarities:
+ return 0.0
+
+ # 计算平均相似度
+ avg_sim = sum(max_similarities) / len(max_similarities)
+
+ # 权重计算: 平均相似度占70%，关键词匹配率占30%
+ key_match_ratio = key_matches / len(query_words) if query_words else 0
+
+ # 标题中包含完整查询短语时给予额外加分
+ exact_bonus = 0.3 if query in title else 0
+
+ return 0.7 * avg_sim + 0.3 * key_match_ratio + exact_bonus
+
+# 添加语义关联词典，用于增强搜索能力
+def load_semantic_mappings():
+ """
+ 加载语义关联映射表，用于增强搜索语义理解
+ 返回包含语义映射关系的字典
+ """
+ # 初始化空字典，所有映射将从配置文件加载
+ mappings = {}
+
+ # 从配置文件加载映射
+ try:
+ config_path = os.path.join(os.path.dirname(__file__), "semantic_config.json")
+ if os.path.exists(config_path):
+ with open(config_path, 'r', encoding='utf-8') as f:
+ mappings = json.load(f)
+ logger.info(f"已从配置文件加载 {len(mappings)} 个语义映射")
+ else:
+ logger.warning(f"语义配置文件不存在: {config_path}")
+ except Exception as e:
+ logger.error(f"加载语义配置文件失败: {e}")
+
+ return mappings
+
+# 初始化语义映射
+SEMANTIC_MAPPINGS = load_semantic_mappings()
+
+def expand_search_keywords(keyword):
+ """
+ 扩展搜索关键词，增加语义关联词
+ """
+ expanded = [keyword]
+
+ # 分词处理
+ words = list(jieba.cut(keyword))
+ logger.info(f"关键词 '{keyword}' 分词结果: {words}") # 记录分词结果
+
+ # 分别对每个分词进行语义扩展
+ for word in words:
+ if word in SEMANTIC_MAPPINGS:
+ # 添加语义关联词
+ mapped_words = SEMANTIC_MAPPINGS[word]
+ expanded.extend(mapped_words)
+ logger.info(f"语义映射: '{word}' -> {mapped_words}")
+
+ # 移除所有特殊处理部分
+ # 不再对任何特定关键词如"越狱"进行特殊处理
+
+ # Word2Vec扩展 - 如果可用，对分词结果进行Word2Vec扩展
+ if WORD2VEC_ENABLED:
+ try:
+ # 使用单独的变量记录原始扩展结果，方便记录日志
+ original_expanded = set(expanded)
+
+ # 首先尝试对整个关键词进行扩展
+ w2v_expanded = set()
+ similar_words = get_similar_words(keyword, topn=3, min_similarity=0.6)
+ w2v_expanded.update(similar_words)
+
+ # 然后对较长的分词进行扩展
+ for word in words:
+ if len(word) > 1: # 忽略单字
+ similar_words = get_similar_words(word, topn=2, min_similarity=0.65)
+ w2v_expanded.update(similar_words)
+
+ # 合并结果
+ expanded.extend(w2v_expanded)
+
+ # 记录日志
+ if w2v_expanded:
+ logger.info(f"Word2Vec扩展: {keyword} -> {list(w2v_expanded)}")
+ except Exception as e:
+ # 出错时记录但不中断搜索流程
+ logger.error(f"Word2Vec扩展失败: {e}")
+ logger.info("将仅使用配置文件中的语义映射")
+
+ # 去重
+ return list(set(expanded))
+
+# 替换原有的calculate_keyword_relevance函数，采用更通用的相关性算法
+def calculate_keyword_relevance(keyword, item):
+ """计算搜索关键词与条目的相关性得分"""
+ title = item.get('title', '')
+ description = item.get('description', '') or ''
+ tags = item.get('tags', '') or ''
+ category = item.get('category', '') or '' # 添加category字段
+
+ # 初始化得分
+ score = 0
+
+ # 1. 精确匹配（最高优先级）
+ if keyword.lower() == title.lower():
+ return 15.0 # 完全匹配给予最高分
+
+ # 2. 标题中精确词匹配
+ title_words = re.findall(r'\b\w+\b', title.lower())
+ if keyword.lower() in title_words:
+ score += 10.0 # 作为独立词完全匹配
+
+ # 3. 标题包含关键词（部分匹配）
+ elif keyword.lower() in title.lower():
+ # 计算关键词所占标题比例
+ match_ratio = len(keyword) / len(title)
+ if match_ratio > 0.5: # 关键词占标题很大比例
+ score += 8.0
+ else:
+ score += 5.0
+
+ # 4. 标题分词匹配
+ keyword_words = list(jieba.cut(keyword))
+ title_jieba_words = list(jieba.cut(title))
+
+ matched_words = 0
+ for k_word in keyword_words:
+ if len(k_word) > 1: # 忽略单字
+ if k_word in title_jieba_words:
+ matched_words += 1
+ else:
+ # 拼音匹配
+ k_pinyin = get_pinyin(k_word)
+ for t_word in title_jieba_words:
+ if get_pinyin(t_word) == k_pinyin:
+ matched_words += 0.8
+ break
+
+ if len(keyword_words) > 0:
+ word_match_ratio = matched_words / len(keyword_words)
+ score += 3.0 * word_match_ratio
+
+ # 5. 拼音相似度
+ keyword_pinyin = get_pinyin(keyword)
+ title_pinyin = get_pinyin(title)
+
+ if keyword_pinyin == title_pinyin:
+ score += 3.5
+ elif keyword_pinyin in title_pinyin:
+ # 计算拼音在标题中的位置影响
+ pos = title_pinyin.find(keyword_pinyin)
+ if pos == 0: # 出现在开头
+ score += 3.0
+ else:
+ score += 2.0
+
+ # 6. 编辑距离相似度
+ try:
+ edit_distance = Levenshtein.distance(keyword.lower(), title.lower())
+ max_len = max(len(keyword), len(title))
+ if max_len > 0:
+ similarity = 1 - (edit_distance / max_len)
+ if similarity > 0.7:
+ score += 1.5 * similarity
+ except:
+ similarity = difflib.SequenceMatcher(None, keyword.lower(), title.lower()).ratio()
+ if similarity > 0.7:
+ score += 1.5 * similarity
+
+ # 7. 中文字符重叠检测 - 修改为仅当重叠2个以上汉字或占比超过40%时才计分
+ if re.search(r'[\u4e00-\u9fff]', keyword) and re.search(r'[\u4e00-\u9fff]', title):
+ cn_chars_keyword = set(re.findall(r'[\u4e00-\u9fff]', keyword))
+ cn_chars_title = set(re.findall(r'[\u4e00-\u9fff]', title))
+
+ # 计算重叠的汉字集合
+ overlapped_chars = cn_chars_keyword & cn_chars_title
+
+ # 仅当重叠汉字数量大于1且占比超过阈值时才计分
+ if len(overlapped_chars) > 1 and len(cn_chars_keyword) > 0:
+ overlap_ratio = len(overlapped_chars) / len(cn_chars_keyword)
+ # 增加重叠比例的阈值要求，防止单个汉字导致的误匹配
+ if overlap_ratio >= 0.4 or len(overlapped_chars) >= 3:
+ score += 2.0 * overlap_ratio
+ # 对于非常低的重叠度，不加分，避免无关内容干扰
+
+ # 记录日志，帮助调试特定案例
+ if keyword == "明日方舟" and "白日梦想家" in title:
+ logger.info(f"'明日方舟'与'{title}'的汉字重叠: {overlapped_chars}, 重叠比例: {len(overlapped_chars)/len(cn_chars_keyword) if cn_chars_keyword else 0}")
+
+ # 8. 序列资源检测（如"功夫熊猫2"是"功夫熊猫"的系列）
+ base_title_match = re.match(r'(.*?)([0-9]+|[一二三四五六七八九十]|：|\:|\s+[0-9]+)', title)
+ if base_title_match:
+ base_title = base_title_match.group(1).strip()
+ if keyword.lower() == base_title.lower():
+ score += 2.0
+
+ # 9. 标签和描述匹配（增加权重）
+ if tags:
+ tags_list = tags.split(',')
+ if keyword in tags_list:
+ score += 1.5 # 提高标签匹配的权重
+ elif any(keyword.lower() in tag.lower() for tag in tags_list):
+ score += 1.0 # 提高部分匹配的权重
+
+ # 描述匹配增强
+ if keyword.lower() in description.lower():
+ score += 1.5 # 提高描述匹配的权重
+
+ # 检查关键词在描述中的位置和上下文
+ pos = description.lower().find(keyword.lower())
+ if pos >= 0 and pos < len(description) / 3:
+ # 关键词出现在描述前1/3部分，可能更重要
+ score += 0.5
+
+ # 考虑分词匹配描述
+ keyword_words = list(jieba.cut(keyword))
+ description_words = list(jieba.cut(description))
+ matched_desc_words = 0
+ for k_word in keyword_words:
+ if len(k_word) > 1 and k_word in description_words:
+ matched_desc_words += 1
+
+ if len(keyword_words) > 0:
+ desc_match_ratio = matched_desc_words / len(keyword_words)
+ score += 1.0 * desc_match_ratio
+
+ # 分类匹配
+ if keyword.lower() in category.lower():
+ score += 1.0
+
+ # 添加语义关联匹配得分
+ # 扩展关键词进行匹配
+ expanded_keywords = expand_search_keywords(keyword)
+ # 检测标题是否包含语义相关词
+ for exp_keyword in expanded_keywords:
+ if exp_keyword != keyword and exp_keyword in title: # 避免重复计算原关键词
+ score += 1.5 # 一般语义关联
+
+ return score
+
+# 创建Flask应用
+app = Flask(__name__)
+CORS(app) # 允许所有跨域请求
+
+# 添加init_word2vec函数
+def init_word2vec():
+ """初始化Word2Vec模型"""
+ try:
+ helper = get_word2vec_helper()
+ if helper.initialized:
+ logger.info(f"Word2Vec模型已成功加载，词汇量: {len(helper.model.index_to_key)}, 向量维度: {helper.model.vector_size}")
+ else:
+ if helper.load_model():
+ logger.info(f"Word2Vec模型加载成功，词汇量: {len(helper.model.index_to_key)}, 向量维度: {helper.model.vector_size}")
+ else:
+ logger.error("Word2Vec模型加载失败")
+ except Exception as e:
+ logger.error(f"初始化Word2Vec出错: {e}")
+
+# 新的初始化方式:
+def initialize_app():
+ """应用初始化函数，替代before_first_request装饰器"""
+ # 修正：使用正确的函数名
+ # 原代码: init_semantic_mapping()
+ # 修正为使用已定义的函数名
+ global SEMANTIC_MAPPINGS
+ SEMANTIC_MAPPINGS = load_semantic_mappings() # 更新全局语义映射变量
+
+ if WORD2VEC_ENABLED:
+ init_word2vec() # 现在这个函数已经定义了
+
+# 在启动应用之前调用初始化函数
+initialize_app()
+
+# 测试路由
+@app.route('/test', methods=['GET'])
+def test():
+ import datetime
+ return jsonify({"message": "服务器正常运行", "timestamp": str(datetime.datetime.now())})
+
+# 获取单个帖子详情的API
+@app.route('/post/<int:post_id>', methods=['GET'])
+def get_post_detail(post_id):
+ """
+ 获取单个帖子详情
+ """
+ logger.info(f"接收到获取帖子详情请求，post_id: {post_id}")
+ conn = get_db_conn()
+ try:
+ with conn.cursor(pymysql.cursors.DictCursor) as cursor:
+ # 联表查询帖子详情，获取分类名和type
+ query = """
+ SELECT
+ p.id,
+ p.title,
+ p.content,
+ p.heat,
+ p.created_at as create_time,
+ p.updated_at as last_active,
+ p.status,
+ p.type,
+ tp.name as category
+ FROM posts p
+ LEFT JOIN topics tp ON p.topic_id = tp.id
+ WHERE p.id = %s
+ """
+ logger.info(f"执行查询: {query} with post_id: {post_id}")
+ cursor.execute(query, (post_id,))
+ post = cursor.fetchone()
+
+ logger.info(f"查询结果: {post}")
+
+ if not post:
+ logger.warning(f"帖子不存在，post_id: {post_id}")
+ return jsonify({"error": "帖子不存在"}), 404
+
+ # 设置默认值
+ post['tags'] = []
+ post['author'] = '匿名用户'
+ if not post.get('category'):
+ post['category'] = '未分类'
+ if not post.get('type'):
+ post['type'] = 'text'
+ # 格式化时间
+ if post['create_time']:
+ post['create_time'] = post['create_time'].strftime('%Y-%m-%d %H:%M:%S')
+ if post['last_active']:
+ post['last_active'] = post['last_active'].strftime('%Y-%m-%d %H:%M:%S')
+
+ logger.info(f"返回帖子详情: {post}")
+ return Response(json.dumps(post, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+ except Exception as e:
+ logger.error(f"获取帖子详情失败: {e}")
+ import traceback
+ traceback.print_exc()
+ return jsonify({"error": "服务器内部错误"}), 500
+ finally:
+ conn.close()
+
+# 搜索功能的API
+@app.route('/search', methods=['POST'])
+def search():
+ """
+ 搜索功能API
+ 请求格式：{
+ "keyword": "关键词",
+ "sort_by": "downloads" | "downloads_asc" | "newest" | "oldest" | "similarity" | "title_asc" | "title_desc",
+ "category": "可选，分类名",
+ "search_mode": "title" | "title_desc" | "tags" | "all" # 可选，默认"title",
+ "tags": ["标签1", "标签2"] # 可选，支持传递多个标签
+ }
+ """
+ if request.content_type != 'application/json':
+ return jsonify({"error": "Content-Type must be application/json"}), 415
+
+ data = request.get_json()
+ keyword = data.get("keyword", "").strip()
+ sort_by = data.get("sort_by", "similarity") # 默认按相似度排序
+ category = data.get("category", None)
+ search_mode = data.get("search_mode", "title")
+ tags = data.get("tags", None) # 支持传递多个标签
+
+ # 校验参数 - 不管什么模式都要求关键词
+ if not (1 <= len(keyword) <= 20):
+ return jsonify({"error": "请输入1-20个字符"}), 400
+
+ # 第一阶段：数据库查询获取候选集
+ results = []
+ conn = get_db_conn()
+ try:
+ with conn.cursor(pymysql.cursors.DictCursor) as cursor:
+ # 首先尝试查询完全匹配的结果
+ exact_query = f"""
+ SELECT id, title, topic_id, heat, created_at, content
+ FROM posts
+ WHERE title = %s
+ """
+ cursor.execute(exact_query, (keyword,))
+ exact_matches = cursor.fetchall() or [] # 确保返回列表而非元组
+
+ # 扩展关键词，增加语义关联词
+ expanded_keywords = expand_search_keywords(keyword)
+ logger.info(f"扩展后的关键词: {expanded_keywords}") # 调试信息
+
+ # 构建查询条件
+ conditions = []
+ params = []
+
+ # 标题匹配 - 所有搜索模式都匹配title
+ conditions.append("title LIKE %s")
+ params.append(f"%{keyword}%")
+
+ # 为扩展关键词添加标题匹配条件
+ for exp_keyword in expanded_keywords:
+ if exp_keyword != keyword: # 避免重复原关键词
+ conditions.append("title LIKE %s")
+ params.append(f"%{exp_keyword}%")
+
+ # 描述匹配
+ if search_mode in ["title_desc", "all"]:
+ # 原始关键词匹配描述
+ conditions.append("content LIKE %s")
+ params.append(f"%{keyword}%")
+
+ # 扩展关键词匹配描述
+ for exp_keyword in expanded_keywords:
+ if exp_keyword != keyword:
+ conditions.append("content LIKE %s")
+ params.append(f"%{exp_keyword}%")
+
+ # 标签匹配
+ # 暂不处理，后续join实现
+
+ # 分类匹配 - 仅在all模式下
+ if search_mode == "all":
+ # 原始关键词匹配分类
+ conditions.append("topic_id LIKE %s")
+ params.append(f"%{keyword}%")
+
+ # 扩展关键词匹配分类
+ for exp_keyword in expanded_keywords:
+ if exp_keyword != keyword:
+ conditions.append("topic_id LIKE %s")
+ params.append(f"%{exp_keyword}%")
+
+ # 构建SQL查询
+ if conditions:
+ where_clause = " OR ".join(conditions)
+ logger.info(f"搜索条件: {where_clause}")
+ logger.info(f"参数列表: {params}")
+
+ if category:
+ where_clause = f"({where_clause}) AND topic_id=%s"
+ params.append(category)
+
+ sql = f"""
+ SELECT p.id, p.title, tp.name as category, p.heat, p.created_at, p.content,
+ GROUP_CONCAT(t.name) as tags
+ FROM posts p
+ LEFT JOIN post_tags pt ON p.id = pt.post_id
+ LEFT JOIN tags t ON pt.tag_id = t.id
+ LEFT JOIN topics tp ON p.topic_id = tp.id
+ WHERE {where_clause}
+ GROUP BY p.id
+ LIMIT 500
+ """
+
+ cursor.execute(sql, params)
+ expanded_results = cursor.fetchall()
+ logger.info(f"数据库返回记录数: {len(expanded_results) if expanded_results else 0}")
+ else:
+ expanded_results = []
+
+ # 如果扩展查询和精确匹配都没有结果，获取全部记录进行相关性计算
+ if not expanded_results and not exact_matches:
+ sql = "SELECT p.id, p.title, tp.name as category, p.heat, p.created_at, p.content, GROUP_CONCAT(t.name) as tags FROM posts p LEFT JOIN post_tags pt ON p.id = pt.post_id LEFT JOIN tags t ON pt.tag_id = t.id LEFT JOIN topics tp ON p.topic_id = tp.id"
+ if category:
+ sql += " WHERE p.topic_id=%s"
+ category_params = [category]
+ cursor.execute(sql + " GROUP BY p.id", category_params)
+ else:
+ cursor.execute(sql + " GROUP BY p.id")
+
+ all_results = cursor.fetchall() or [] # 确保返回列表
+ else:
+ if isinstance(exact_matches, tuple):
+ exact_matches = list(exact_matches)
+ if isinstance(expanded_results, tuple):
+ expanded_results = list(expanded_results)
+ all_results = expanded_results + exact_matches
+
+ # 对所有结果使用相关性计算规则
+ scored_results = []
+ for item in all_results:
+ # 计算相关性得分
+ relevance_score = calculate_keyword_relevance(keyword, item)
+
+ # 降低相关性阈值，确保更多结果被保留 (从0.5改为0.1)
+ if relevance_score > 0.1:
+ item['relevance_score'] = relevance_score
+ scored_results.append(item)
+ logger.info(f"匹配项: {item['title']}, 相关性得分: {relevance_score}")
+
+ # 按相关性得分排序
+ scored_results.sort(key=lambda x: x.get('relevance_score', 0), reverse=True)
+
+ # 确保精确匹配的结果置顶
+ if exact_matches:
+ for exact_match in exact_matches:
+ exact_match['relevance_score'] = 20.0 # 超高分确保置顶
+
+ # 移除scored_results中已经存在于exact_matches的项
+ exact_ids = {item['id'] for item in exact_matches}
+ scored_results = [item for item in scored_results if item['id'] not in exact_ids]
+
+ # 合并两个结果集
+ results = exact_matches + scored_results
+ else:
+ results = scored_results
+
+ # 限制返回结果数量
+ results = results[:50]
+
+ except Exception as e:
+ logger.error(f"搜索出错: {e}")
+ import traceback
+ traceback.print_exc()
+ return jsonify({"error": "搜索系统异常，请稍后再试"}), 500
+ finally:
+ conn.close()
+
+ # 第二阶段：根据指定方式排序
+ if results:
+ if sort_by == "similarity" or not sort_by:
+ # 保持按相关性得分排序，已经排好了
+ pass
+ elif sort_by == "downloads":
+ results.sort(key=lambda x: x.get("download_count", 0), reverse=True)
+ elif sort_by == "downloads_asc":
+ results.sort(key=lambda x: x.get("download_count", 0))
+ elif sort_by == "newest":
+ results.sort(key=lambda x: x.get("create_time", ""), reverse=True)
+ elif sort_by == "oldest":
+ results.sort(key=lambda x: x.get("create_time", ""))
+ elif sort_by == "title_asc":
+ results.sort(key=lambda x: x.get("title", ""))
+ elif sort_by == "title_desc":
+ results.sort(key=lambda x: x.get("title", ""), reverse=True)
+
+ # 最终处理：清理不需要返回的字段，并将 datetime 转为字符串
+ for item in results:
+ item.pop("description", None)
+ item.pop("tags", None)
+ item.pop("relevance_score", None)
+ for k, v in item.items():
+ if hasattr(v, 'isoformat'):
+ item[k] = v.isoformat(sep=' ', timespec='seconds')
+
+ return Response(json.dumps({"results": results}, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+
+# 推荐功能的API
+@app.route('/recommend_tags', methods=['POST'])
+def recommend_tags():
+ """
+ 推荐功能API
+ 请求格式：{
+ "user_id": "user1",
+ "tags": ["标签1", "标签2"] # 可为空
+ }
+ """
+ if request.content_type != 'application/json':
+ return jsonify({"error": "Content-Type must be application/json"}), 415
+
+ data = request.get_json()
+ user_id = data.get("user_id")
+ tags = set(data.get("tags", []))
+
+ # 查询用户已保存的兴趣标签
+ user_tags = set()
+ if user_id:
+ conn = get_db_conn()
+ try:
+ with conn.cursor() as cursor:
+ cursor.execute("SELECT t.name FROM user_tags ut JOIN tags t ON ut.tag_id = t.id WHERE ut.user_id=%s", (user_id,))
+ user_tags = set(row[0] for row in cursor.fetchall())
+ finally:
+ conn.close()
+
+ # 合并前端传递的tags和用户兴趣标签
+ all_tags = list(tags | user_tags)
+
+ if not all_tags:
+ return Response(json.dumps({"error": "暂无推荐结果"}, ensure_ascii=False), mimetype='application/json; charset=utf-8'), 200
+
+ conn = get_db_conn()
+ try:
+ with conn.cursor(pymysql.cursors.DictCursor) as cursor:
+ # 优先用tags字段匹配
+ # 先查找所有tag_id
+ tag_ids = []
+ for tag in all_tags:
+ cursor.execute("SELECT id FROM tags WHERE name=%s", (tag,))
+ row = cursor.fetchone()
+ if row:
+ tag_ids.append(row['id'])
+ if not tag_ids:
+ return Response(json.dumps({"error": "暂无推荐结果"}, ensure_ascii=False), mimetype='application/json; charset=utf-8'), 200
+ tag_placeholders = ','.join(['%s'] * len(tag_ids))
+ sql = f"""
+ SELECT p.id, p.title, tp.name as category, p.heat,
+ GROUP_CONCAT(tg.name) as tags
+ FROM posts p
+ LEFT JOIN post_tags pt ON p.id = pt.post_id
+ LEFT JOIN tags tg ON pt.tag_id = tg.id
+ LEFT JOIN topics tp ON p.topic_id = tp.id
+ WHERE pt.tag_id IN ({tag_placeholders})
+ GROUP BY p.id
+ LIMIT 50
+ """
+ cursor.execute(sql, tuple(tag_ids))
+ results = cursor.fetchall()
+ # 若无结果，回退title/content模糊匹配
+ if not results:
+ or_conditions = []
+ params = []
+ for tag in all_tags:
+ or_conditions.append("p.title LIKE %s OR p.content LIKE %s")
+ params.extend(['%' + tag + '%', '%' + tag + '%'])
+ where_clause = ' OR '.join(or_conditions)
+ sql = f"""
+ SELECT p.id, p.title, tp.name as category, p.heat,
+ GROUP_CONCAT(tg.name) as tags
+ FROM posts p
+ LEFT JOIN post_tags pt ON p.id = pt.post_id
+ LEFT JOIN tags tg ON pt.tag_id = tg.id
+ LEFT JOIN topics tp ON p.topic_id = tp.id
+ WHERE {where_clause}
+ GROUP BY p.id
+ LIMIT 50
+ """
+ cursor.execute(sql, tuple(params))
+ results = cursor.fetchall()
+ finally:
+ conn.close()
+
+ if not results:
+ return Response(json.dumps({"error": "暂无推荐结果"}, ensure_ascii=False), mimetype='application/json; charset=utf-8'), 200
+
+ return Response(json.dumps({"recommendations": results}, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+
+# 用户兴趣标签管理API（可选）
+@app.route('/tags', methods=['POST', 'GET', 'DELETE'])
+def user_tags():
+ """
+ POST: 添加用户兴趣标签
+ GET: 查询用户兴趣标签
+ DELETE: 删除用户兴趣标签
+ """
+ if request.method == 'POST':
+ if request.content_type != 'application/json':
+ return jsonify({"error": "Content-Type must be application/json"}), 415
+ data = request.get_json()
+ user_id = data.get("user_id")
+ tags = data.get("tags", [])
+
+ if not user_id:
+ return jsonify({"error": "用户ID不能为空"}), 400
+
+ # 确保标签列表格式正确
+ if isinstance(tags, str):
+ tags = [tag.strip() for tag in tags.split(',') if tag.strip()]
+
+ if not tags:
+ return jsonify({"error": "标签不能为空"}), 400
+
+ conn = get_db_conn()
+ try:
+ with conn.cursor() as cursor:
+ # 添加用户标签
+ for tag in tags:
+ # 先查找tag_id
+ cursor.execute("SELECT id FROM tags WHERE name=%s", (tag,))
+ tag_row = cursor.fetchone()
+ if tag_row:
+ tag_id = tag_row[0]
+ cursor.execute("REPLACE INTO user_tags (user_id, tag_id) VALUES (%s, %s)", (user_id, tag_id))
+ conn.commit()
+ # 返回更新后的标签列表
+ cursor.execute("SELECT t.name FROM user_tags ut JOIN tags t ON ut.tag_id = t.id WHERE ut.user_id=%s", (user_id,))
+ updated_tags = [row[0] for row in cursor.fetchall()]
+ finally:
+ conn.close()
+ return Response(json.dumps({"msg": "添加成功", "tags": updated_tags}, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+ elif request.method == 'DELETE':
+ if request.content_type != 'application/json':
+ return jsonify({"error": "Content-Type must be application/json"}), 415
+ data = request.get_json()
+ user_id = data.get("user_id")
+ tags = data.get("tags", [])
+ if not user_id:
+ return jsonify({"error": "用户ID不能为空"}), 400
+ if not tags:
+ return jsonify({"error": "标签不能为空"}), 400
+
+ conn = get_db_conn()
+ try:
+ with conn.cursor() as cursor:
+ for tag in tags:
+ cursor.execute("SELECT id FROM tags WHERE name=%s", (tag,))
+ tag_row = cursor.fetchone()
+ if tag_row:
+ tag_id = tag_row[0]
+ cursor.execute("DELETE FROM user_tags WHERE user_id=%s AND tag_id=%s", (user_id, tag_id))
+ conn.commit()
+ cursor.execute("SELECT t.name FROM user_tags ut JOIN tags t ON ut.tag_id = t.id WHERE ut.user_id=%s", (user_id,))
+ remaining_tags = [row[0] for row in cursor.fetchall()]
+ finally:
+ conn.close()
+ return Response(json.dumps({"msg": "删除成功", "tags": remaining_tags}, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+ else: # GET 请求
+ user_id = request.args.get("user_id")
+ if not user_id:
+ return jsonify({"error": "用户ID不能为空"}), 400
+ conn = get_db_conn()
+ try:
+ with conn.cursor() as cursor:
+ cursor.execute("SELECT t.name FROM user_tags ut JOIN tags t ON ut.tag_id = t.id WHERE ut.user_id=%s", (user_id,))
+ tags = [row[0] for row in cursor.fetchall()]
+ finally:
+ conn.close()
+ return Response(json.dumps({"tags": tags}, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+
+# 添加/user_tags路由作为/tags的别名
+@app.route('/user_tags', methods=['POST', 'GET', 'DELETE'])
+def user_tags_alias():
+ """
+ /user_tags路由 - 作为/tags路由的别名
+ POST: 添加用户兴趣标签
+ GET: 查询用户兴趣标签
+ DELETE: 删除用户兴趣标签
+ """
+ return user_tags()
+
+# 基于用户的协同过滤推荐API
+@app.route('/user_based_recommend', methods=['POST'])
+def user_based_recommend():
+ """
+ 基于用户的协同过滤推荐API
+ 请求格式：{
+ "user_id": "user1",
+ "top_n": 5
+ }
+ """
+ if request.content_type != 'application/json':
+ return jsonify({"error": "Content-Type must be application/json"}), 415
+
+ data = request.get_json()
+ user_id = data.get("user_id")
+ top_n = int(data.get("top_n", 5))
+
+ if not user_id:
+ return jsonify({"error": "用户ID不能为空"}), 400
+
+ conn = get_db_conn()
+ try:
+ with conn.cursor(pymysql.cursors.DictCursor) as cursor:
+ # 1. 检查用户是否存在下载记录（收藏或浏览）
+ cursor.execute("""
+ SELECT COUNT(*) as count
+ FROM behaviors
+ WHERE user_id = %s AND type IN ('favorite', 'view')
+ """, (user_id,))
+ result = cursor.fetchone()
+ user_download_count = result['count'] if result else 0
+
+ logger.info(f"用户 {user_id} 下载记录数: {user_download_count}")
+
+ # 如果用户没有足够的行为数据，返回基于热度的推荐
+ if user_download_count < 3:
+ logger.info(f"用户 {user_id} 下载记录不足，返回热门推荐")
+ cursor.execute("""
+ SELECT p.id, p.title, tp.name as category, p.heat
+ FROM posts p
+ LEFT JOIN topics tp ON p.topic_id = tp.id
+ ORDER BY p.heat DESC
+ LIMIT %s
+ """, (top_n,))
+ popular_seeds = cursor.fetchall()
+ return Response(json.dumps({"recommendations": popular_seeds, "type": "popular"}, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+
+ # 2. 获取用户已下载（收藏/浏览）的帖子
+ cursor.execute("""
+ SELECT post_id
+ FROM behaviors
+ WHERE user_id = %s AND type IN ('favorite', 'view')
+ """, (user_id,))
+ user_seeds = set(row['post_id'] for row in cursor.fetchall())
+ logger.info(f"用户 {user_id} 已下载种子: {user_seeds}")
+
+ # 3. 获取所有用户-帖子下载（收藏/浏览）矩阵
+ cursor.execute("""
+ SELECT user_id, post_id
+ FROM behaviors
+ WHERE created_at > DATE_SUB(NOW(), INTERVAL 3 MONTH)
+ AND user_id <> %s AND type IN ('favorite', 'view')
+ """, (user_id,))
+ download_records = cursor.fetchall()
+
+ if not download_records:
+ logger.info(f"没有其他用户的下载记录，返回热门推荐")
+ cursor.execute("""
+ SELECT p.id, p.title, tp.name as category, p.heat
+ FROM posts p
+ LEFT JOIN topics tp ON p.topic_id = tp.id
+ ORDER BY p.heat DESC
+ LIMIT %s
+ """, (top_n,))
+ popular_seeds = cursor.fetchall()
+ return Response(json.dumps({"recommendations": popular_seeds, "type": "popular"}, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+
+ # 构建用户-物品矩阵
+ user_item_matrix = {}
+ for record in download_records:
+ uid = record['user_id']
+ sid = record['post_id']
+ if uid not in user_item_matrix:
+ user_item_matrix[uid] = set()
+ user_item_matrix[uid].add(sid)
+
+ # 4. 计算用户相似度
+ similar_users = []
+ for other_id, other_seeds in user_item_matrix.items():
+ if other_id == user_id:
+ continue
+ intersection = len(user_seeds.intersection(other_seeds))
+ union = len(user_seeds.union(other_seeds))
+ if union > 0 and intersection > 0:
+ similarity = intersection / union
+ similar_users.append((other_id, similarity, other_seeds))
+ logger.info(f"找到 {len(similar_users)} 个相似用户")
+ similar_users.sort(key=lambda x: x[1], reverse=True)
+ similar_users = similar_users[:5]
+ # 5. 基于相似用户推荐帖子
+ candidate_seeds = {}
+ for similar_user, similarity, seeds in similar_users:
+ logger.info(f"相似用户 {similar_user}, 相似度 {similarity}")
+ for post_id in seeds:
+ if post_id not in user_seeds:
+ if post_id not in candidate_seeds:
+ candidate_seeds[post_id] = 0
+ candidate_seeds[post_id] += similarity
+ if not candidate_seeds:
+ logger.info(f"没有找到候选种子，返回热门推荐")
+ cursor.execute("""
+ SELECT p.id, p.title, tp.name as category, p.heat
+ FROM posts p
+ LEFT JOIN topics tp ON p.topic_id = tp.id
+ ORDER BY p.heat DESC
+ LIMIT %s
+ """, (top_n,))
+ popular_seeds = cursor.fetchall()
+ return Response(json.dumps({"recommendations": popular_seeds, "type": "popular"}, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+ # 6. 获取推荐帖子的详细信息
+ recommended_seeds = sorted(candidate_seeds.items(), key=lambda x: x[1], reverse=True)[:top_n]
+ post_ids = [post_id for post_id, _ in recommended_seeds]
+ format_strings = ','.join(['%s'] * len(post_ids))
+ cursor.execute(f"""
+ SELECT p.id, p.title, tp.name as category, p.heat
+ FROM posts p
+ LEFT JOIN topics tp ON p.topic_id = tp.id
+ WHERE p.id IN ({format_strings})
+ """, tuple(post_ids))
+ result_seeds = cursor.fetchall()
+ seed_score_map = {post_id: score for post_id, score in recommended_seeds}
+ result_seeds.sort(key=lambda x: seed_score_map.get(x['id'], 0), reverse=True)
+ logger.info(f"返回 {len(result_seeds)} 个基于协同过滤的推荐")
+ return Response(json.dumps({"recommendations": result_seeds, "type": "collaborative"}, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+ except Exception as e:
+ logger.error(f"推荐系统错误: {e}")
+ import traceback
+ traceback.print_exc()
+ return Response(json.dumps({"error": "推荐系统异常，请稍后再试", "details": str(e)}, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+ finally:
+ conn.close()
+@app.route('/word2vec_status', methods=['GET'])
+def word2vec_status():
+ """
+ 检查Word2Vec模型状态
+ 返回模型是否加载、词汇量等信息
+ """
+ if not WORD2VEC_ENABLED:
+ return Response(json.dumps({
+ "enabled": False,
+ "message": "Word2Vec功能未启用"
+ }, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+ try:
+ helper = get_word2vec_helper()
+ status = {
+ "enabled": WORD2VEC_ENABLED,
+ "initialized": helper.initialized,
+ "vocab_size": len(helper.model.index_to_key) if helper.model else 0,
+ "vector_size": helper.model.vector_size if helper.model else 0
+ }
+
+ # 测试几个常用词的相似词，展示模型效果
+ test_results = {}
+ test_words = ["电影", "动作", "科幻", "动漫", "游戏"]
+ for word in test_words:
+ similar_words = helper.get_similar_words(word, topn=5)
+ test_results[word] = similar_words
+
+ status["test_results"] = test_results
+ return Response(json.dumps(status, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+ except Exception as e:
+ return Response(json.dumps({
+ "enabled": WORD2VEC_ENABLED,
+ "initialized": False,
+ "error": str(e)
+ }, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+
+# 添加一个临时诊断端点
+@app.route('/debug_search', methods=['POST'])
+def debug_search():
+ """临时的调试端点，用于检查数据库中的记录"""
+ if request.content_type != 'application/json':
+ return jsonify({"error": "Content-Type must be application/json"}), 415
+
+ data = request.get_json()
+ keyword = data.get("keyword", "").strip()
+
+ conn = get_db_conn()
+ try:
+ with conn.cursor(pymysql.cursors.DictCursor) as cursor:
+ # 尝试查询包含特定词的所有记录
+ queries = [
+ ("标题中包含关键词", f"SELECT seed_id, title, description, tags FROM pt_seed WHERE title LIKE '%{keyword}%' LIMIT 10"),
+ ("描述中包含关键词", f"SELECT seed_id, title, description, tags FROM pt_seed WHERE description LIKE '%{keyword}%' LIMIT 10"),
+ ("标签中包含关键词", f"SELECT seed_id, title, description, tags FROM pt_seed WHERE FIND_IN_SET('{keyword}', tags) LIMIT 10"),
+ ("肖申克的救赎", "SELECT seed_id, title, description, tags FROM pt_seed WHERE title = '肖申克的救赎'")
+ ]
+
+ results = {}
+ for query_name, query in queries:
+ cursor.execute(query)
+ results[query_name] = cursor.fetchall()
+
+ return Response(json.dumps(results, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+ finally:
+ conn.close()
+
+"""
+接口本地测试方法（可直接运行main_online.py后用curl或Postman测试）：
+
+1. 搜索接口
+curl -X POST http://127.0.0.1:5000/search -H "Content-Type: application/json" -d '{"keyword":"电影","sort_by":"downloads"}'
+
+2. 标签推荐接口
+curl -X POST http://127.0.0.1:5000/recommend_tags -H "Content-Type: application/json" -d '{"user_id":"1","tags":["动作","科幻"]}'
+
+3. 用户兴趣标签管理（添加标签）
+curl -X POST http://127.0.0.1:5000/user_tags -H "Content-Type: application/json" -d '{"user_id":"1","tags":["动作","科幻"]}'
+
+4. 用户兴趣标签管理（查询标签）
+curl "http://127.0.0.1:5000/user_tags?user_id=1"
+
+5. 用户兴趣标签管理（删除标签）
+curl -X DELETE http://127.0.0.1:5000/user_tags -H "Content-Type: application/json" -d '{"user_id":"1","tags":["动作","科幻"]}'
+
+6. 协同过滤推荐
+curl -X POST http://127.0.0.1:5000/user_based_recommend -H "Content-Type: application/json" -d '{"user_id":"user1","top_n":3}'
+
+7. Word2Vec状态检查
+curl "http://127.0.0.1:5000/word2vec_status"
+
+8. 调试接口（临时）
+curl -X POST http://127.0.0.1:5000/debug_search -H "Content-Type: application/json" -d '{"keyword":"电影"}'
+
+所有接口均可用Postman按上述参数测试。
+"""
+
+if __name__ == "__main__":
+ try:
+ logger.info("搜索推荐服务启动中...")
+ app.run(host="0.0.0.0", port=5000)
+ except Exception as e:
+ logger.error(f"启动异常: {e}")
+ import traceback
+ traceback.print_exc()
diff --git a/Merge/back_jwlll/config.py b/Merge/back_jwlll/config.py
new file mode 100644
index 0000000..77cb8dc
--- /dev/null
+++ b/Merge/back_jwlll/config.py
@@ -0,0 +1,38 @@
+# JWLLL 搜索推荐服务配置
+
+# 数据库配置
+DB_CONFIG = {
+ "host": "10.126.59.25",
+ "port": 3306,
+ "user": "root",
+ "password": "123456",
+ "database": "redbook",
+ "charset": "utf8mb4"
+}
+
+# 服务器配置
+SERVER_CONFIG = {
+ "host": "127.0.0.1",
+ "port": 5000,
+ "debug": True
+}
+
+# Word2Vec 模型配置
+WORD2VEC_CONFIG = {
+ "model_path": "models/chinese_word2vec.bin",
+ "enabled": True # 如果没有模型文件，设置为 False
+}
+
+# 搜索推荐配置
+SEARCH_CONFIG = {
+ "default_user_id": "3",
+ "default_tags": ["美食", "影视", "穿搭"],
+ "max_results": 50,
+ "similarity_threshold": 0.1
+}
+
+# 日志配置
+LOGGING_CONFIG = {
+ "level": "INFO",
+ "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+}
diff --git a/Merge/back_jwlll/requirements.txt b/Merge/back_jwlll/requirements.txt
new file mode 100644
index 0000000..6e7bd22
--- /dev/null
+++ b/Merge/back_jwlll/requirements.txt
@@ -0,0 +1,9 @@
+Flask==2.3.3
+pymysql==1.1.0
+scikit-learn==1.3.2
+jieba==0.42.1
+pypinyin==0.49.0
+flask-cors==4.0.0
+numpy==1.24.4
+Levenshtein==0.23.0
+gensim==4.3.2
diff --git a/Merge/back_jwlll/semantic_config.json b/Merge/back_jwlll/semantic_config.json
new file mode 100644
index 0000000..f5e34d3
--- /dev/null
+++ b/Merge/back_jwlll/semantic_config.json
@@ -0,0 +1,77 @@
+{
+ "国宝": ["熊猫", "大熊猫", "功夫熊猫", "四川", "成都", "保护动物"],
+ "熊猫": ["国宝", "大熊猫", "功夫熊猫", "竹子", "四川", "黑白"],
+ "功夫": ["武术", "格斗", "武打", "功夫熊猫", "李小龙", "成龙", "太极", "截拳道", "中国功夫"],
+
+ "梦": ["梦想", "梦境", "白日梦", "白日梦想家", "潜意识", "睡眠", "做梦"],
+ "白日梦": ["梦想", "幻想", "白日梦想家", "想象", "憧憬"],
+
+ "魔戒": ["指环王", "魔戒再现", "中土世界", "霍比特人", "精灵", "魔法", "奇幻"],
+ "指环": ["魔戒", "指环王", "戒指", "魔戒再现", "首饰"],
+ "中土世界": ["魔戒", "指环王", "霍比特人", "精灵", "矮人", "奇幻"],
+
+ "漫威": ["复仇者", "钢铁侠", "蜘蛛侠", "美国队长", "雷神", "绿巨人", "黑寡妇", "惊奇队长", "超级英雄", "漫画"],
+ "钢铁侠": ["托尼斯塔克", "钢铁战衣", "贾维斯", "复仇者", "漫威", "超级英雄"],
+ "蜘蛛侠": ["彼得帕克", "蜘蛛", "纽约", "漫威", "超级英雄", "蜘蛛感应"],
+
+ "DC": ["蝙蝠侠", "超人", "神奇女侠", "正义联盟", "闪电侠", "水行侠", "超级英雄", "漫画"],
+ "蝙蝠侠": ["布鲁斯韦恩", "高谭市", "小丑", "罗宾", "DC", "超级英雄"],
+ "超人": ["克拉克肯特", "氪星", "莱克斯卢瑟", "超能力", "DC", "超级英雄"],
+
+ "星球大战": ["星战", "原力", "天行者", "达斯维达", "尤达", "绝地武士", "光剑", "帝国", "科幻"],
+ "原力": ["绝地武士", "星球大战", "天行者", "尤达", "光剑", "西斯", "科幻"],
+
+ "哈利波特": ["魔法", "霍格沃茨", "魔杖", "魔法石", "伏地魔", "巫师", "奇幻", "魔幻"],
+ "魔法": ["巫师", "法术", "咒语", "哈利波特", "霍格沃茨", "魔杖", "奇幻", "魔幻"],
+
+ "科幻": ["未来", "太空", "星际", "外星人", "人工智能", "机器人", "时空", "星球大战", "星际穿越"],
+ "太空": ["宇宙", "星球", "卫星", "宇航员", "航天", "科幻", "星际", "外太空"],
+ "人工智能": ["AI", "机器学习", "深度学习", "神经网络", "机器人", "算法", "科技", "科幻"],
+
+ "动作": ["武打", "格斗", "功夫", "特技", "追逐", "冒险", "刺激", "爆破"],
+ "冒险": ["探险", "奇遇", "探索", "未知", "旅程", "冒险家", "刺激", "危险"],
+ "奇幻": ["魔法", "魔幻", "神话", "异世界", "精灵", "龙", "魔戒", "哈利波特"],
+
+ "悬疑": ["推理", "谜题", "侦探", "神秘", "悬念", "惊悚", "犯罪", "悬疑片"],
+ "推理": ["侦探", "线索", "谜题", "破案", "悬疑", "逻辑", "智力", "悬疑片"],
+
+ "恐怖": ["惊悚", "鬼怪", "恶魔", "惊吓", "血腥", "恐怖片", "心理恐惧", "超自然"],
+ "鬼怪": ["幽灵", "鬼魂", "妖怪", "超自然", "恐怖", "惊悚", "诡异", "恐怖片"],
+
+ "喜剧": ["搞笑", "幽默", "欢乐", "笑声", "喜剧片", "滑稽", "逗乐", "喜剧演员"],
+ "搞笑": ["幽默", "笑话", "喜剧", "逗乐", "滑稽", "欢乐", "喜剧片", "喜剧演员"],
+
+ "战争": ["军事", "战场", "士兵", "军队", "战役", "武器", "战争片", "历史战争"],
+ "军事": ["军队", "武器", "战争", "军人", "战略", "战术", "国防", "军事片"],
+
+ "剧情": ["情节", "故事", "叙事", "人物", "感人", "真实", "戏剧性", "剧情片"],
+ "历史": ["古代", "历史事件", "历史人物", "朝代", "文明", "历史片", "传记", "纪实"],
+
+ "纪录片": ["真实记录", "纪实", "历史", "自然", "科学", "社会", "文化", "探索"],
+ "动画": ["卡通", "动漫", "动画片", "动画电影", "CG", "3D动画", "手绘", "二次元"],
+
+ "音乐": ["歌曲", "旋律", "节奏", "乐器", "演唱", "音乐家", "音乐剧", "音乐会"],
+ "歌曲": ["歌词", "唱歌", "歌手", "流行歌曲", "音乐", "专辑", "单曲", "MV"],
+
+ "爱情": ["恋爱", "浪漫", "情侣", "爱情故事", "爱情片", "感情", "爱意", "约会"],
+ "浪漫": ["爱情", "情感", "温馨", "甜蜜", "爱意", "爱情片", "情侣", "表白"],
+
+ "Netflix": ["网飞", "流媒体", "自制剧", "电视剧", "纸牌屋", "怪奇物语", "王冠", "订阅"],
+ "迪士尼": ["米老鼠", "唐老鸭", "公主", "动画", "迪士尼乐园", "皮克斯", "童话", "漫威"],
+
+ "游戏": ["电子游戏", "游戏机", "主机游戏", "PC游戏", "手游", "网游", "单机", "多人游戏"],
+ "动漫": ["日本动画", "漫画", "二次元", "动画", "动画片", "ACGN", "宅文化", "御宅族"],
+
+ "日本": ["东京", "京都", "大阪", "日本文化", "日本料理", "樱花", "动漫", "武士道"],
+ "美国": ["纽约", "洛杉矶", "华盛顿", "美国文化", "好莱坞", "自由女神像", "美式"],
+
+ "教育": ["学习", "知识", "课程", "教学", "学校", "教科书", "老师", "学生"],
+ "技术": ["科技", "工程", "编程", "软件", "硬件", "开发", "技术革新", "IT"],
+
+ "监狱": ["越狱", "囚犯", "牢房", "服刑", "狱警"],
+ "越狱": ["监狱", "囚犯", "逃狱", "越狱计划", "监狱逃脱"],
+
+ "肥皂": ["手工皂", "皂"],
+ "手工皂": ["肥皂", "皂"],
+ "皂": ["肥皂", "手工皂"]
+}
diff --git a/Merge/back_jwlll/word2vec_helper.py b/Merge/back_jwlll/word2vec_helper.py
new file mode 100644
index 0000000..ecd1a72
--- /dev/null
+++ b/Merge/back_jwlll/word2vec_helper.py
@@ -0,0 +1,279 @@
+# word2vec_helper.py
+# Word2Vec模型加载与使用的辅助模块
+
+import os
+import numpy as np
+from gensim.models import KeyedVectors, Word2Vec
+import jieba
+import logging
+import time
+
+# 设置日志
+logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
+
+class Word2VecHelper:
+ def __init__(self, model_path=None):
+ """
+ 初始化Word2Vec辅助类
+
+ 参数:
+ model_path: 预训练模型路径，支持word2vec格式和二进制格式
+ 如果为None，将使用默认路径或尝试下载小型模型
+ """
+ self.model = None
+
+ # 更改默认模型路径和备用选项
+ if model_path:
+ self.model_path = model_path
+ else:
+ # 首选路径 - 大型腾讯模型
+ primary_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
+ "models", "chinese_word2vec.bin")
+
+ # 备用路径 - 小型模型
+ backup_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
+ "models", "chinese_word2vec_small.bin")
+
+ if os.path.exists(primary_path):
+ self.model_path = primary_path
+ elif os.path.exists(backup_path):
+ self.model_path = backup_path
+ else:
+ # 如果都不存在，可以尝试自动下载小模型
+ self.model_path = primary_path
+ self._try_download_small_model()
+
+ self.initialized = False
+ # 缓存查询结果，提高性能
+ self.similarity_cache = {}
+ self.similar_words_cache = {}
+
+ def _try_download_small_model(self):
+ """尝试下载小型词向量模型作为备用选项"""
+ try:
+ import gensim.downloader as api
+ logging.info("尝试下载小型中文词向量模型...")
+
+ # 创建模型目录
+ os.makedirs(os.path.dirname(self.model_path), exist_ok=True)
+
+ # 尝试下载fastText的小型中文模型
+ small_model = api.load("fasttext-wiki-news-subwords-300")
+ small_model.save(self.model_path.replace(".bin", "_small.bin"))
+ logging.info(f"小型模型已下载并保存到 {self.model_path}")
+ except Exception as e:
+ logging.error(f"无法下载备用模型: {e}")
+
+ def load_model(self):
+ """加载Word2Vec模型"""
+ try:
+ start_time = time.time()
+ logging.info(f"开始加载Word2Vec模型: {self.model_path}")
+
+ # 判断文件扩展名，选择合适的加载方式
+ if self.model_path.endswith('.bin'):
+ # 加载二进制格式的模型
+ self.model = KeyedVectors.load_word2vec_format(self.model_path, binary=True)
+ else:
+ # 加载文本格式的模型或gensim模型
+ self.model = Word2Vec.load(self.model_path).wv
+
+ self.initialized = True
+ logging.info(f"Word2Vec模型加载完成，耗时 {time.time() - start_time:.2f} 秒")
+ logging.info(f"词向量维度: {self.model.vector_size}")
+ logging.info(f"词汇表大小: {len(self.model.index_to_key)}")
+ return True
+ except Exception as e:
+ logging.error(f"加载Word2Vec模型失败: {e}")
+ self.initialized = False
+ return False
+
+ def ensure_initialized(self):
+ """确保模型已初始化"""
+ if not self.initialized:
+ return self.load_model()
+ return True
+
+ def get_similar_words(self, word, topn=10, min_similarity=0.5):
+ """
+ 获取与给定词语最相似的词语列表
+
+ 参数:
+ word: 输入词语
+ topn: 返回相似词的数量
+ min_similarity: 最小相似度阈值
+ 返回:
+ 相似词列表，如果词不存在或模型未加载则返回空列表
+ """
+ if not self.ensure_initialized():
+ return []
+
+ # 检查缓存
+ cache_key = f"{word}_{topn}_{min_similarity}"
+ if cache_key in self.similar_words_cache:
+ return self.similar_words_cache[cache_key]
+
+ try:
+ # 如果词不在词汇表中，进行分词处理
+ if word not in self.model.key_to_index:
+ # 对中文词进行分词，然后查找每个子词的相似词
+ word_parts = list(jieba.cut(word))
+
+ if not word_parts:
+ return []
+
+ # 如果存在多个子词，找到存在于模型中的子词
+ valid_parts = [w for w in word_parts if w in self.model.key_to_index]
+
+ if not valid_parts:
+ return []
+
+ # 使用最长的有效子词或第一个有效子词
+ valid_parts.sort(key=len, reverse=True)
+ word = valid_parts[0]
+
+ # 如果替换后的词仍不在词汇表中，返回空列表
+ if word not in self.model.key_to_index:
+ return []
+
+ # 获取相似词
+ similar_words = self.model.most_similar(word, topn=topn*2) # 多获取一些，后续过滤
+
+ # 过滤低于阈值的结果，并只返回词语(不返回相似度)
+ filtered_words = [w for w, sim in similar_words if sim >= min_similarity][:topn]
+
+ # 缓存结果
+ self.similar_words_cache[cache_key] = filtered_words
+ return filtered_words
+
+ except Exception as e:
+ logging.error(f"获取相似词失败: {e}, 词语: {word}")
+ return []
+
+ def calculate_similarity(self, word1, word2):
+ """
+ 计算两个词的相似度
+
+ 参数:
+ word1, word2: 输入词语
+ 返回:
+ 相似度分数(0-1)，如果任意词不存在则返回0
+ """
+ if not self.ensure_initialized():
+ return 0
+
+ # 检查缓存
+ cache_key = f"{word1}_{word2}"
+ reverse_key = f"{word2}_{word1}"
+
+ if cache_key in self.similarity_cache:
+ return self.similarity_cache[cache_key]
+ if reverse_key in self.similarity_cache:
+ return self.similarity_cache[reverse_key]
+
+ try:
+ # 检查词是否在词汇表中
+ if word1 not in self.model.key_to_index or word2 not in self.model.key_to_index:
+ return 0
+
+ similarity = self.model.similarity(word1, word2)
+
+ # 缓存结果
+ self.similarity_cache[cache_key] = similarity
+ return similarity
+
+ except Exception as e:
+ logging.error(f"计算相似度失败: {e}, 词语: {word1}, {word2}")
+ return 0
+
+ def expand_query(self, query, topn=5, min_similarity=0.6):
+ """
+ 扩展查询词，返回相关词汇
+
+ 参数:
+ query: 查询词
+ topn: 每个词扩展的相似词数量
+ min_similarity: 最小相似度阈值
+ 返回:
+ 扩展后的词语列表
+ """
+ if not self.ensure_initialized():
+ return [query]
+
+ expanded_terms = [query]
+
+ # 对查询进行分词
+ words = list(jieba.cut(query))
+
+ # 为每个词找相似词
+ for word in words:
+ if len(word) <= 1: # 忽略单字，减少噪音
+ continue
+
+ similar_words = self.get_similar_words(word, topn=topn, min_similarity=min_similarity)
+ expanded_terms.extend(similar_words)
+
+ # 确保唯一性
+ return list(set(expanded_terms))
+
+# 单例模式，全局使用一个模型实例
+_word2vec_helper = None
+
+def get_word2vec_helper(model_path=None):
+ """获取Word2Vec辅助类的全局单例"""
+ global _word2vec_helper
+ if _word2vec_helper is None:
+ _word2vec_helper = Word2VecHelper(model_path)
+ _word2vec_helper.ensure_initialized()
+ return _word2vec_helper
+
+# 便捷函数，方便直接调用
+def get_similar_words(word, topn=10, min_similarity=0.5):
+ """获取相似词的便捷函数"""
+ helper = get_word2vec_helper()
+ return helper.get_similar_words(word, topn, min_similarity)
+
+def calculate_similarity(word1, word2):
+ """计算相似度的便捷函数"""
+ helper = get_word2vec_helper()
+ return helper.calculate_similarity(word1, word2)
+
+def expand_query(query, topn=5, min_similarity=0.6):
+ """扩展查询的便捷函数"""
+ helper = get_word2vec_helper()
+ return helper.expand_query(query, topn, min_similarity)
+
+# 使用示例
+if __name__ == "__main__":
+ # 测试模型加载和词语相似度
+ helper = get_word2vec_helper()
+
+ # 测试词
+ test_words = ["电影", "功夫", "熊猫", "科幻", "漫威"]
+
+ for word in test_words:
+ print(f"\n{word} 的相似词:")
+ similar = helper.get_similar_words(word, topn=5)
+ for sim_word in similar:
+ print(f" - {sim_word}")
+
+ # 测试相似度计算
+ word_pairs = [
+ ("电影", "电视"),
+ ("功夫", "武术"),
+ ("科幻", "未来"),
+ ("漫威", "超级英雄")
+ ]
+
+ print("\n词语相似度:")
+ for w1, w2 in word_pairs:
+ sim = helper.calculate_similarity(w1, w2)
+ print(f" {w1} <-> {w2}: {sim:.4f}")
+
+ # 测试查询扩展
+ test_queries = ["功夫熊猫", "科幻电影", "漫威英雄"]
+
+ print("\n查询扩展:")
+ for query in test_queries:
+ expanded = helper.expand_query(query)
+ print(f" {query} -> {expanded}")
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index ba17fcf..9295682 100644
--- a/Merge/back_rhj/app/utils/__pycache__/parse_args.cpython-312.pyc
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diff --git a/Merge/back_rhj/app/utils/__pycache__/scheduler_manager.cpython-312.pyc b/Merge/back_rhj/app/utils/__pycache__/scheduler_manager.cpython-312.pyc
index 6ed8964..89f68ad 100644
--- a/Merge/back_rhj/app/utils/__pycache__/scheduler_manager.cpython-312.pyc
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diff --git a/Merge/back_rhj/requirements.txt b/Merge/back_rhj/requirements.txt
new file mode 100644
index 0000000..9886994
--- /dev/null
+++ b/Merge/back_rhj/requirements.txt
@@ -0,0 +1,10 @@
+Flask==2.3.3
+Flask-CORS==4.0.0
+python-dotenv==1.0.0
+SQLAlchemy==2.0.23
+PyMySQL==1.1.0
+torch==2.1.0
+numpy==1.24.3
+PyJWT==2.8.0
+Flask-Mail==0.9.1
+APScheduler==3.10.4
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diff --git a/Merge/back_wzy/routes/__pycache__/posts.cpython-312.pyc b/Merge/back_wzy/routes/__pycache__/posts.cpython-312.pyc
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diff --git a/Merge/front/src/api/search_jwlll.js b/Merge/front/src/api/search_jwlll.js
new file mode 100644
index 0000000..5ab7eb1
--- /dev/null
+++ b/Merge/front/src/api/search_jwlll.js
@@ -0,0 +1,97 @@
+// 搜索推荐算法相关的API接口
+// 对应 JWLLL 后端服务
+
+const BASE_URL = 'http://127.0.0.1:5000'
+
+// 通用请求函数
+const request = async (url, options = {}) => {
+ try {
+ const response = await fetch(url, {
+ headers: {
+ 'Content-Type': 'application/json',
+ ...options.headers
+ },
+ ...options
+ })
+ return await response.json()
+ } catch (error) {
+ console.error('API请求错误:', error)
+ throw error
+ }
+}
+
+// 搜索API
+export const searchAPI = {
+ // 搜索内容
+ search: async (keyword, category = undefined) => {
+ return await request(`${BASE_URL}/search`, {
+ method: 'POST',
+ body: JSON.stringify({ keyword, category })
+ })
+ },
+
+ // 获取用户标签
+ getUserTags: async (userId) => {
+ return await request(`${BASE_URL}/user_tags?user_id=${userId}`)
+ },
+
+ // 标签推荐
+ recommendByTags: async (userId, tags) => {
+ return await request(`${BASE_URL}/recommend_tags`, {
+ method: 'POST',
+ body: JSON.stringify({ user_id: userId, tags })
+ })
+ },
+
+ // 协同过滤推荐
+ userBasedRecommend: async (userId, topN = 20) => {
+ return await request(`${BASE_URL}/user_based_recommend`, {
+ method: 'POST',
+ body: JSON.stringify({ user_id: userId, top_n: topN })
+ })
+ },
+
+ // 获取帖子详情
+ getPostDetail: async (postId) => {
+ return await request(`${BASE_URL}/post/${postId}`)
+ },
+
+ // 点赞帖子
+ likePost: async (postId, userId) => {
+ return await request(`${BASE_URL}/like`, {
+ method: 'POST',
+ body: JSON.stringify({ post_id: postId, user_id: userId })
+ })
+ },
+
+ // 取消点赞
+ unlikePost: async (postId, userId) => {
+ return await request(`${BASE_URL}/unlike`, {
+ method: 'POST',
+ body: JSON.stringify({ post_id: postId, user_id: userId })
+ })
+ },
+
+ // 添加评论
+ addComment: async (postId, userId, content) => {
+ return await request(`${BASE_URL}/comment`, {
+ method: 'POST',
+ body: JSON.stringify({ post_id: postId, user_id: userId, content })
+ })
+ },
+
+ // 获取评论
+ getComments: async (postId) => {
+ return await request(`${BASE_URL}/comments/${postId}`)
+ },
+
+ // 上传帖子
+ uploadPost: async (postData) => {
+ return await request(`${BASE_URL}/upload`, {
+ method: 'POST',
+ body: JSON.stringify(postData)
+ })
+ }
+}
+
+export default searchAPI
diff --git a/Merge/front/src/components/CreatePost.jsx b/Merge/front/src/components/CreatePost.jsx
index 1d2f306..c11e247 100644
--- a/Merge/front/src/components/CreatePost.jsx
+++ b/Merge/front/src/components/CreatePost.jsx
@@ -13,10 +13,8 @@
const navigate = useNavigate()
const { postId } = useParams()
const isEdit = Boolean(postId)
-
// 步骤：新帖先上传，编辑则直接到 detail
const [step, setStep] = useState(isEdit ? 'detail' : 'upload')
- const [files, setFiles] = useState([])
const [mediaUrls, setMediaUrls] = useState([])
// 表单字段
@@ -53,10 +51,8 @@
.catch(err => setError(err.message))
.finally(() => setLoading(false))
}, [isEdit, postId])
-
// 上传回调
const handleUploadComplete = async uploadedFiles => {
- setFiles(uploadedFiles)
// TODO: 真正上传到服务器后替换为服务端 URL
const urls = await Promise.all(
uploadedFiles.map(f => URL.createObjectURL(f))
diff --git a/Merge/front/src/components/HomeFeed.jsx b/Merge/front/src/components/HomeFeed.jsx
index c681858..e32a2eb 100644
--- a/Merge/front/src/components/HomeFeed.jsx
+++ b/Merge/front/src/components/HomeFeed.jsx
@@ -1,9 +1,10 @@
// src/components/HomeFeed.jsx
-import React, { useState, useEffect } from 'react'
+import React, { useState, useEffect, useCallback } from 'react'
import { useNavigate } from 'react-router-dom'
import { ThumbsUp } from 'lucide-react'
import { fetchPosts, fetchPost } from '../api/posts_wzy'
+import { searchAPI } from '../api/search_jwlll'
import '../style/HomeFeed.css'
const categories = [
@@ -11,15 +12,120 @@
'职场','情感','家居','游戏','旅行','健身'
]
+const recommendModes = [
+ { label: '标签推荐', value: 'tag' },
+ { label: '协同过滤推荐', value: 'cf' }
+]
+
+const DEFAULT_USER_ID = '3' // 确保数据库有此用户
+const DEFAULT_TAGS = ['美食','影视','穿搭'] // 可根据实际数据库调整
+
export default function HomeFeed() {
const navigate = useNavigate()
const [activeCat, setActiveCat] = useState('推荐')
const [items, setItems] = useState([])
const [loading, setLoading] = useState(true)
const [error, setError] = useState(null)
+ // JWLLL 搜索推荐相关状态
+ const [search, setSearch] = useState('')
+ const [recMode, setRecMode] = useState('tag')
+ const [recCFNum, setRecCFNum] = useState(20)
+ const [useSearchRecommend, setUseSearchRecommend] = useState(false) // 是否使用搜索推荐模式 // JWLLL 搜索推荐功能函数
+
+ // JWLLL搜索推荐内容
+ const fetchSearchContent = useCallback(async (keyword = '') => {
+ setLoading(true)
+ setError(null)
+ try {
+ const data = await searchAPI.search(keyword || activeCat, activeCat === '推荐' ? undefined : activeCat)
+ const formattedItems = (data.results || []).map(item => ({
+ id: item.id,
+ title: item.title,
+ author: item.author || '佚名',
+ avatar: `https://i.pravatar.cc/40?img=${item.id}`,
+ img: item.img || '',
+ likes: item.heat || 0,
+ content: item.content
+ }))
+ setItems(formattedItems)
+ } catch (e) {
+ console.error('搜索失败:', e)
+ setError('搜索失败')
+ setItems([])
+ }
+ setLoading(false)
+ }, [activeCat])
+
+ // 标签推荐
+ const fetchTagRecommend = useCallback(async (tags) => {
+ setLoading(true)
+ setError(null)
+ try {
+ const data = await searchAPI.recommendByTags(DEFAULT_USER_ID, tags)
+ const formattedItems = (data.recommendations || []).map(item => ({
+ id: item.id,
+ title: item.title,
+ author: item.author || '佚名',
+ avatar: `https://i.pravatar.cc/40?img=${item.id}`,
+ img: item.img || '',
+ likes: item.heat || 0,
+ content: item.content
+ }))
+ setItems(formattedItems)
+ } catch (e) {
+ console.error('标签推荐失败:', e)
+ setError('标签推荐失败')
+ setItems([])
+ }
+ setLoading(false)
+ }, [])
+
+ // 协同过滤推荐
+ const fetchCFRecommend = useCallback(async (topN = recCFNum) => {
+ setLoading(true)
+ setError(null)
+ try {
+ const data = await searchAPI.userBasedRecommend(DEFAULT_USER_ID, topN)
+ const formattedItems = (data.recommendations || []).map(item => ({
+ id: item.id,
+ title: item.title,
+ author: item.author || '佚名',
+ avatar: `https://i.pravatar.cc/40?img=${item.id}`,
+ img: item.img || '',
+ likes: item.heat || 0,
+ content: item.content
+ }))
+ setItems(formattedItems)
+ } catch (e) {
+ console.error('协同过滤推荐失败:', e)
+ setError('协同过滤推荐失败')
+ setItems([])
+ }
+ setLoading(false)
+ }, [recCFNum])
+
+ // 获取用户兴趣标签后再推荐
+ const fetchUserTagsAndRecommend = useCallback(async () => {
+ setLoading(true)
+ setError(null)
+ let tags = []
+ try {
+ const data = await searchAPI.getUserTags(DEFAULT_USER_ID)
+ tags = Array.isArray(data.tags) && data.tags.length > 0 ? data.tags : DEFAULT_TAGS
+ } catch {
+ tags = DEFAULT_TAGS
+ }
+ if (recMode === 'tag') {
+ await fetchTagRecommend(tags)
+ } else {
+ await fetchCFRecommend()
+ }
+ setLoading(false)
+ }, [recMode, fetchTagRecommend, fetchCFRecommend])
useEffect(() => {
- async function loadPosts() {
+ // 原始数据加载函数
+ const loadPosts = async () => {
try {
const list = await fetchPosts() // [{id, title, heat, created_at}, …]
// 为了拿到 media_urls 和 user_id，这里再拉详情
@@ -43,25 +149,149 @@
setLoading(false)
}
}
- loadPosts()
- }, [])
+ // 根据模式选择加载方式
+ if (activeCat === '推荐' && useSearchRecommend) {
+ fetchUserTagsAndRecommend()
+ } else {
+ loadPosts()
+ }
+ }, [activeCat, useSearchRecommend, fetchUserTagsAndRecommend])
+ // 切换推荐模式时的额外处理
+ useEffect(() => {
+ if (activeCat === '推荐' && useSearchRecommend) {
+ fetchUserTagsAndRecommend()
+ }
+ // eslint-disable-next-line
+ }, [recMode, fetchUserTagsAndRecommend])
+
+ // 根据模式选择不同的加载方式
+ const handleSearch = e => {
+ e.preventDefault()
+ if (useSearchRecommend) {
+ fetchSearchContent(search)
+ } else {
+ // 切换到搜索推荐模式
+ setUseSearchRecommend(true)
+ fetchSearchContent(search)
+ }
+ }
+
+ const handlePostClick = (postId) => {
+ navigate(`/post/${postId}`)
+ }
return (
<div className="home-feed">
+ {/* 数据源切换 */}
+ <div style={{marginBottom:12, display:'flex', alignItems:'center', gap:16}}>
+ <span>数据源：</span>
+ <div style={{display:'flex', gap:8}}>
+ <button
+ className={!useSearchRecommend ? 'rec-btn styled active' : 'rec-btn styled'}
+ onClick={() => {setUseSearchRecommend(false); setActiveCat('推荐')}}
+ type="button"
+ style={{
+ borderRadius: 20,
+ padding: '4px 18px',
+ border: !useSearchRecommend ? '2px solid #e84c4a' : '1px solid #ccc',
+ background: !useSearchRecommend ? '#fff0f0' : '#fff',
+ color: !useSearchRecommend ? '#e84c4a' : '#333',
+ fontWeight: !useSearchRecommend ? 600 : 400,
+ cursor: 'pointer',
+ transition: 'all 0.2s',
+ outline: 'none',
+ }}
+ >原始数据</button>
+ <button
+ className={useSearchRecommend ? 'rec-btn styled active' : 'rec-btn styled'}
+ onClick={() => {setUseSearchRecommend(true); setActiveCat('推荐')}}
+ type="button"
+ style={{
+ borderRadius: 20,
+ padding: '4px 18px',
+ border: useSearchRecommend ? '2px solid #e84c4a' : '1px solid #ccc',
+ background: useSearchRecommend ? '#fff0f0' : '#fff',
+ color: useSearchRecommend ? '#e84c4a' : '#333',
+ fontWeight: useSearchRecommend ? 600 : 400,
+ cursor: 'pointer',
+ transition: 'all 0.2s',
+ outline: 'none',
+ }}
+ >智能推荐</button>
+ </div>
+ </div>
+
+ {/* 推荐模式切换，仅在推荐页显示且使用搜索推荐时 */}
+ {activeCat === '推荐' && useSearchRecommend && (
+ <div style={{marginBottom:12, display:'flex', alignItems:'center', gap:16}}>
+ <span style={{marginRight:8}}>推荐模式：</span>
+ <div style={{display:'flex', gap:8}}>
+ {recommendModes.map(m => (
+ <button
+ key={m.value}
+ className={recMode===m.value? 'rec-btn styled active':'rec-btn styled'}
+ onClick={() => setRecMode(m.value)}
+ type="button"
+ style={{
+ borderRadius: 20,
+ padding: '4px 18px',
+ border: recMode===m.value ? '2px solid #e84c4a' : '1px solid #ccc',
+ background: recMode===m.value ? '#fff0f0' : '#fff',
+ color: recMode===m.value ? '#e84c4a' : '#333',
+ fontWeight: recMode===m.value ? 600 : 400,
+ cursor: 'pointer',
+ transition: 'all 0.2s',
+ outline: 'none',
+ }}
+ >{m.label}</button>
+ ))}
+ </div>
+ {/* 协同过滤推荐数量选择 */}
+ {recMode === 'cf' && (
+ <div style={{display:'flex',alignItems:'center',gap:4}}>
+ <span>推荐数量：</span>
+ <select value={recCFNum} onChange={e => { setRecCFNum(Number(e.target.value)); fetchCFRecommend(Number(e.target.value)) }} style={{padding:'2px 8px',borderRadius:6,border:'1px solid #ccc'}}>
+ {[10, 20, 30, 50].map(n => <option key={n} value={n}>{n}</option>)}
+ </select>
+ </div>
+ )}
+ </div>
+ )}
+
+ {/* 搜索栏 */}
+ <form className="feed-search" onSubmit={handleSearch} style={{marginBottom:16, display:'flex', gap:8, alignItems:'center'}}>
+ <input
+ type="text"
+ className="search-input"
+ placeholder="搜索内容/标题/标签"
+ value={search}
+ onChange={e => setSearch(e.target.value)}
+ />
+ <button type="submit" className="search-btn">搜索</button>
+ </form>
+
{/* 顶部分类 */}
<nav className="feed-tabs">
{categories.map(cat => (
<button
key={cat}
className={cat === activeCat ? 'tab active' : 'tab'}
- onClick={() => setActiveCat(cat)}
+ onClick={() => {
+ setActiveCat(cat);
+ setSearch('');
+ if (useSearchRecommend) {
+ if (cat === '推荐') {
+ fetchUserTagsAndRecommend()
+ } else {
+ fetchSearchContent()
+ }
+ }
+ }}
>
{cat}
</button>
))}
- </nav>
-
- {/* 状态提示 */}
+ </nav> {/* 状态提示 */}
{loading ? (
<div className="loading">加载中…</div>
) : error ? (
@@ -69,22 +299,27 @@
) : (
/* 瀑布流卡片区 */
<div className="feed-grid">
- {items.map(item => (
- <div key={item.id} className="feed-card">
- <img className="card-img" src={item.img} alt={item.title} />
- <h3 className="card-title">{item.title}</h3>
- <div className="card-footer">
- <div className="card-author">
- <img className="avatar" src={item.avatar} alt={item.author} />
- <span className="username">{item.author}</span>
- </div>
- <div className="card-likes">
- <ThumbsUp size={16} />
- <span className="likes-count">{item.likes}</span>
+ {items.length === 0 ? (
+ <div style={{padding:32, color:'#aaa'}}>暂无内容</div>
+ ) : (
+ items.map(item => (
+ <div key={item.id} className="feed-card" onClick={() => handlePostClick(item.id)}>
+ {item.img && <img className="card-img" src={item.img} alt={item.title} />}
+ <h3 className="card-title">{item.title}</h3>
+ {item.content && <div className="card-content">{item.content.slice(0, 60) || ''}</div>}
+ <div className="card-footer">
+ <div className="card-author">
+ <img className="avatar" src={item.avatar} alt={item.author} />
+ <span className="username">{item.author}</span>
+ </div>
+ <div className="card-likes">
+ <ThumbsUp size={16} />
+ <span className="likes-count">{item.likes}</span>
+ </div>
</div>
</div>
- </div>
- ))}
+ ))
+ )}
</div>
)}
</div>
diff --git a/Merge/front/src/components/LogsDashboard.js b/Merge/front/src/components/LogsDashboard.js
index 22047e2..6ab4746 100644
--- a/Merge/front/src/components/LogsDashboard.js
+++ b/Merge/front/src/components/LogsDashboard.js
@@ -3,7 +3,9 @@
import '../style/Admin.css';
function LogsDashboard() {
+ // eslint-disable-next-line no-unused-vars
const [logs, setLogs] = useState([]);
+ // eslint-disable-next-line no-unused-vars
const [stats, setStats] = useState({});
useEffect(() => {
diff --git a/Merge/front/src/components/PostDetailJWLLL.jsx b/Merge/front/src/components/PostDetailJWLLL.jsx
new file mode 100644
index 0000000..0dc7289
--- /dev/null
+++ b/Merge/front/src/components/PostDetailJWLLL.jsx
@@ -0,0 +1,322 @@
+import React, { useState, useEffect } from 'react'
+import { useParams, useNavigate } from 'react-router-dom'
+import { ArrowLeft, ThumbsUp, MessageCircle, Share2, BookmarkPlus, Heart, Eye } from 'lucide-react'
+import { searchAPI } from '../api/search_jwlll'
+import '../style/PostDetail.css'
+
+export default function PostDetail() {
+ const { id } = useParams()
+ const navigate = useNavigate()
+ const [post, setPost] = useState(null)
+ const [loading, setLoading] = useState(true)
+ const [error, setError] = useState(null)
+ const [liked, setLiked] = useState(false)
+ const [bookmarked, setBookmarked] = useState(false)
+ const [likeCount, setLikeCount] = useState(0)
+ const [comments, setComments] = useState([])
+ const [newComment, setNewComment] = useState('')
+ const [showComments, setShowComments] = useState(false)
+
+ const DEFAULT_USER_ID = '3' // 默认用户ID
+
+ useEffect(() => {
+ fetchPostDetail()
+ fetchComments()
+ }, [id])
+
+ const fetchPostDetail = async () => {
+ setLoading(true)
+ setError(null)
+ try {
+ const data = await searchAPI.getPostDetail(id)
+ setPost(data)
+ setLikeCount(data.heat || 0)
+ } catch (error) {
+ console.error('获取帖子详情失败:', error)
+ setError('帖子不存在或已被删除')
+ } finally {
+ setLoading(false)
+ }
+ }
+
+ const fetchComments = async () => {
+ try {
+ const data = await searchAPI.getComments(id)
+ setComments(data.comments || [])
+ } catch (error) {
+ console.error('获取评论失败:', error)
+ }
+ }
+
+ const handleBack = () => {
+ navigate(-1)
+ }
+
+ const handleLike = async () => {
+ try {
+ const newLiked = !liked
+ if (newLiked) {
+ await searchAPI.likePost(id, DEFAULT_USER_ID)
+ } else {
+ await searchAPI.unlikePost(id, DEFAULT_USER_ID)
+ }
+ setLiked(newLiked)
+ setLikeCount(prev => newLiked ? prev + 1 : prev - 1)
+ } catch (error) {
+ console.error('点赞失败:', error)
+ // 回滚状态
+ setLiked(!liked)
+ setLikeCount(prev => liked ? prev + 1 : prev - 1)
+ }
+ }
+
+ const handleBookmark = () => {
+ setBookmarked(!bookmarked)
+ // 实际项目中这里应该调用后端API保存收藏状态
+ }
+
+ const handleShare = () => {
+ // 分享功能
+ if (navigator.share) {
+ navigator.share({
+ title: post?.title,
+ text: post?.content,
+ url: window.location.href,
+ })
+ } else {
+ // 复制链接到剪贴板
+ navigator.clipboard.writeText(window.location.href)
+ alert('链接已复制到剪贴板')
+ }
+ }
+
+ const handleAddComment = async (e) => {
+ e.preventDefault()
+ if (!newComment.trim()) return
+
+ try {
+ await searchAPI.addComment(id, DEFAULT_USER_ID, newComment)
+ setNewComment('')
+ fetchComments() // 刷新评论列表
+ } catch (error) {
+ console.error('添加评论失败:', error)
+ alert('评论失败，请重试')
+ }
+ }
+
+ if (loading) {
+ return (
+ <div className="post-detail">
+ <div className="loading-container">
+ <div className="loading-spinner"></div>
+ <p>加载中...</p>
+ </div>
+ </div>
+ )
+ }
+
+ if (error) {
+ return (
+ <div className="post-detail">
+ <div className="error-container">
+ <h2>😔 出错了</h2>
+ <p>{error}</p>
+ <button onClick={handleBack} className="back-btn">
+ <ArrowLeft size={20} />
+ 返回
+ </button>
+ </div>
+ </div>
+ )
+ }
+
+ if (!post) {
+ return (
+ <div className="post-detail">
+ <div className="error-container">
+ <h2>😔 帖子不存在</h2>
+ <p>该帖子可能已被删除或不存在</p>
+ <button onClick={handleBack} className="back-btn">
+ <ArrowLeft size={20} />
+ 返回
+ </button>
+ </div>
+ </div>
+ )
+ }
+
+ return (
+ <div className="post-detail">
+ {/* 顶部导航栏 */}
+ <header className="post-header">
+ <button onClick={handleBack} className="back-btn">
+ <ArrowLeft size={20} />
+ 返回
+ </button>
+ <div className="header-actions">
+ <button onClick={handleShare} className="action-btn">
+ <Share2 size={20} />
+ </button>
+ <button
+ onClick={handleBookmark}
+ className={`action-btn ${bookmarked ? 'active' : ''}`}
+ >
+ <BookmarkPlus size={20} />
+ </button>
+ </div>
+ </header>
+
+ {/* 主要内容区 */}
+ <main className="post-content">
+ {/* 帖子标题 */}
+ <h1 className="post-title">{post.title}</h1>
+
+ {/* 作者信息和元数据 */}
+ <div className="post-meta">
+ <div className="author-info">
+ <div className="avatar">
+ {post.author ? post.author.charAt(0).toUpperCase() : 'U'}
+ </div>
+ <div className="author-details">
+ <span className="author-name">{post.author || '匿名用户'}</span>
+ <span className="post-date">
+ {post.create_time ? new Date(post.create_time).toLocaleDateString('zh-CN') : '未知时间'}
+ </span>
+ </div>
+ </div>
+ <div className="post-stats">
+ <span className="stat-item">
+ <Eye size={16} />
+ {post.views || 0}
+ </span>
+ <span className="stat-item">
+ <Heart size={16} />
+ {likeCount}
+ </span>
+ </div>
+ </div>
+
+ {/* 标签 */}
+ {post.tags && post.tags.length > 0 && (
+ <div className="post-tags">
+ {post.tags.map((tag, index) => (
+ <span key={index} className="tag">{tag}</span>
+ ))}
+ </div>
+ )}
+
+ {/* 帖子正文 */}
+ <div className="post-body">
+ <p>{post.content}</p>
+ </div>
+
+ {/* 类别信息 */}
+ {(post.category || post.type) && (
+ <div className="post-category">
+ {post.category && (
+ <>
+ <span className="category-label">分类：</span>
+ <span className="category-name">{post.category}</span>
+ </>
+ )}
+ {post.type && (
+ <>
+ <span className="category-label" style={{marginLeft: '1em'}}>类型：</span>
+ <span className="category-name">{post.type}</span>
+ </>
+ )}
+ </div>
+ )}
+
+ {/* 评论区 */}
+ <div className="comments-section">
+ <div className="comments-header">
+ <button
+ onClick={() => setShowComments(!showComments)}
+ className="comments-toggle"
+ >
+ <MessageCircle size={20} />
+ 评论 ({comments.length})
+ </button>
+ </div>
+
+ {showComments && (
+ <div className="comments-content">
+ {/* 添加评论 */}
+ <form onSubmit={handleAddComment} className="comment-form">
+ <textarea
+ value={newComment}
+ onChange={(e) => setNewComment(e.target.value)}
+ placeholder="写下你的评论..."
+ className="comment-input"
+ rows={3}
+ />
+ <button type="submit" className="comment-submit">
+ 发布评论
+ </button>
+ </form>
+
+ {/* 评论列表 */}
+ <div className="comments-list">
+ {comments.length === 0 ? (
+ <p className="no-comments">暂无评论</p>
+ ) : (
+ comments.map((comment, index) => (
+ <div key={index} className="comment-item">
+ <div className="comment-author">
+ <div className="comment-avatar">
+ {comment.user_name ? comment.user_name.charAt(0).toUpperCase() : 'U'}
+ </div>
+ <span className="comment-name">{comment.user_name || '匿名用户'}</span>
+ <span className="comment-time">
+ {comment.create_time ? new Date(comment.create_time).toLocaleString('zh-CN') : ''}
+ </span>
+ </div>
+ <div className="comment-content">
+ {comment.content}
+ </div>
+ </div>
+ ))
+ )}
+ </div>
+ </div>
+ )}
+ </div>
+ </main>
+
+ {/* 底部操作栏 */}
+ <footer className="post-footer">
+ <div className="action-bar">
+ <button
+ onClick={handleLike}
+ className={`action-button ${liked ? 'liked' : ''}`}
+ >
+ <ThumbsUp size={20} />
+ <span>{likeCount}</span>
+ </button>
+
+ <button
+ onClick={() => setShowComments(!showComments)}
+ className="action-button"
+ >
+ <MessageCircle size={20} />
+ <span>评论</span>
+ </button>
+
+ <button onClick={handleShare} className="action-button">
+ <Share2 size={20} />
+ <span>分享</span>
+ </button>
+
+ <button
+ onClick={handleBookmark}
+ className={`action-button ${bookmarked ? 'bookmarked' : ''}`}
+ >
+ <BookmarkPlus size={20} />
+ <span>收藏</span>
+ </button>
+ </div>
+ </footer>
+ </div>
+ )
+}
diff --git a/Merge/front/src/components/Sidebar.jsx b/Merge/front/src/components/Sidebar.jsx
index 92bc8f1..e35db63 100644
--- a/Merge/front/src/components/Sidebar.jsx
+++ b/Merge/front/src/components/Sidebar.jsx
@@ -7,6 +7,8 @@
Activity,
Users,
ChevronDown,
+ Search,
+ Upload,
} from 'lucide-react'
import '../App.css'
@@ -24,6 +26,7 @@
{ id: 'fans', label: '粉丝数据', path: '/dashboard/fans' },
]
},
+ { id: 'upload-jwlll', label: '智能发布', icon: Upload, path: '/upload-jwlll' },
// { id: 'activity', label: '活动中心', icon: Activity, path: '/activity' },
// { id: 'notes', label: '笔记灵感', icon: BookOpen, path: '/notes' },
// { id: 'creator', label: '创作学院', icon: Users, path: '/creator' },
diff --git a/Merge/front/src/components/UploadPageJWLLL.jsx b/Merge/front/src/components/UploadPageJWLLL.jsx
new file mode 100644
index 0000000..2d9ee7d
--- /dev/null
+++ b/Merge/front/src/components/UploadPageJWLLL.jsx
@@ -0,0 +1,328 @@
+import React, { useState } from 'react'
+import { Image, Video, Send } from 'lucide-react'
+import { searchAPI } from '../api/search_jwlll'
+import '../style/UploadPage.css'
+
+const categories = [
+ '穿搭','美食','彩妆','影视',
+ '职场','情感','家居','游戏','旅行','健身'
+]
+
+export default function UploadPageJWLLL({ onComplete }) {
+ const [activeTab, setActiveTab] = useState('image')
+ const [isDragOver, setIsDragOver] = useState(false)
+ const [isUploading, setIsUploading] = useState(false)
+ const [uploadedFiles, setUploadedFiles] = useState([])
+ const [uploadProgress, setUploadProgress] = useState(0)
+
+ // 新增表单字段
+ const [title, setTitle] = useState('')
+ const [content, setContent] = useState('')
+ const [tags, setTags] = useState('')
+ const [category, setCategory] = useState(categories[0])
+ const [isPublishing, setIsPublishing] = useState(false)
+
+ const DEFAULT_USER_ID = '3' // 默认用户ID
+
+ const validateFiles = files => {
+ const imgTypes = ['image/jpeg','image/jpg','image/png','image/webp']
+ const vidTypes = ['video/mp4','video/mov','video/avi']
+ const types = activeTab==='video'? vidTypes : imgTypes
+ const max = activeTab==='video'? 2*1024*1024*1024 : 32*1024*1024
+
+ const invalid = files.filter(f => !types.includes(f.type) || f.size > max)
+ if (invalid.length) {
+ alert(`发现 ${invalid.length} 个无效文件，请检查文件格式和大小`)
+ return false
+ }
+ return true
+ }
+
+ const simulateUpload = files => {
+ setIsUploading(true)
+ setUploadProgress(0)
+ setUploadedFiles(files)
+ const iv = setInterval(() => {
+ setUploadProgress(p => {
+ if (p >= 100) {
+ clearInterval(iv)
+ setIsUploading(false)
+ if (typeof onComplete === 'function') {
+ onComplete(files)
+ }
+ return 100
+ }
+ return p + 10
+ })
+ }, 200)
+ }
+
+ const handleFileUpload = () => {
+ if (isUploading) return
+ const input = document.createElement('input')
+ input.type = 'file'
+ input.accept = activeTab==='video'? 'video/*' : 'image/*'
+ input.multiple = activeTab==='image'
+ input.onchange = e => {
+ const files = Array.from(e.target.files)
+ if (files.length > 0 && validateFiles(files)) simulateUpload(files)
+ }
+ input.click()
+ }
+
+ const handleDragOver = e => { e.preventDefault(); e.stopPropagation(); setIsDragOver(true) }
+ const handleDragLeave = e => { e.preventDefault(); e.stopPropagation(); setIsDragOver(false) }
+ const handleDrop = e => {
+ e.preventDefault(); e.stopPropagation(); setIsDragOver(false)
+ if (isUploading) return
+ const files = Array.from(e.dataTransfer.files)
+ if (files.length > 0 && validateFiles(files)) simulateUpload(files)
+ }
+
+ const clearFiles = () => setUploadedFiles([])
+ const removeFile = idx => setUploadedFiles(f => f.filter((_,i) => i!==idx))
+
+ // 发布帖子
+ const handlePublish = async () => {
+ if (!title.trim()) {
+ alert('请输入标题')
+ return
+ }
+ if (!content.trim()) {
+ alert('请输入内容')
+ return
+ }
+
+ setIsPublishing(true)
+ try {
+ const postData = {
+ user_id: DEFAULT_USER_ID,
+ title: title.trim(),
+ content: content.trim(),
+ tags: tags.split(',').map(t => t.trim()).filter(t => t),
+ category: category,
+ type: activeTab === 'video' ? 'video' : 'image',
+ media_files: uploadedFiles.map(f => f.name) // 实际项目中应该是上传后的URL
+ }
+
+ await searchAPI.uploadPost(postData)
+ alert('发布成功！')
+
+ // 清空表单
+ setTitle('')
+ setContent('')
+ setTags('')
+ setUploadedFiles([])
+ setActiveTab('image')
+
+ } catch (error) {
+ console.error('发布失败:', error)
+ alert('发布失败，请重试')
+ } finally {
+ setIsPublishing(false)
+ }
+ }
+
+ return (
+ <div className="upload-page-jwlll">
+ <div className="upload-tabs">
+ <button
+ className={`upload-tab${activeTab==='video'?' active':''}`}
+ onClick={() => setActiveTab('video')}
+ >上传视频</button>
+ <button
+ className={`upload-tab${activeTab==='image'?' active':''}`}
+ onClick={() => setActiveTab('image')}
+ >上传图文</button>
+ </div>
+
+ {/* 内容表单 */}
+ <div className="content-form">
+ <div className="form-group">
+ <label htmlFor="title">标题</label>
+ <input
+ id="title"
+ type="text"
+ value={title}
+ onChange={(e) => setTitle(e.target.value)}
+ placeholder="请输入标题..."
+ className="form-input"
+ maxLength={100}
+ />
+ </div>
+
+ <div className="form-group">
+ <label htmlFor="content">内容</label>
+ <textarea
+ id="content"
+ value={content}
+ onChange={(e) => setContent(e.target.value)}
+ placeholder="请输入内容..."
+ className="form-textarea"
+ rows={4}
+ maxLength={1000}
+ />
+ </div>
+
+ <div className="form-row">
+ <div className="form-group">
+ <label htmlFor="category">分类</label>
+ <select
+ id="category"
+ value={category}
+ onChange={(e) => setCategory(e.target.value)}
+ className="form-select"
+ >
+ {categories.map(cat => (
+ <option key={cat} value={cat}>{cat}</option>
+ ))}
+ </select>
+ </div>
+
+ <div className="form-group">
+ <label htmlFor="tags">标签</label>
+ <input
+ id="tags"
+ type="text"
+ value={tags}
+ onChange={(e) => setTags(e.target.value)}
+ placeholder="用逗号分隔多个标签..."
+ className="form-input"
+ />
+ </div>
+ </div>
+ </div>
+
+ {/* 文件上传区域 */}
+ <div
+ className={`upload-area${isDragOver?' drag-over':''}`}
+ onDragOver={handleDragOver}
+ onDragLeave={handleDragLeave}
+ onDrop={handleDrop}
+ >
+ <div className="upload-icon">
+ {activeTab==='video'? <Video/> : <Image/>}
+ </div>
+ <h2 className="upload-title">
+ {activeTab==='video'
+ ? '拖拽视频到此处或点击上传'
+ : '拖拽图片到此处或点击上传'
+ }
+ </h2>
+ <p className="upload-subtitle">(需支持上传格式)</p>
+ <button
+ className={`upload-btn${isUploading?' uploading':''}`}
+ onClick={handleFileUpload}
+ disabled={isUploading}
+ >
+ {isUploading
+ ? `上传中... ${uploadProgress}%`
+ : activeTab==='video'
+ ? '上传视频'
+ : '上传图片'
+ }
+ </button>
+
+ {isUploading && (
+ <div className="progress-container">
+ <div className="progress-bar">
+ <div
+ className="progress-fill"
+ style={{ width: `${uploadProgress}%` }}
+ />
+ </div>
+ <div className="progress-text">{uploadProgress}%</div>
+ </div>
+ )}
+ </div>
+
+ {uploadedFiles.length > 0 && (
+ <div className="file-preview-area">
+ <div className="preview-header">
+ <h3 className="preview-title">已上传文件 ({uploadedFiles.length})</h3>
+ <button className="clear-files-btn" onClick={clearFiles}>
+ 清除所有
+ </button>
+ </div>
+ <div className="file-grid">
+ {uploadedFiles.map((file, i) => (
+ <div key={i} className="file-item">
+ <button
+ className="remove-file-btn"
+ onClick={() => removeFile(i)}
+ title="删除文件"
+ >×</button>
+ {file.type.startsWith('image/') ? (
+ <div className="file-thumbnail">
+ <img src={URL.createObjectURL(file)} alt={file.name} />
+ </div>
+ ) : (
+ <div className="file-thumbnail video-thumbnail">
+ <Video size={24} />
+ </div>
+ )}
+ <div className="file-info">
+ <div className="file-name" title={file.name}>
+ {file.name.length > 20
+ ? file.name.slice(0,17) + '...'
+ : file.name
+ }
+ </div>
+ <div className="file-size">
+ {(file.size/1024/1024).toFixed(2)} MB
+ </div>
+ </div>
+ </div>
+ ))}
+ </div>
+ </div>
+ )}
+
+ {/* 发布按钮 */}
+ <div className="publish-section">
+ <button
+ className={`publish-btn${isPublishing?' publishing':''}`}
+ onClick={handlePublish}
+ disabled={isPublishing || !title.trim() || !content.trim()}
+ >
+ <Send size={20} />
+ {isPublishing ? '发布中...' : '发布'}
+ </button>
+ </div>
+
+ <div className="upload-info fade-in">
+ {activeTab==='image' ? (
+ <>
+ <div className="info-item">
+ <h3 className="info-title">图片大小</h3>
+ <p className="info-desc">最大32MB</p>
+ </div>
+ <div className="info-item">
+ <h3 className="info-title">图片格式</h3>
+ <p className="info-desc">png/jpg/jpeg/webp</p>
+ </div>
+ <div className="info-item">
+ <h3 className="info-title">分辨率</h3>
+ <p className="info-desc">建议720×960及以上</p>
+ </div>
+ </>
+ ) : (
+ <>
+ <div className="info-item">
+ <h3 className="info-title">视频大小</h3>
+ <p className="info-desc">最大2GB，时长≤5分钟</p>
+ </div>
+ <div className="info-item">
+ <h3 className="info-title">视频格式</h3>
+ <p className="info-desc">mp4/mov</p>
+ </div>
+ <div className="info-item">
+ <h3 className="info-title">分辨率</h3>
+ <p className="info-desc">建议720P及以上</p>
+ </div>
+ </>
+ )}
+ </div>
+ </div>
+ )
+}
diff --git a/Merge/front/src/router/App.js b/Merge/front/src/router/App.js
index d7f5f09..efc4067 100644
--- a/Merge/front/src/router/App.js
+++ b/Merge/front/src/router/App.js
@@ -12,6 +12,8 @@
import NotebookPage from '../components/NotebookPage'
import PlaceholderPage from '../components/PlaceholderPage'
import UserProfile from '../components/UserProfile'
+import PostDetailJWLLL from '../components/PostDetailJWLLL'
+import UploadPageJWLLL from '../components/UploadPageJWLLL'
import AdminPage from '../components/Admin'
import SuperAdmin from '../components/SuperAdmin'
@@ -41,13 +43,13 @@
{/* 2.1 任何登录用户都能看自己的主页 */}
<Route element={<RequireOwnProfile />}>
<Route path="/user/:userId" element={<UserProfile />} />
- </Route>
-
- {/* 2.2 普通用户 */}
+ </Route> {/* 2.2 普通用户 */}
<Route element={<RequireRole allowedRoles={['user']} />}>
<Route path="/home" element={<HomeFeed />} />
+ <Route path="/post/:id" element={<PostDetailJWLLL />} />
<Route path="/posts/new" element={<CreatePost />} />
<Route path="/posts/edit/:postId" element={<CreatePost />} />
+ <Route path="/upload-jwlll" element={<UploadPageJWLLL />} />
<Route path="/notebooks" element={<NotebookPage />} />
<Route path="/dashboard/*" element={<PlaceholderPage />} />
<Route path="/activity" element={<PlaceholderPage pageId="activity" />} />
diff --git a/Merge/front/src/style/HomeFeed.css b/Merge/front/src/style/HomeFeed.css
index f1bf75d..394c84b 100644
--- a/Merge/front/src/style/HomeFeed.css
+++ b/Merge/front/src/style/HomeFeed.css
@@ -113,4 +113,95 @@
.card-likes .likes-count {
font-size: 13px;
color: #666;
+}
+
+/* --------- JWLLL 搜索推荐功能样式 --------- */
+
+/* 搜索框美化 */
+.feed-search {
+ display: flex;
+ gap: 8px;
+ align-items: center;
+}
+
+.search-input {
+ flex: 1;
+ padding: 8px 14px;
+ border: 1.5px solid #e0e0e0;
+ border-radius: 20px;
+ font-size: 15px;
+ outline: none;
+ transition: border 0.2s;
+ background: #fafbfc;
+}
+
+.search-input:focus {
+ border: 1.5px solid #e84c4a;
+ background: #fff;
+}
+
+.search-btn {
+ padding: 8px 22px;
+ border: none;
+ border-radius: 20px;
+ background: linear-gradient(90deg,#ff6a6a,#ff4757);
+ color: #fff;
+ font-weight: 600;
+ font-size: 15px;
+ cursor: pointer;
+ box-shadow: 0 2px 8px rgba(255,71,87,0.08);
+ transition: background 0.2s, box-shadow 0.2s;
+}
+
+.search-btn:hover {
+ background: linear-gradient(90deg,#ff4757,#e84c4a);
+ box-shadow: 0 4px 16px rgba(255,71,87,0.15);
+}
+
+/* 推荐模式切换按钮 */
+.rec-btn {
+ background: #f8f9fa;
+ border: 1px solid #dee2e6;
+ color: #495057;
+ padding: 6px 12px;
+ border-radius: 20px;
+ cursor: pointer;
+ transition: all 0.2s;
+ font-size: 14px;
+ outline: none;
+}
+
+.rec-btn:hover {
+ background: #e9ecef;
+}
+
+.rec-btn.active {
+ background: #fff0f0;
+ border: 2px solid #e84c4a;
+ color: #e84c4a;
+ font-weight: 600;
+}
+
+/* 卡片内容显示区域 */
+.card-content {
+ padding: 0 12px 8px;
+ font-size: 13px;
+ color: #666;
+ line-height: 1.4;
+ overflow: hidden;
+ text-overflow: ellipsis;
+ display: -webkit-box;
+ -webkit-line-clamp: 2;
+ -webkit-box-orient: vertical;
+}
+
+/* 点击效果 */
+.feed-card {
+ cursor: pointer;
+ transition: transform 0.2s, box-shadow 0.2s;
+}
+
+.feed-card:hover {
+ transform: translateY(-2px);
+ box-shadow: 0 4px 12px rgba(0,0,0,0.15);
}
\ No newline at end of file
diff --git a/Merge/front/src/style/PostDetail.css b/Merge/front/src/style/PostDetail.css
new file mode 100644
index 0000000..1be7126
--- /dev/null
+++ b/Merge/front/src/style/PostDetail.css
@@ -0,0 +1,436 @@
+/* 帖子详情页面容器 */
+.post-detail {
+ max-width: 800px;
+ margin: 0 auto;
+ background: #fff;
+ min-height: 100vh;
+ display: flex;
+ flex-direction: column;
+}
+
+/* 加载状态 */
+.loading-container {
+ display: flex;
+ flex-direction: column;
+ align-items: center;
+ justify-content: center;
+ padding: 60px 20px;
+ color: #666;
+}
+
+.loading-spinner {
+ width: 40px;
+ height: 40px;
+ border: 3px solid #f3f3f3;
+ border-top: 3px solid #ff4757;
+ border-radius: 50%;
+ animation: spin 1s linear infinite;
+ margin-bottom: 20px;
+}
+
+@keyframes spin {
+ 0% { transform: rotate(0deg); }
+ 100% { transform: rotate(360deg); }
+}
+
+/* 错误状态 */
+.error-container {
+ text-align: center;
+ padding: 60px 20px;
+ color: #666;
+}
+
+.error-container h2 {
+ margin-bottom: 16px;
+ color: #333;
+}
+
+/* 顶部导航栏 */
+.post-header {
+ display: flex;
+ justify-content: space-between;
+ align-items: center;
+ padding: 16px 20px;
+ border-bottom: 1px solid #eee;
+ background: #fff;
+ position: sticky;
+ top: 0;
+ z-index: 100;
+}
+
+.back-btn {
+ display: flex;
+ align-items: center;
+ gap: 8px;
+ padding: 8px 16px;
+ border: none;
+ background: #f8f9fa;
+ border-radius: 20px;
+ cursor: pointer;
+ transition: background-color 0.2s;
+ font-size: 14px;
+ color: #333;
+}
+
+.back-btn:hover {
+ background: #e9ecef;
+}
+
+.header-actions {
+ display: flex;
+ gap: 8px;
+}
+
+.action-btn {
+ display: flex;
+ align-items: center;
+ justify-content: center;
+ width: 40px;
+ height: 40px;
+ border: none;
+ background: #f8f9fa;
+ border-radius: 50%;
+ cursor: pointer;
+ transition: background-color 0.2s;
+ color: #666;
+}
+
+.action-btn:hover {
+ background: #e9ecef;
+}
+
+.action-btn.active {
+ background: #ff4757;
+ color: white;
+}
+
+/* 主要内容区 */
+.post-content {
+ flex: 1;
+ padding: 20px;
+}
+
+.post-title {
+ font-size: 24px;
+ font-weight: 700;
+ line-height: 1.4;
+ margin-bottom: 20px;
+ color: #333;
+}
+
+/* 帖子元信息 */
+.post-meta {
+ display: flex;
+ justify-content: space-between;
+ align-items: center;
+ margin-bottom: 20px;
+ padding-bottom: 16px;
+ border-bottom: 1px solid #f0f0f0;
+}
+
+.author-info {
+ display: flex;
+ align-items: center;
+ gap: 12px;
+}
+
+.avatar {
+ width: 40px;
+ height: 40px;
+ border-radius: 50%;
+ background: #ff4757;
+ color: white;
+ display: flex;
+ align-items: center;
+ justify-content: center;
+ font-weight: 600;
+ font-size: 16px;
+}
+
+.author-details {
+ display: flex;
+ flex-direction: column;
+ gap: 2px;
+}
+
+.author-name {
+ font-weight: 600;
+ color: #333;
+ font-size: 14px;
+}
+
+.post-date {
+ font-size: 12px;
+ color: #666;
+}
+
+.post-stats {
+ display: flex;
+ gap: 16px;
+}
+
+.stat-item {
+ display: flex;
+ align-items: center;
+ gap: 4px;
+ font-size: 14px;
+ color: #666;
+}
+
+/* 标签 */
+.post-tags {
+ display: flex;
+ flex-wrap: wrap;
+ gap: 8px;
+ margin-bottom: 20px;
+}
+
+.tag {
+ padding: 4px 12px;
+ background: #f8f9fa;
+ border-radius: 16px;
+ font-size: 12px;
+ color: #666;
+ border: 1px solid #e9ecef;
+}
+
+/* 帖子正文 */
+.post-body {
+ margin-bottom: 24px;
+ line-height: 1.6;
+ color: #333;
+ font-size: 16px;
+}
+
+.post-body p {
+ margin-bottom: 16px;
+}
+
+/* 类别信息 */
+.post-category {
+ margin-bottom: 20px;
+ padding: 12px;
+ background: #f8f9fa;
+ border-radius: 8px;
+ font-size: 14px;
+}
+
+.category-label {
+ color: #666;
+ font-weight: 500;
+}
+
+.category-name {
+ color: #333;
+ font-weight: 600;
+}
+
+/* 评论区 */
+.comments-section {
+ margin-top: 32px;
+ padding-top: 24px;
+ border-top: 1px solid #eee;
+}
+
+.comments-header {
+ margin-bottom: 20px;
+}
+
+.comments-toggle {
+ display: flex;
+ align-items: center;
+ gap: 8px;
+ padding: 10px 16px;
+ border: 1px solid #e9ecef;
+ background: #f8f9fa;
+ border-radius: 8px;
+ cursor: pointer;
+ transition: background-color 0.2s;
+ font-size: 14px;
+ color: #333;
+}
+
+.comments-toggle:hover {
+ background: #e9ecef;
+}
+
+.comments-content {
+ margin-top: 16px;
+}
+
+/* 评论表单 */
+.comment-form {
+ margin-bottom: 24px;
+ padding: 16px;
+ background: #f8f9fa;
+ border-radius: 8px;
+}
+
+.comment-input {
+ width: 100%;
+ padding: 12px;
+ border: 1px solid #dee2e6;
+ border-radius: 6px;
+ resize: vertical;
+ font-family: inherit;
+ font-size: 14px;
+ margin-bottom: 12px;
+ outline: none;
+ transition: border-color 0.2s;
+}
+
+.comment-input:focus {
+ border-color: #ff4757;
+}
+
+.comment-submit {
+ padding: 8px 16px;
+ background: #ff4757;
+ color: white;
+ border: none;
+ border-radius: 6px;
+ cursor: pointer;
+ font-size: 14px;
+ transition: background-color 0.2s;
+}
+
+.comment-submit:hover {
+ background: #e84c4a;
+}
+
+/* 评论列表 */
+.comments-list {
+ display: flex;
+ flex-direction: column;
+ gap: 16px;
+}
+
+.no-comments {
+ text-align: center;
+ color: #666;
+ font-style: italic;
+ padding: 32px 0;
+}
+
+.comment-item {
+ padding: 16px;
+ background: #fff;
+ border: 1px solid #e9ecef;
+ border-radius: 8px;
+}
+
+.comment-author {
+ display: flex;
+ align-items: center;
+ gap: 8px;
+ margin-bottom: 8px;
+}
+
+.comment-avatar {
+ width: 32px;
+ height: 32px;
+ border-radius: 50%;
+ background: #6c757d;
+ color: white;
+ display: flex;
+ align-items: center;
+ justify-content: center;
+ font-weight: 600;
+ font-size: 14px;
+}
+
+.comment-name {
+ font-weight: 600;
+ color: #333;
+ font-size: 14px;
+}
+
+.comment-time {
+ font-size: 12px;
+ color: #666;
+ margin-left: auto;
+}
+
+.comment-content {
+ font-size: 14px;
+ line-height: 1.5;
+ color: #333;
+ margin-left: 40px;
+}
+
+/* 底部操作栏 */
+.post-footer {
+ padding: 16px 20px;
+ border-top: 1px solid #eee;
+ background: #fff;
+ position: sticky;
+ bottom: 0;
+}
+
+.action-bar {
+ display: flex;
+ justify-content: space-around;
+ align-items: center;
+ max-width: 400px;
+ margin: 0 auto;
+}
+
+.action-button {
+ display: flex;
+ flex-direction: column;
+ align-items: center;
+ gap: 4px;
+ padding: 8px 12px;
+ border: none;
+ background: transparent;
+ cursor: pointer;
+ transition: color 0.2s;
+ color: #666;
+ font-size: 12px;
+}
+
+.action-button:hover {
+ color: #333;
+}
+
+.action-button.liked {
+ color: #ff4757;
+}
+
+.action-button.bookmarked {
+ color: #ffa502;
+}
+
+/* 响应式设计 */
+@media (max-width: 768px) {
+ .post-detail {
+ margin: 0;
+ }
+
+ .post-content {
+ padding: 16px;
+ }
+
+ .post-title {
+ font-size: 20px;
+ }
+
+ .post-meta {
+ flex-direction: column;
+ align-items: flex-start;
+ gap: 12px;
+ }
+
+ .post-stats {
+ align-self: flex-end;
+ }
+
+ .action-bar {
+ max-width: none;
+ }
+
+ .comment-content {
+ margin-left: 0;
+ margin-top: 8px;
+ }
+}