Revert "feat: 完整集成JWLLL搜索推荐系统到Merge项目"
This reverts commit b2ef519aa46c958768dba291676a9a4590d2b9ff.
Reason for revert: <错误的接口修改>
Change-Id: Ie3e7748c5331c509f45757792af5fe9e17172953
diff --git a/Merge/back_jwlll/README.md b/Merge/back_jwlll/README.md
deleted file mode 100644
index 9f19db8..0000000
--- a/Merge/back_jwlll/README.md
+++ /dev/null
@@ -1,64 +0,0 @@
-# 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
deleted file mode 100644
index 940c564..0000000
--- a/Merge/back_jwlll/app.py
+++ /dev/null
@@ -1,1076 +0,0 @@
-# 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
deleted file mode 100644
index 77cb8dc..0000000
--- a/Merge/back_jwlll/config.py
+++ /dev/null
@@ -1,38 +0,0 @@
-# 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
deleted file mode 100644
index 6e7bd22..0000000
--- a/Merge/back_jwlll/requirements.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-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
deleted file mode 100644
index f5e34d3..0000000
--- a/Merge/back_jwlll/semantic_config.json
+++ /dev/null
@@ -1,77 +0,0 @@
-{
- "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
deleted file mode 100644
index ecd1a72..0000000
--- a/Merge/back_jwlll/word2vec_helper.py
+++ /dev/null
@@ -1,279 +0,0 @@
-# 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 9295682..ba17fcf 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 89f68ad..6ed8964 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
deleted file mode 100644
index 9886994..0000000
--- a/Merge/back_rhj/requirements.txt
+++ /dev/null
@@ -1,10 +0,0 @@
-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
deleted file mode 100644
index 5ab7eb1..0000000
--- a/Merge/front/src/api/search_jwlll.js
+++ /dev/null
@@ -1,97 +0,0 @@
-// 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 c11e247..1d2f306 100644
--- a/Merge/front/src/components/CreatePost.jsx
+++ b/Merge/front/src/components/CreatePost.jsx
@@ -13,8 +13,10 @@
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
@@ -51,8 +53,10 @@
.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 e32a2eb..c681858 100644
--- a/Merge/front/src/components/HomeFeed.jsx
+++ b/Merge/front/src/components/HomeFeed.jsx
@@ -1,10 +1,9 @@
// src/components/HomeFeed.jsx
-import React, { useState, useEffect, useCallback } from 'react'
+import React, { useState, useEffect } 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 = [
@@ -12,120 +11,15 @@
'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(() => {
- // 原始数据加载函数
- const loadPosts = async () => {
+ async function loadPosts() {
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
@@ -149,149 +43,25 @@
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);
- setSearch('');
- if (useSearchRecommend) {
- if (cat === '推荐') {
- fetchUserTagsAndRecommend()
- } else {
- fetchSearchContent()
- }
- }
- }}
+ onClick={() => setActiveCat(cat)}
>
{cat}
</button>
))}
- </nav> {/* 状态提示 */}
+ </nav>
+
+ {/* 状态提示 */}
{loading ? (
<div className="loading">加载中…</div>
) : error ? (
@@ -299,27 +69,22 @@
) : (
/* 瀑布流卡片区 */
<div className="feed-grid">
- {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>
+ {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>
</div>
</div>
- ))
- )}
+ </div>
+ ))}
</div>
)}
</div>
diff --git a/Merge/front/src/components/LogsDashboard.js b/Merge/front/src/components/LogsDashboard.js
index 6ab4746..22047e2 100644
--- a/Merge/front/src/components/LogsDashboard.js
+++ b/Merge/front/src/components/LogsDashboard.js
@@ -3,9 +3,7 @@
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
deleted file mode 100644
index 0dc7289..0000000
--- a/Merge/front/src/components/PostDetailJWLLL.jsx
+++ /dev/null
@@ -1,322 +0,0 @@
-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 e35db63..92bc8f1 100644
--- a/Merge/front/src/components/Sidebar.jsx
+++ b/Merge/front/src/components/Sidebar.jsx
@@ -7,8 +7,6 @@
Activity,
Users,
ChevronDown,
- Search,
- Upload,
} from 'lucide-react'
import '../App.css'
@@ -26,7 +24,6 @@
{ 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
deleted file mode 100644
index 2d9ee7d..0000000
--- a/Merge/front/src/components/UploadPageJWLLL.jsx
+++ /dev/null
@@ -1,328 +0,0 @@
-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 efc4067..d7f5f09 100644
--- a/Merge/front/src/router/App.js
+++ b/Merge/front/src/router/App.js
@@ -12,8 +12,6 @@
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'
@@ -43,13 +41,13 @@
{/* 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 394c84b..f1bf75d 100644
--- a/Merge/front/src/style/HomeFeed.css
+++ b/Merge/front/src/style/HomeFeed.css
@@ -113,95 +113,4 @@
.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
deleted file mode 100644
index 1be7126..0000000
--- a/Merge/front/src/style/PostDetail.css
+++ /dev/null
@@ -1,436 +0,0 @@
-/* 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;
- }
-}