feat: 加入了download_model.py进行模型的下载和训练，修复了recommend.py的bug
Change-Id: I72fb3fdb160ff73934396e2127ae6432e8a727c8
diff --git a/recommend/demo.py b/recommend/demo.py
new file mode 100644
index 0000000..b64ec6b
--- /dev/null
+++ b/recommend/demo.py
@@ -0,0 +1,149 @@
+import os
+import time
+import jieba
+import fasttext
+from flask import Flask, request, jsonify
+import mysql.connector
+
+# ✅ 初始化数据库连接
+import mysql.connector
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+
+def compute_similarity(vec1, vec2):
+ """计算两个词向量之间的余弦相似度"""
+ # 使用 cosine_similarity 计算相似度
+ return cosine_similarity([vec1], [vec2])[0][0]
+
+db = mysql.connector.connect(
+ host="49.233.215.144",
+ port=3306,
+ user="sy",
+ password="sy_password",
+ database="pt_station"
+)
+
+cursor = db.cursor()
+
+
+app = Flask(__name__)
+
+# ✅ 加载 fastText 模型
+fasttext_model_path = './models/cc.zh.300.bin'
+if not os.path.exists(fasttext_model_path):
+ raise FileNotFoundError("fastText 模型文件不存在，请检查路径。")
+
+print("加载 fastText 模型中...")
+ft_model = fasttext.load_model(fasttext_model_path)
+print("模型加载完成 ✅")
+fasttext_vocab = set(ft_model.words)
+
+# ✅ 全局标签缓存（用于避免频繁查询）
+existing_tags = set()
+
+def refresh_existing_tags():
+ """刷新数据库中已存在的标签集合"""
+ global existing_tags
+ cursor.execute("SELECT DISTINCT tag FROM user_tag_scores")
+ existing_tags = set(tag[0] for tag in cursor.fetchall())
+ print(f"已加载标签数: {len(existing_tags)}")
+
+# ✅ 启动时初始化标签缓存
+refresh_existing_tags()
+
+# ✅ 扩展函数：仅保留数据库已有标签
+def expand_tags_from_input(input_tags, topn=5, similarity_threshold=0.7):
+ """
+ 扩展输入标签列表，查找与之语义相似的标签，返回相似度大于阈值或最相似的前 n 个标签。
+
+ :param input_tags: 输入标签的列表，例如 ['电影', '动漫', '游戏', '1080p']
+ :param topn: 返回的最相似标签的数量，默认为 5
+ :param similarity_threshold: 相似度阈值，默认为 0.7
+ :return: 返回与输入标签相关的扩展标签列表
+ """
+ # 用于存储所有扩展标签及其相似度
+ tag_scores = {}
+
+ for tag in input_tags:
+ # 获取当前标签的词向量
+ tag_vector = ft_model.get_word_vector(tag)
+
+ # 遍历标签库中的所有标签并计算相似度
+ for db_tag in existing_tags:
+ db_tag_vector = ft_model.get_word_vector(db_tag)
+ similarity = compute_similarity(tag_vector, db_tag_vector)
+
+ if similarity >= similarity_threshold:
+ if db_tag not in tag_scores:
+ tag_scores[db_tag] = similarity
+ else:
+ tag_scores[db_tag] = max(tag_scores[db_tag], similarity)
+
+ # 根据相似度排序并返回前 n 个标签
+ sorted_tags = sorted(tag_scores.items(), key=lambda x: x[1], reverse=True)
+ top_tags = [tag for tag, _ in sorted_tags[:topn]]
+
+ return top_tags
+
+
+# ✅ 接口路由
+@app.route("/expand_tags", methods=["POST"])
+def expand_tags():
+ start_time = time.time()
+
+ # 从请求中获取数据
+ data = request.get_json()
+ input_tags = data.get("tags", [])
+ user_id = data.get("user_id")
+ topn = data.get("topn", 10) # 默认为 5
+ similarity_threshold = 0.4 # 默认阈值为 0.7
+
+ if not input_tags or not user_id:
+ return jsonify({"error": "缺少参数 tags 或 user_id"}), 400
+
+ # 获取与输入标签最相关的标签
+ expanded_tags = expand_tags_from_input(input_tags, topn=topn, similarity_threshold=similarity_threshold)
+
+ # 打印日志
+ print(f"[用户 {user_id}] 输入标签: {input_tags}")
+ print(f"[用户 {user_id}] 匹配扩展标签: {expanded_tags}")
+
+ # 数据写入打分逻辑
+ token_set = set(input_tags) # 用于确定哪些标签是用户输入的标签
+ for tag in expanded_tags:
+ score = 2.0 if tag in token_set else 1.0
+ try:
+ cursor.execute("""
+ INSERT INTO user_tag_scores (user_id, tag, score)
+ VALUES (%s, %s, %s)
+ ON DUPLICATE KEY UPDATE score = score + VALUES(score)
+ """, (user_id, tag, score))
+ except Exception as e:
+ print(f"插入失败 [{tag}]:", e)
+ db.commit()
+ # ⏳ 插入后立即查询该用户的所有标签和评分
+ try:
+ cursor.execute("""
+ SELECT tag, score FROM user_tag_scores WHERE user_id = %s
+ """, (user_id,))
+ user_scores = [{"tag": tag, "score": float(score)} for tag, score in cursor.fetchall()]
+ print(user_scores)
+ except Exception as e:
+ print(f"查询用户评分失败: {e}")
+
+ duration = round(time.time() - start_time, 3)
+ return jsonify({
+ "expanded_tags": expanded_tags,
+ })
+
+
+# ✅ 触发标签缓存刷新（可选：手动/接口/定时任务调用）
+@app.route("/refresh_tags", methods=["POST"])
+def refresh_tags():
+ refresh_existing_tags()
+ return jsonify({"status": "标签缓存已刷新", "count": len(existing_tags)})
+
+# ✅ 启动服务
+if __name__ == "__main__":
+ from waitress import serve
+ serve(app, host="0.0.0.0", port=5000, threads=16)