recommend/demo.py

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)