融合门户系统与智慧技术的深度对话

from flask import Flask, request, jsonify

app = Flask(__name__)

# 模拟数据库

users = {

"admin": "123456"

}

@app.route('/login', methods=['POST'])

def login():

data = request.json

username = data.get('username')

password = data.get('password')

if username in users and users[username] == password:

return jsonify({"status": "success", "message": "登录成功"})

else:

return jsonify({"status": "error", "message": "用户名或密码错误"})

if __name__ == '__main__':

app.run(debug=True)

# 假设有一个用户-物品评分矩阵

ratings = {

'user1': {'itemA': 5, 'itemB': 3, 'itemC': 4},

'user2': {'itemA': 4, 'itemB': 2, 'itemD': 5},

'user3': {'itemB': 4, 'itemC': 3, 'itemD': 4}

}

def recommend_items(user):

# 计算相似度

similarities = {}

for other_user in ratings:

if other_user != user:

common_items = set(ratings[user].keys()) & set(ratings[other_user].keys())

if len(common_items) > 0:

dot_product = sum(ratings[user][item] * ratings[other_user][item] for item in common_items)

norm_user = sum(r ** 2 for r in ratings[user].values()) ** 0.5

norm_other = sum(r ** 2 for r in ratings[other_user].values()) ** 0.5

similarity = dot_product / (norm_user * norm_other)

similarities[other_user] = similarity

# 推荐未评分的物品

recommended_items = {}

for item in set([item for user_ratings in ratings.values() for item in user_ratings]):

if item not in ratings[user]:

weighted_sum = 0

total_similarity = 0

for other_user, sim in similarities.items():

if item in ratings[other_user]:

weighted_sum += sim * ratings[other_user][item]

total_similarity += sim

if total_similarity > 0:

recommended_items[item] = weighted_sum / total_similarity

return sorted(recommended_items.items(), key=lambda x: x[1], reverse=True)

# 示例：为 user1 推荐物品

print(recommend_items('user1'))

# 使用官方的Python镜像作为基础

FROM python:3.9-slim

# 设置工作目录

WORKDIR /app

# 复制 requirements.txt 文件

COPY requirements.txt .

# 安装依赖

RUN pip install --no-cache-dir -r requirements.txt

# 复制应用代码

COPY . .

# 暴露端口

EXPOSE 5000

# 启动应用

CMD ["python", "app.py"]

apiVersion: apps/v1

kind: Deployment

metadata:

name: flask-app

spec:

replicas: 2

selector:

matchLabels:

app: flask

template:

metadata:

labels:

app: flask

spec:

containers:

- name: flask

image: your-dockerhub-username/flask-app:latest

ports:

- containerPort: 5000

env:

- name: FLASK_ENV

value: "production"

---

apiVersion: v1

kind: Service

metadata:

name: flask-service

spec:

type: LoadBalancer

selector:

app: flask

ports:

- protocol: TCP

port: 80

targetPort: 5000