锦中人工智能助手

# app.py

from flask import Flask, jsonify

import requests

app = Flask(__name__)

@app.route('/api/announcement', methods=['GET'])

def get_announcement():

url = 'https://api.example.edu/announcement'

response = requests.get(url)

if response.status_code == 200:

return jsonify(response.json())

else:

return jsonify({'error': '无法获取公告'}), 500

if __name__ == '__main__':

app.run(debug=True)

# nlu.py

import nltk

from nltk.chat.util import Chat, reflections

# 定义一些常见意图

pairs = [

['今天有什么会议？', ['查询今日会议']],

['我想查看课程表', ['查询课程表']],

['帮我找图书馆的书', ['查询图书资源']],

['我需要请假', ['提交请假申请']],

['帮我查成绩', ['查询成绩']],

]

chatbot = Chat(pairs, reflections)

def respond(message):

return chatbot.respond(message)

if __name__ == '__main__':

while True:

msg = input("你: ")

print("助手:", respond(msg))

# intent_classification.py

from transformers import pipeline

# 加载预训练模型

classifier = pipeline('text-classification', model='bert-base-uncased')

# 示例文本

text = "今天有什么会议？"

# 进行意图分类

result = classifier(text)

print("意图分类结果:", result[0]['label'], "置信度:", result[0]['score'])

# neo4j_example.py

from py2neo import Graph, Node, Relationship

graph = Graph("http://localhost:7474", auth=("neo4j", "password"))

# 创建节点

student = Node("Student", name="张三")

course = Node("Course", name="计算机科学导论")

# 创建关系

relationship = Relationship(student, "ENROLLED_IN", course)

# 保存到数据库

graph.create(relationship)

# recommendation.py

from py2neo import Graph

graph = Graph("http://localhost:7474", auth=("neo4j", "password"))

def recommend_courses(user_name):

query = """

MATCH (u:Student {name: $user})-[:ENROLLED_IN]->(c:Course)

WITH c

MATCH (c)-[:RELATED_TO]->(r:Course)

RETURN r.name AS recommended_course

LIMIT 5

"""

results = graph.run(query, user=user_name)

return [record['recommended_course'] for record in results]

# 示例调用

recommendations = recommend_courses("张三")

print("推荐课程:", recommendations)