锦中人工智能助手

from flask import Flask, request, jsonify

app = Flask(__name__)

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

def handle_query():

data = request.get_json()

query = data.get('query')

response = ""

if "课程" in query:

response = "您需要查询课程安排，请访问教务系统或联系教务处。"

elif "图书馆" in query:

response = "您可以登录图书馆官网预约座位或查阅书籍。"

elif "请假" in query:

response = "请填写请假申请表并提交给辅导员。"

else:

response = "抱歉，我暂时无法处理您的请求，请尝试更具体的问题。"

return jsonify({"response": response})

if __name__ == '__main__':

app.run(debug=True)

import sqlite3

def init_db():

conn = sqlite3.connect('assistant.db')

c = conn.cursor()

c.execute('''CREATE TABLE IF NOT EXISTS questions

(id INTEGER PRIMARY KEY AUTOINCREMENT,

question TEXT,

answer TEXT)''')

conn.commit()

conn.close()

def add_question(question, answer):

conn = sqlite3.connect('assistant.db')

c = conn.cursor()

c.execute("INSERT INTO questions (question, answer) VALUES (?, ?)", (question, answer))

conn.commit()

conn.close()

def get_answer(query):

conn = sqlite3.connect('assistant.db')

c = conn.cursor()

c.execute("SELECT answer FROM questions WHERE question LIKE ?", ('%' + query + '%',))

result = c.fetchone()

conn.close()

return result[0] if result else None

from transformers import pipeline

# 加载预训练的意图分类模型

intent_classifier = pipeline("text-classification", model="distilbert-base-uncased")

def classify_intent(text):

result = intent_classifier(text)

return result[0]['label']