锦中人工智能助手

# 导入必要的库

import spacy

# 加载英文语言模型

nlp = spacy.load("en_core_web_sm")

# 示例问题

question = "What is the schedule for tomorrow?"

# 使用spaCy处理文本

doc = nlp(question)

# 提取实体和意图

entities = [ent.text for ent in doc.ents]

print("Entities:", entities)

print("Dependencies:", [(token.text, token.dep_, token.head.text) for token in doc])

# 加载中文语言模型

nlp_zh = spacy.load("zh_core_web_sm")

# 中文问题示例

question_zh = "明天的课程安排是什么？"

# 处理中文文本

doc_zh = nlp_zh(question_zh)

# 提取实体和依赖关系

entities_zh = [ent.text for ent in doc_zh.ents]

print("Entities (Chinese):", entities_zh)

print("Dependencies (Chinese):", [(token.text, token.dep_, token.head.text) for token in doc_zh])

from flask import Flask, request, jsonify

import mysql.connector

app = Flask(__name__)

# 数据库连接配置

db_config = {

'host': 'localhost',

'user': 'root',

'password': '123456',

'database': 'university_db'

}

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

def query():

data = request.json

question = data.get('question')

# 连接数据库

conn = mysql.connector.connect(**db_config)

cursor = conn.cursor()

# 查询课程表

cursor.execute("SELECT * FROM schedule WHERE date = CURDATE() + INTERVAL 1 DAY")

results = cursor.fetchall()

cursor.close()

conn.close()

return jsonify({"response": "Tomorrow's schedule: " + str(results)})

if __name__ == '__main__':

app.run(debug=True)

from transformers import BertTokenizer, TFBertForSequenceClassification

import tensorflow as tf

# 加载预训练的BERT模型和分词器

model_name = 'bert-base-uncased'

tokenizer = BertTokenizer.from_pretrained(model_name)

model = TFBertForSequenceClassification.from_pretrained(model_name)

# 示例输入

text = "What is the schedule for tomorrow?"

# 分词和编码

inputs = tokenizer(text, return_tensors="tf")

# 模型预测

outputs = model(inputs)

predictions = tf.nn.softmax(outputs.logits, axis=1)

print("Predicted class:", predictions.numpy())