学生信息管理系统

import re

def extract_student_info(text):

name_pattern = r'姓名：(.*?)\n'

student_id_pattern = r'学号：(\d{8})\n'

name = re.search(name_pattern, text).group(1)

student_id = re.search(student_id_pattern, text).group(1)

return {

'name': name,

'student_id': student_id

}

# 示例文本

text = "姓名：张三\n学号：20210001\n专业：计算机科学"

info = extract_student_info(text)

print(info)

from PIL import Image

import pytesseract

# 打开图片

image = Image.open('student_info.png')

# 使用pytesseract进行OCR

text = pytesseract.image_to_string(image)

print(text)

import pandas as pd

from sklearn.linear_model import LinearRegression

# 加载数据

data = pd.read_csv('student_grades.csv')

# 特征和标签

X = data[['study_hours', 'attendance']]

y = data['final_grade']

# 创建模型

model = LinearRegression()

model.fit(X, y)

# 预测新数据

new_data = [[5, 90], [3, 80]]

predictions = model.predict(new_data)

print(predictions)

from sklearn.ensemble import RandomForestRegressor

# 创建随机森林模型

rf_model = RandomForestRegressor(n_estimators=100)

rf_model.fit(X, y)

# 预测新数据

new_data = [[5, 90], [3, 80]]

predictions = rf_model.predict(new_data)

print(predictions)

from sklearn.metrics.pairwise import cosine_similarity

import numpy as np

# 假设我们有学生的历史选课数据

student_courses = {

'A': ['数学', '物理'],

'B': ['语文', '英语'],

'C': ['数学', '英语']

}

# 构建特征矩阵

courses = set([course for courses in student_courses.values() for course in courses])

matrix = np.zeros((len(student_courses), len(courses)))

for i, student in enumerate(student_courses):

for j, course in enumerate(courses):

if course in student_courses[student]:

matrix[i, j] = 1

# 计算相似度

similarity = cosine_similarity(matrix)

print(similarity)

# domain.yml

intents:

- greet

- goodbye

- ask_course

- ask_schedule

responses:

utter_greet:

- text: "你好！我是学生助手，有什么可以帮助你的吗？"

utter_goodbye:

- text: "再见！如有需要，请随时联系我。"

utter_course:

- text: "当前可选课程包括：数学、物理、英语、编程。"

utter_schedule:

- text: "课程时间表如下：周一上午9点，周三下午2点。"

actions:

- action_default_fallback