一站式网上办事大厅

def check_leave_request(request_text):

# 使用NLP模型判断请求内容是否合理

import nltk

from nltk.sentiment import SentimentIntensityAnalyzer

nltk.download('vader_lexicon')

sid = SentimentIntensityAnalyzer()

sentiment = sid.polarity_scores(request_text)

if sentiment['neg'] > 0.3:

return "该请求情绪负面，需人工审核"

elif "病假" in request_text or "医疗" in request_text:

return "该请求为病假，需提供医疗证明"

elif "紧急" in request_text or "突发" in request_text:

return "该请求为紧急情况，需快速审批"

else:

return "该请求为正常请假，可自动审批"

# 示例调用

request = "我因感冒需要请假两天，请批准。"

result = check_leave_request(request)

print(result)

from transformers import pipeline

# 加载预训练的文本分类模型

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

def classify_leave_request(text):

result = classifier(text)[0]

if result['label'] == 'LABEL_1':

return "该请求为病假或特殊情况，需人工审核"

else:

return "该请求为正常请假，可自动审批"

# 示例调用

request = "我因为家庭原因需要请假一天。"

classification_result = classify_leave_request(request)

print(classification_result)

CREATE TABLE leave_requests (

id INT AUTO_INCREMENT PRIMARY KEY,

student_id VARCHAR(20) NOT NULL,

start_date DATE NOT NULL,

end_date DATE NOT NULL,

reason TEXT NOT NULL,

status ENUM('pending', 'approved', 'rejected') DEFAULT 'pending',

approver_id VARCHAR(20),

created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP

);