为了实现更智能的分类，我们需要改进分类器，使其能够处理用户输入的任意文本，并自动分类到相应的类别。可以通过增加训练数据并改进数据预处理和分类器训练来实现这一点。

改进步骤

1. 增加训练数据：增加更多样化的训练数据，以提高分类器的准确性。
2. 改进数据预处理：考虑更多的文本特征。
3. 使用更高级的机器学习模型：可以考虑使用sklearn中的其他分类算法或者使用深度学习模型。

import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.metrics import accuracy_score
from flask import Flask, render_template, request, jsonify
import os
import pandas as pd
from datetime import datetime# 初始化nltk
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')# 初始化分类器和评分模型
classifier = None
scorer = None# 初始化分类和评分标准
categories = ['工作', '生活']
score_criteria = {'工作': {'完成任务': 10,'解决问题': 5,'编程': 8,'其他': 1},'生活': {'锻炼': 5,'学习': 3,'休息': 2,'其他': 1}
}# 初始化基础分数和等级
base_score = 0
level = '初级'# 初始化日志文件和评分记录
daily_log = []
scores = []# 初始化TF-IDF向量化器和词形还原器
vectorizer = TfidfVectorizer()
lemmatizer = WordNetLemmatizer()# 检查并创建示例数据文件
def create_example_files():example_data = {'工作': ['完成工作任务\n','解决问题\n','编写代码\n','参加会议\n','其他工作相关活动\n'],'生活': ['锻炼身体\n','学习新知识\n','看电影\n','休息\n','其他生活相关活动\n']}for category, data in example_data.items():filename = f'{category}_log.txt'if not os.path.exists(filename):with open(filename, 'w', encoding='utf-8') as file:file.writelines(data)# 训练分类器
def train_classifier():global classifiercreate_example_files()# 读取训练数据training_data = []training_labels = []for category in categories:with open(f'{category}_log.txt', 'r', encoding='utf-8') as file:activities = file.readlines()training_data.extend(activities)training_labels.extend([category] * len(activities))# 数据预处理processed_data = preprocess_text(training_data)# 特征提取和训练分类器classifier = make_pipeline(TfidfVectorizer(), MultinomialNB())classifier.fit(processed_data, training_labels)# 预处理文本数据
def preprocess_text(data):processed_data = []for text in data:# 分词tokens = word_tokenize(text.lower())# 去除停用词和标点符号stop_words = set(stopwords.words('english'))filtered_tokens = [token for token in tokens if token.isalnum() and token not in stop_words]# 词形还原lemmatized_tokens = [lemmatizer.lemmatize(token) for token in filtered_tokens]# 重新组合为文本processed_text = ' '.join(lemmatized_tokens)processed_data.append(processed_text)return processed_data# 智能分类工作日常
def classify_activity(activity):processed_activity = preprocess_text([activity])category = classifier.predict(processed_activity)[0]return category# 根据分类和评分标准计算活动得分
def score_activity(activity, category):score = 0for word, points in score_criteria[category].items():if word in activity:score += pointsreturn score# 记录每天的工作日常到Excel
def log_daily_activity(activity):global base_score, level# 智能分类category = classify_activity(activity)# 计算得分score = score_activity(activity, category)# 更新基础分数和等级base_score += scoreif base_score >= 100:level = '高级'elif base_score >= 50:level = '中级'# 记录日志daily_log.append((activity, category, score))# 更新评分记录scores.append(score)# 获取当前时间timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')# 记录到Excellog_to_excel(timestamp, activity, category, score)# 记录数据到Excel文件
def log_to_excel(timestamp, activity, category, score):filename = 'activity_log.xlsx'if os.path.exists(filename):df = pd.read_excel(filename)else:df = pd.DataFrame(columns=['Timestamp', 'Activity', 'Category', 'Score'])new_entry = pd.DataFrame([[timestamp, activity, category, score]], columns=['Timestamp', 'Activity', 'Category', 'Score'])df = pd.concat([df, new_entry], ignore_index=True)df.to_excel(filename, index=False)# 创建Flask应用
app = Flask(__name__)@app.route('/')
def index():return render_template('index.html')@app.route('/log_activity', methods=['POST'])
def log_activity():activity = request.form['activity']log_daily_activity(activity)return jsonify({'base_score': base_score, 'level': level})@app.route('/simulate')
def simulate():global base_score, level# 模拟每天的活动记录activities = ['完成工作任务','锻炼身体','解决问题','学习新知识','编写代码','看电影','休息','参加会议','其他活动']for activity in activities:log_daily_activity(activity)# 返回当前分数和等级return jsonify({'base_score': base_score, 'level': level})if __name__ == '__main__':# 训练分类器和评分模型train_classifier()# 启动Flask应用app.run(debug=True)

HTML代码（templates/index.html）

我们将创建一个表单，让用户输入他们的活动，并通过Ajax发送到服务器：

<!DOCTYPE html>
<html lang="en">
<head><meta charset="UTF-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>升级动画</title><style>body {text-align: center;font-family: Arial, sans-serif;}.character {width: 200px;height: 200px;margin: 50px auto;border: 2px solid #000;border-radius: 10px;background-color: #f0f0f0;position: relative;}.character img {width: 100%;height: 100%;display: none;}.character img.active {display: block;}.level {font-size: 1.5em;margin-top: 20px;}.upgrade {font-size: 1.2em;color: green;}.form-container {margin: 20px auto;}</style>
</head>
<body><div class="character"><img src="/static/character_level1.png" id="level1" class="active"><img src="/static/character_level2.png" id="level2"><img src="/static/character_level3.png" id="level3"></div><div class="level" id="level">当前等级: 初级</div><div class="upgrade" id="upgrade"></div><div class="form-container"><form id="activity-form"><input type="text" id="activity" name="activity" placeholder="输入今天的活动" required><button type="submit">记录活动</button></form></div><script>function updateLevel(newLevel) {const levelImages = {'初级': document.getElementById('level1'),'中级': document.getElementById('level2'),'高级': document.getElementById('level3')};for (let level in levelImages) {if (level === newLevel) {levelImages[level].classList.add('active');document.getElementById('level').textContent = `当前等级: ${level}`;document.getElementById('upgrade').textContent = `升级到${level}!`;} else {levelImages[level].classList.remove('active');}}}function logActivity(event) {event.preventDefault();const activity = document.getElementById('activity').value;fetch('/log_activity', {method: 'POST',headers: {'Content-Type': 'application/x-www-form-urlencoded'},body: `activity=${encodeURIComponent(activity)}`}).then(response => response.json()).then(data => {updateLevel(data.level);document.getElementById('activity').value = '';});}document.getElementById('activity-form').addEventListener('submit', logActivity);</script>
</body>
</html>