戒酒的李白
304 lines
11 KiB
Python
304 lines
11 KiB
Python
from flask import Flask, session, render_template, redirect, Blueprint, request
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from utils.mynlp import SnowNLP
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from utils.getHomePageData import *
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from utils.getHotWordPageData import *
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from utils.getTableData import *
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from utils.getPublicData import getAllHotWords, getAllTopics, getArticleByType, getArticleById
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from utils.getEchartsData import *
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from utils.getTopicPageData import *
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from utils.yuqingpredict import *
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from utils.logger import app_logger as logging
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import torch
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from model_pro.MHA import MultiHeadAttentionLayer
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from model_pro.classifier import FinalClassifier
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from model_pro.BERT_CTM import BERT_CTM_Model
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pb = Blueprint('page',
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__name__,
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url_prefix='/page',
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template_folder='templates')
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# 设置设备
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# 加载模型（全局变量，避免重复加载）
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model_save_path = 'model_pro/final_model.pt'
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bert_model_path = 'model_pro/bert_model'
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ctm_tokenizer_path = 'model_pro/sentence_bert_model'
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try:
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classifier_model = torch.load(model_save_path, map_location=device)
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classifier_model.eval()
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attention_model = MultiHeadAttentionLayer(embed_size=768, num_heads=8)
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attention_model.to(device)
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attention_model.eval()
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bert_ctm_model = BERT_CTM_Model(
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bert_model_path=bert_model_path,
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ctm_tokenizer_path=ctm_tokenizer_path
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)
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except Exception as e:
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print(f"模型加载失败: {e}")
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def predict_sentiment(text):
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"""使用改进版模型预测单个文本的情感"""
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try:
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# 获取文本嵌入
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embeddings = bert_ctm_model.get_bert_embeddings([text])
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# 转换为tensor
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batch_x = torch.tensor(embeddings, dtype=torch.float32).to(device)
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batch_x = torch.mean(batch_x, dim=1)
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with torch.no_grad():
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# 使用注意力机制
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attention_output = attention_model(batch_x, batch_x, batch_x)
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# 获取分类结果
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outputs = classifier_model(attention_output)
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outputs = torch.mean(outputs, dim=1)
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# 获取预测标签和概率
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probabilities = torch.softmax(outputs, dim=1)
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_, predicted = torch.max(outputs, 1)
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return predicted.item(), probabilities[0][predicted.item()].item()
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except Exception as e:
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print(f"预测过程中出现错误: {e}")
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return None, None
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@pb.route('/home')
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def home():
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username = session.get('username')
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articleLenMax, likeCountMaxAuthorName, cityMax = getHomeTagsData()
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commentsLikeCountTopFore = getHomeCommentsLikeCountTopFore()
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X, Y = getHomeArticleCreatedAtChart()
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typeChart = getHomeTypeChart()
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createAtChart = getHomeCommentCreatedChart()
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# getUserNameWordCloud()
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return render_template('index.html',
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username=username,
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articleLenMax=articleLenMax,
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likeCountMaxAuthorName=likeCountMaxAuthorName,
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cityMax=cityMax,
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commentsLikeCountTopFore=commentsLikeCountTopFore,
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xData=X,
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yData=Y,
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typeChart=typeChart,
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createAtChart=createAtChart)
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@pb.route('/hotWord')
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def hotWord():
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username = session.get('username')
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hotWordList = getAllHotWords()
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print(hotWordList)
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defaultHotWord = hotWordList[0][0]
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if request.args.get('hotWord'):
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defaultHotWord = request.args.get('hotWord')
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hotWordLen = getHotWordLen(defaultHotWord)
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X, Y = getHotWordPageCreatedAtCharData(defaultHotWord)
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sentences = ''
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value = SnowNLP(defaultHotWord).sentiments
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if value == 0.5:
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sentences = '中性'
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elif value > 0.5:
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sentences = '正面'
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elif value < 0.5:
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sentences = '负面'
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comments = getCommentFilterData(defaultHotWord)
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return render_template('hotWord.html',
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username=username,
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hotWordList=hotWordList,
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defaultHotWord=defaultHotWord,
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hotWordLen=hotWordLen,
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sentences=sentences,
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xData=X,
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yData=Y,
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comments=comments)
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@pb.route('/hotTopic')
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def hotTopic():
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username = session.get('username')
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topicList = getAllTopics()
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defaultTopic = topicList[0][0]
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if request.args.get('topic'):
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defaultTopic = request.args.get('topic')
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topicLen = getTopicLen(defaultTopic)
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X, Y = getTopicPageCreatedAtCharData()
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sentences = ''
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# ... 这里要嵌入 topic 相关内容（热度？）来填充 sentences
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comments = getCommentFilterDataTopic(defaultTopic)
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return render_template('hotWord.html',
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username=username,
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topicList=topicList,
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defaultTopic=defaultTopic,
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topicLen=topicLen,
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sentences=sentences,
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xData=X,
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yData=Y,
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comments=comments)
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@pb.route('/tableData')
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def tableData():
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username = session.get('username')
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defaultFlag = False
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if request.args.get('flag'): defaultFlag = True
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tableData = getTableDataList(defaultFlag)
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return render_template('tableData.html',
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username=username,
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tableData=tableData,
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defaultFlag=defaultFlag)
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@pb.route('/articleChar')
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def articleChar():
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username = session.get('username')
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typeList = getTypeList()
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defaultType = typeList[0]
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if request.args.get('type'): defaultType = request.args.get('type')
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X, Y = getArticleLikeCount(defaultType)
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x1Data, y1Data = getArticleCommentsLen(defaultType)
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x2Data, y2Data = getArticleRepotsLen(defaultType)
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return render_template('articleChar.html',
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username=username,
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typeList=typeList,
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defaultType=defaultType,
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xData=X,
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yData=Y,
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x1Data=x1Data,
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y1Data=y1Data,
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x2Data=x2Data,
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y2Data=y2Data)
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@pb.route('/ipChar')
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def ipChar():
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username = session.get('username')
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articleRegionData = getIPByArticleRegion()
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commentRegionData = getIPByCommentsRegion()
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return render_template('ipChar.html',
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username=username,
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articleRegionData=articleRegionData,
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commentRegionData=commentRegionData)
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@pb.route('/commentChar')
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def commentChar():
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username = session.get('username')
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X, Y = getCommentDataOne()
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genderPieData = getCommentDataTwo()
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return render_template('commentChar.html',
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username=username,
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xData=X,
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yData=Y,
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genderPieData=genderPieData)
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@pb.route('/yuqingChar')
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def yuqingChar():
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username = session.get('username')
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# 获取模型选择参数
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model_type = request.args.get('model', 'pro') # 默认使用改进模型
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X, Y, biedata = getYuQingCharDataOne()
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biedata1, biedata2 = getYuQingCharDataTwo(model_type)
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x1Data, y1Data = getYuQingCharDataThree()
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return render_template('yuqingChar.html',
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username=username,
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xData=X,
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yData=Y,
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biedata=biedata,
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biedata1=biedata1,
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biedata2=biedata2,
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x1Data=x1Data,
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y1Data=y1Data,
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model_type=model_type)
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@pb.route('/yuqingpredict')
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def yuqingpredict():
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username = session.get('username')
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TopicList = getAllTopicData()
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defaultTopic = TopicList[0][0]
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if request.args.get('Topic'):
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defaultTopic = request.args.get('Topic')
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TopicLen = getTopicLen(defaultTopic)
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X, Y = getTopicCreatedAtandpredictData(defaultTopic)
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# 获取模型选择参数
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model_type = request.args.get('model', 'pro') # 默认使用改进模型
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if model_type == 'basic':
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# 使用基础模型（SnowNLP）
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value = SnowNLP(defaultTopic).sentiments
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if value == 0.5:
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sentences = '中性'
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elif value > 0.5:
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sentences = '正面'
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elif value < 0.5:
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sentences = '负面'
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else:
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# 使用改进模型
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predicted_label, confidence = predict_sentiment(defaultTopic)
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if predicted_label is not None:
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sentences = '良好' if predicted_label == 0 else '不良'
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sentences = f"{sentences} (置信度: {confidence:.2f})"
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else:
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sentences = '预测失败'
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comments = getCommentFilterDataTopic(defaultTopic)
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return render_template('yuqingpredict.html',
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username=username,
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hotWordList=TopicList,
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defaultHotWord=defaultTopic,
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hotWordLen=TopicLen,
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sentences=sentences,
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xData=X,
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yData=Y,
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comments=comments,
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model_type=model_type)
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@pb.route('/articleCloud')
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def articleCloud():
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username = session.get('username')
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return render_template('articleContentCloud.html', username=username)
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@pb.route('/page/index')
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def index():
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"""首页路由"""
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try:
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hotWordList = getAllHotWords()
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logging.info("成功获取热词列表")
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return render_template('index.html', hotWordList=hotWordList)
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except Exception as e:
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logging.error(f"渲染首页时发生错误: {e}")
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return render_template('error.html', error_message="加载首页失败")
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@pb.route('/page/article/<type>')
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def article(type):
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"""文章列表页路由"""
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try:
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articleList = getArticleByType(type)
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logging.info(f"成功获取类型为 {type} 的文章列表")
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return render_template('article.html', articleList=articleList)
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except Exception as e:
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logging.error(f"获取文章列表时发生错误: {e}")
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return render_template('error.html', error_message="加载文章列表失败")
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@pb.route('/page/articleChar/<id>')
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def articleChar(id):
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"""文章详情页路由"""
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try:
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article = getArticleById(id)
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if not article:
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logging.warning(f"未找到ID为 {id} 的文章")
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return render_template('error.html', error_message="文章不存在")
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logging.info(f"成功获取ID为 {id} 的文章详情")
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return render_template('articleChar.html', article=article)
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except Exception as e:
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logging.error(f"获取文章详情时发生错误: {e}")
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return render_template('error.html', error_message="加载文章详情失败")
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