戒酒的李白
403 lines
14 KiB
Python
403 lines
14 KiB
Python
from flask import Flask, session, render_template, redirect, Blueprint, request, jsonify
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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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from utils.cache_manager import prediction_cache
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from utils.ai_analyzer import ai_analyzer
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from models.ai_analysis import AIAnalysis
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from sqlalchemy.orm import Session
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from sqlalchemy import create_engine
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import asyncio
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import torch
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from BCAT_front.predict import model_manager
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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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# 设置模型路径
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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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# 初始化模型
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try:
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model_manager.load_models(model_save_path, bert_model_path, ctm_tokenizer_path)
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except Exception as e:
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logging.error(f"模型加载失败: {e}")
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# 数据库配置
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DATABASE_URL = "sqlite:///ai_analysis.db"
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engine = create_engine(DATABASE_URL)
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AIAnalysis.metadata.create_all(engine)
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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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predictions, probabilities = model_manager.predict_batch([text])
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if predictions is not None and len(predictions) > 0:
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return predictions[0], probabilities[0][predictions[0]]
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return None, None
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except Exception as e:
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logging.error(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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try:
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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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# 尝试从缓存获取预测结果
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cache_key = f"{defaultTopic}_{model_type}"
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cached_result = prediction_cache.get(cache_key)
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if cached_result is not None:
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sentences = cached_result
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else:
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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:.2%})"
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else:
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sentences = '预测失败，请稍后重试'
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logging.error(f"预测失败，话题: {defaultTopic}")
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# 将结果存入缓存
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prediction_cache.set(cache_key, sentences)
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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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except Exception as e:
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logging.error(f"舆情预测页面渲染失败: {e}")
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return render_template('error.html', error_message="加载舆情预测页面失败，请稍后重试")
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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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@pb.route('/api/analyze_messages', methods=['POST'])
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async def analyze_messages():
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try:
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# 获取最近50条消息
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messages = getRecentMessages(50) # 需要实现这个函数
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# 调用AI进行分析
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analysis_results = await ai_analyzer.analyze_messages(messages)
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# 保存到数据库
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with Session(engine) as session:
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for result in analysis_results:
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analysis = AIAnalysis(
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message_id=result['message_id'],
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sentiment=result['sentiment'],
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sentiment_score=float(result['sentiment_score']),
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keywords=result['keywords'],
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key_points=result['key_points'],
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influence_analysis=result['influence_analysis'],
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risk_level=result['risk_level']
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)
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session.add(analysis)
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session.commit()
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# 格式化结果用于显示
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display_results = [
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ai_analyzer.format_analysis_for_display(result)
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for result in analysis_results
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]
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return jsonify({
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'success': True,
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'data': display_results
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})
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except Exception as e:
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logging.error(f"AI分析过程出错: {e}")
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return jsonify({
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'success': False,
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'error': str(e)
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}), 500
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@pb.route('/api/get_analysis/<int:message_id>')
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def get_message_analysis(message_id):
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"""获取特定消息的分析结果"""
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try:
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with Session(engine) as session:
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analysis = session.query(AIAnalysis)\
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.filter(AIAnalysis.message_id == message_id)\
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.order_by(AIAnalysis.created_at.desc())\
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.first()
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if analysis:
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return jsonify({
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'success': True,
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'data': analysis.to_dict()
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})
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else:
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return jsonify({
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'success': False,
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'error': '未找到分析结果'
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}), 404
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except Exception as e:
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logging.error(f"获取分析结果时出错: {e}")
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return jsonify({
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'success': False,
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'error': str(e)
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}), 500
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def getRecentMessages(limit=50):
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"""获取最近的消息"""
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# 这里需要根据你的数据库结构实现具体的查询逻辑
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messages = []
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try:
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# 示例查询逻辑
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with Session(engine) as session:
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results = session.execute(
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"""
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SELECT id, content
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FROM comments
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ORDER BY created_at DESC
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LIMIT :limit
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""",
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{'limit': limit}
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).fetchall()
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messages = [
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{'id': row[0], 'content': row[1]}
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for row in results
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]
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except Exception as e:
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logging.error(f"获取最近消息时出错: {e}")
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return messages
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