Optimize model loading and prediction performance, implement the singleton pattern, and provide comprehensive error handling and error messages, along with confidence level display.
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戒酒的李白
+55
-73
@@ -1,4 +1,4 @@
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from flask import Flask, session, render_template, redirect, Blueprint, request
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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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@@ -9,9 +9,7 @@ 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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from BCAT_front.predict import model_manager
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pb = Blueprint('page',
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__name__,
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@@ -21,47 +19,26 @@ pb = Blueprint('page',
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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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# 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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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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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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print(f"模型加载失败: {e}")
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logging.error(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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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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print(f"预测过程中出现错误: {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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@@ -218,46 +195,51 @@ def yuqingChar():
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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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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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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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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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# 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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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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