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75 lines
2.3 KiB
75 lines
2.3 KiB
import torch
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from flask import Flask, request
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import torch.nn as nn
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import pickle
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import jieba
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app = Flask(__name__)
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# 初始化模型
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result = ""
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class Model(nn.Module):
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def __init__(self):
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super(Model, self).__init__()
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self.embedding = nn.Embedding(10002, 300, padding_idx=10001)
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self.lstm = nn.LSTM(300, 128, 2, bidirectional=True, batch_first=True, dropout=0.5)
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self.fc = nn.Linear(128 * 2, 2)
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def forward(self, x):
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x, _ = x
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out = self.embedding(x) # [batch_size, seq_len, embeding]=[128, 32, 300]
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out, _ = self.lstm(out)
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out = self.fc(out[:, -1, :]) # 句子最后时刻的 hidden state
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return out
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model = Model()
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model.load_state_dict(torch.load('./THUCNews/saved_dict/TextRNN.ckpt', map_location=torch.device("cpu")))
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model.eval()
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stopwords = open('./THUCNews/data/hit_stopwords.txt', encoding='utf8').read().split('\n')[:-1]
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vocab = pickle.load(open('./THUCNews/data/vocab.pkl', 'rb'))
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classes = ['negative', 'positive']
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s = '空调吵，住在电梯旁，电梯门口放垃圾箱，极臭，布草间没关门，也臭，臭到房间里，门下塞毛巾也挡不住臭味，开窗外面吵，关窗空调吵，楼下早餐桌子上摆满垃圾没人整理，不能再差的体验了'
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# s = '东东还算不错。重装系统时，网上查不到怎么修改BIOS，才能安装？问题请帮忙解决！'
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@app.route('/api/content', methods=["POST"])
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def content():
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get_json = request.get_json()
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global model
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global result
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global classes
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result = ""
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s = get_json.get("content")[0]
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print(6777, s)
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try:
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s = list(jieba.lcut(s))
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s = [i for i in s if i not in stopwords]
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s = [vocab.get(i, 10000) for i in s]
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if len(s) > 64:
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s = s[:64]
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else:
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for i in range(64 - len(s)):
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s.append(vocab['<PAD>'])
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outputs = model((torch.LongTensor(s).unsqueeze(0), None))
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print(torch.argmax(outputs))
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result = classes[torch.argmax(outputs)]
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except Exception as e: # 未捕获到异常，程序直接报错
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result = e
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return "pridicting"
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@app.route('/api/model_res', methods=['GET'])
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def model_res():
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global result
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return str(result)
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if __name__ == '__main__':
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app.run(host="127.0.0.1", port=8006)
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