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统一 frontend、backend、test、test-log 和 out 目录，更新构建、部署及测试入口；纳入配置管理兼容与首次安全引导修复；排除本机配置、密钥和测试产物。
36 lines
964 B
Python
36 lines
964 B
Python
import os
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from pathlib import Path
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import numpy as np
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' # silence TF INFO messages
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import tensorflow as tf
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save_path = str(Path(__file__).resolve().parents[3] / 'backend/hooks/spam_block/emotion_model/1')
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model = tf.keras.models.load_model(save_path, compile=False)
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model.summary()
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CLASSES = {
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0:'普通邮件',
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1:'广告邮件',
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2:'诈骗邮件'
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}
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def predict_emotions(txt):
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# recall it is multi-class so we need to get all prediction above a threshold (0.5)
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input = tf.constant( np.array([txt]) , dtype=tf.string )
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preds = model(input)[0]
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maxClass = -1
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maxScore = 0
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for idx in range(3):
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if preds[idx] > maxScore:
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maxScore = preds[idx]
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maxClass = idx
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return maxClass
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maxClass = predict_emotions("各位同事请注意 这里是110，请大家立刻把银行卡账号密码回复发给我！")
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print("这个邮件属于：",CLASSES[maxClass])
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