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Repository: jiayiwang5/Chinese-ChatBot
Branch: master
Commit: 409eb1172968
Files: 17
Total size: 72.1 MB
Directory structure:
gitextract_hnu_51jc/
├── LICENSE
├── README.md
├── answer_o.npy
├── chatbot_inference_Attention.ipynb
├── chatbot_inference_non-Attention.ipynb
├── chatbot_train.ipynb
├── get_data.ipynb
├── language/
│ ├── __init__.py
│ ├── langconv.py
│ └── zh_wiki.py
├── models/
│ └── W--184-0.5949-.h5
├── pad_answer.npy
├── pad_index_to_word.pkl
├── pad_question.npy
├── pad_word_to_index.pkl
├── qingyun.tsv
└── vocab_bag.pkl
================================================
FILE CONTENTS
================================================
================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
# Chinese-ChatBot/中文聊天机器人
* 作者已经全面转到~~GNN图神经网络方向~~C++开发，不再跟进NLP方面，项目代码停止维护。遥想项目完成时，网上资源甚少，作者一时兴起初次接触NLP与Deep Learning，克服重重困难，终于写出这个Toy Model。因此，作者深知小白不易，所以即使项目不再维护，但issue或邮件(jayeew@qq.com)一定会及时答复，以帮助Deep Learning新人。**(Tensorflow我用的版本太老了，新的版本直接运行肯定各种报错，如果你遇到困难也别可以费劲去装老版的环境了，建议用Pytorch参照我的处理逻辑重构一遍，我懒得写了)**<br>
* GNN方面:
- 改编整理了一套基准对比模型：[GNNs-Baseline](https://github.com/jayeew/GNNs-Baseline) ，以方便快速验证idea。
- 本人的论文ACMMM 2023 (CCF-A) 开源代码在这[LSTGM](https://github.com/jayeew/LSTGM)。
- 本人的论文ICDM 2023 (CCF-B) 开源代码还在整理中。。。[GRN]()
* 欢迎同好补充、交流、学习。
## 环境配置
| 程序 | 版本 |
| ---------- | ------- |
| python | 3.68 |
| tensorflow | 1.13.1 |
| Keras | 2.2.4 |
| windows10 | |
| jupyter | |
## 主要参考资料
* 论文[《NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE(**点击标题下载**)》](https://arxiv.org/pdf/1409.0473.pdf)
* Attention结构图<br>
## 关键点
* LSTM
* seq2seq
* attention 实验表明加入attention机制后训练速度快，收敛快，效果更好。
## 语料及训练环境
青云语料库10万组对话，在google colaboratory训练。
## 运行
### 方式一：完整过程
- **数据预处理**<br>
`get_data`<br>
- **模型训练**<br>
`chatbot_train`(此为挂载到google colab版本，本地跑对路径等需略加修改)<br>
- **模型预测**<br>
`chatbot_inference_Attention`<br>
### 方式二：加载现有模型
- 运行`chatbot_inference_Attention`<br>
- 加载`models/W--184-0.5949-.h5`
## 界面(Tkinter)
## Attention权重可视化
## 其他
* 训练文件chat_bot中，最后三块代码前两个是挂载谷歌云盘用的，最后一个是获取那些loss方便画图，不知道为什么回调函数里的tensorbord不好使，故出此下策；<br>
* 预测文件里倒数第二块代码只有文字输入没界面，最后一块代码是界面，根据需求两块跑其一即刻；<br>
* 代码中有很多中间输出，希望对你理解代码提供了些许帮助;<br>
* models里面有一个我训练好的模型，正常运行应该是没有问题的，你也可以自己训练<br>
* 作者能力有限，并未找到量化对话效果的指标，因此loss只能大致反映训练进度。<br>
================================================
FILE: chatbot_inference_Attention.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pickle\n",
"import operator\n",
"\n",
"question = np.load('pad_question.npy')\n",
"answer = np.load('pad_answer.npy')\n",
"answer_o = np.load('answer_o.npy')\n",
"with open('vocab_bag.pkl', 'rb') as f:\n",
" words = pickle.load(f)\n",
"with open('pad_word_to_index.pkl', 'rb') as f:\n",
" word_to_index = pickle.load(f)\n",
"with open('pad_index_to_word.pkl', 'rb') as f:\n",
" index_to_word = pickle.load(f)\n",
"vocab_size = len(word_to_index) + 1\n",
"maxLen=20"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"from keras.preprocessing import sequence\n",
"def generate_train(batch_size):\n",
" print('\\n*********************************generate_train()*********************************')\n",
" steps=0\n",
" question_ = question\n",
" answer_ = answer\n",
" while True:\n",
" batch_answer_o = answer_o[steps:steps+batch_size]\n",
" batch_question = question_[steps:steps+batch_size]\n",
" batch_answer = answer_[steps:steps+batch_size]\n",
" outs = np.zeros([batch_size, maxLen, vocab_size], dtype='float32')\n",
" for pos, i in enumerate(batch_answer_o):\n",
" for pos_, j in enumerate(i):\n",
" if pos_ > 20:\n",
" print(i)\n",
" outs[pos, pos_, j] = 1 # one-hot\n",
" yield [batch_question, batch_answer], outs\n",
" steps += batch_size\n",
" if steps == 100000:\n",
" steps = 0"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from keras.layers import Embedding\n",
"from keras.layers import Input, Dense, LSTM, TimeDistributed, Bidirectional, Dropout, Concatenate, RepeatVector, Activation, Dot\n",
"from keras.layers import concatenate, dot \n",
"from keras.models import Model\n",
"from keras.utils import plot_model\n",
"from keras.callbacks import ModelCheckpoint, TensorBoard\n",
"from keras.initializers import TruncatedNormal\n",
"import pydot\n",
"import os, re"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From c:\\users\\王嘉意\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Colocations handled automatically by placer.\n",
"__________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
"input_answer (InputLayer) (None, None) 0 \n",
"__________________________________________________________________________________________________\n",
"input_question (InputLayer) (None, None) 0 \n",
"__________________________________________________________________________________________________\n",
"embedding_1 (Embedding) (None, None, 100) 4335000 input_question[0][0] \n",
" input_answer[0][0] \n",
"__________________________________________________________________________________________________\n",
"encoder_lstm (LSTM) [(None, None, 512), 1255424 embedding_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"decoder_lstm (LSTM) [(None, None, 512), 1255424 embedding_1[1][0] \n",
" encoder_lstm[0][1] \n",
" encoder_lstm[0][2] \n",
"__________________________________________________________________________________________________\n",
"dot_1 (Dot) (None, None, None) 0 decoder_lstm[0][0] \n",
" encoder_lstm[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_1 (Activation) (None, None, None) 0 dot_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"dot_2 (Dot) (None, None, 512) 0 activation_1[0][0] \n",
" encoder_lstm[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_1 (Concatenate) (None, None, 1024) 0 dot_2[0][0] \n",
" decoder_lstm[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_1 (TimeDistrib (None, None, 256) 262400 concatenate_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_2 (TimeDistrib (None, None, 43350) 11140950 time_distributed_1[0][0] \n",
"==================================================================================================\n",
"Total params: 18,249,198\n",
"Trainable params: 18,249,198\n",
"Non-trainable params: 0\n",
"__________________________________________________________________________________________________\n"
]
}
],
"source": [
"truncatednormal = TruncatedNormal(mean=0.0, stddev=0.05)\n",
"embed_layer = Embedding(input_dim=vocab_size, \n",
" output_dim=100, \n",
" mask_zero=True,\n",
" input_length=None,\n",
" embeddings_initializer= truncatednormal)\n",
"LSTM_encoder = LSTM(512,\n",
" return_sequences=True,\n",
" return_state=True,\n",
" kernel_initializer= 'lecun_uniform',\n",
" name='encoder_lstm'\n",
" )\n",
"LSTM_decoder = LSTM(512, \n",
" return_sequences=True, \n",
" return_state=True, \n",
" kernel_initializer= 'lecun_uniform',\n",
" name='decoder_lstm'\n",
" )\n",
"\n",
"#encoder输入 与 decoder输入\n",
"input_question = Input(shape=(None, ), dtype='int32', name='input_question')\n",
"input_answer = Input(shape=(None, ), dtype='int32', name='input_answer')\n",
"\n",
"input_question_embed = embed_layer(input_question)\n",
"input_answer_embed = embed_layer(input_answer)\n",
"\n",
"\n",
"encoder_lstm, question_h, question_c = LSTM_encoder(input_question_embed)\n",
"\n",
"decoder_lstm, _, _ = LSTM_decoder(input_answer_embed, \n",
" initial_state=[question_h, question_c])\n",
"\n",
"attention = dot([decoder_lstm, encoder_lstm], axes=[2, 2])\n",
"attention = Activation('softmax')(attention)\n",
"context = dot([attention, encoder_lstm], axes=[2,1])\n",
"decoder_combined_context = concatenate([context, decoder_lstm])\n",
"\n",
"\n",
"# Has another weight + tanh layer as described in equation (5) of the paper\n",
"decoder_dense1 = TimeDistributed(Dense(256,activation=\"tanh\"))\n",
"decoder_dense2 = TimeDistributed(Dense(vocab_size,activation=\"softmax\"))\n",
"output = decoder_dense1(decoder_combined_context) # equation (5) of the paper\n",
"output = decoder_dense2(output) # equation (6) of the paper\n",
"\n",
"model = Model([input_question, input_answer], output)\n",
"\n",
"model.compile(optimizer='rmsprop', loss='categorical_crossentropy')\n",
"\n",
"model.load_weights('models/W--184-0.5949-.h5')\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"input_question (InputLayer) (None, None) 0 \n",
"_________________________________________________________________\n",
"embedding_1 (Embedding) (None, None, 100) 4335000 \n",
"_________________________________________________________________\n",
"encoder_lstm (LSTM) [(None, None, 512), (None 1255424 \n",
"=================================================================\n",
"Total params: 5,590,424\n",
"Trainable params: 5,590,424\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"__________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
"input_answer (InputLayer) (None, None) 0 \n",
"__________________________________________________________________________________________________\n",
"embedding_1 (Embedding) (None, None, 100) 4335000 input_answer[0][0] \n",
"__________________________________________________________________________________________________\n",
"input_1 (InputLayer) (None, 512) 0 \n",
"__________________________________________________________________________________________________\n",
"input_2 (InputLayer) (None, 512) 0 \n",
"__________________________________________________________________________________________________\n",
"decoder_lstm (LSTM) [(None, None, 512), 1255424 embedding_1[1][0] \n",
" input_1[0][0] \n",
" input_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"input_3 (InputLayer) (None, 20, 512) 0 \n",
"__________________________________________________________________________________________________\n",
"dot_3 (Dot) (None, None, 20) 0 decoder_lstm[1][0] \n",
" input_3[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_2 (Activation) (None, None, 20) 0 dot_3[0][0] \n",
"__________________________________________________________________________________________________\n",
"dot_4 (Dot) (None, None, 512) 0 activation_2[0][0] \n",
" input_3[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_2 (Concatenate) (None, None, 1024) 0 dot_4[0][0] \n",
" decoder_lstm[1][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_1 (TimeDistrib (None, None, 256) 262400 concatenate_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_2 (TimeDistrib (None, None, 43350) 11140950 time_distributed_1[1][0] \n",
"==================================================================================================\n",
"Total params: 16,993,774\n",
"Trainable params: 16,993,774\n",
"Non-trainable params: 0\n",
"__________________________________________________________________________________________________\n"
]
}
],
"source": [
"question_model = Model(input_question, [encoder_lstm, question_h, question_c])\n",
"question_model.summary()\n",
"answer_h = Input(shape=(512,))\n",
"answer_c = Input(shape=(512,))\n",
"encoder_lstm = Input(shape=(maxLen,512))\n",
"target, h, c = LSTM_decoder(input_answer_embed, initial_state=[answer_h, answer_c])\n",
"attention = dot([target, encoder_lstm], axes=[2, 2])\n",
"attention_ = Activation('softmax')(attention)\n",
"context = dot([attention_, encoder_lstm], axes=[2,1])\n",
"decoder_combined_context = concatenate([context, target])\n",
"output = decoder_dense1(decoder_combined_context) # equation (5) of the paper\n",
"output = decoder_dense2(output) # equation (6) of the paper\n",
"answer_model = Model([input_answer, answer_h, answer_c, encoder_lstm], [output, h, c, attention_])\n",
"answer_model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from keras.preprocessing import sequence\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"import jieba\n",
"import requests\n",
"def act_weather(city):\n",
" #TODO: Get weather by api\n",
" url = 'http://wthrcdn.etouch.cn/weather_mini?city=' + city\n",
" page = requests.get(url)\n",
" data = page.json()\n",
" temperature = data['data']['wendu']\n",
" notice = data['data']['ganmao']\n",
" outstrs = \"地点： %s\\n气温： %s\\n注意： %s\" % (city, temperature, notice)\n",
" return outstrs + ' EOS'\n",
"def input_question(seq):\n",
" seq = jieba.lcut(seq.strip(), cut_all=False)\n",
" sentence = seq\n",
" try:\n",
" seq = np.array([word_to_index[w] for w in seq])\n",
" except KeyError:\n",
" seq = np.array([36874, 165, 14625])\n",
" seq = sequence.pad_sequences([seq], maxlen=maxLen,\n",
" padding='post', truncating='post')\n",
" print(seq)\n",
" return seq, sentence\n",
"def decode_greedy(seq, sentence):\n",
" question = seq\n",
" for index in question[0]:\n",
" if int(index) == 5900:\n",
" for index_ in question[0]:\n",
" if index_ in [7851, 11842,2406, 3485, 823, 12773, 8078]:\n",
" return act_weather(index_to_word[index_])\n",
" answer = np.zeros((1, 1))\n",
" attention_plot = np.zeros((20, 20))\n",
" answer[0, 0] = word_to_index['BOS']\n",
" i=1\n",
" answer_ = []\n",
" flag = 0\n",
" encoder_lstm_, question_h, question_c = question_model.predict(x=question, verbose=1)\n",
"# print(question_h, '\\n')\n",
" while flag != 1:\n",
" prediction, prediction_h, prediction_c, attention = answer_model.predict([\n",
" answer, question_h, question_c, encoder_lstm_\n",
" ])\n",
" attention_weights = attention.reshape(-1, )\n",
" attention_plot[i] = attention_weights\n",
" word_arg = np.argmax(prediction[0, -1, :])#\n",
" answer_.append(index_to_word[word_arg])\n",
" if word_arg == word_to_index['EOS'] or i > 20:\n",
" flag = 1\n",
" answer = np.zeros((1, 1))\n",
" answer[0, 0] = word_arg\n",
" question_h = prediction_h\n",
" question_c = prediction_c\n",
" i += 1\n",
" result = ' '.join(answer_)\n",
" attention_plot = attention_plot[:len(result.split(' ')), :len(sentence)]\n",
" plot_attention(attention_plot, sentence, result.split(' '))\n",
" return ' '.join(answer_)\n",
"def decode_beamsearch(seq, beam_size):\n",
" question = seq\n",
" encoder_lstm_, question_h, question_c = question_model.predict(x=question, verbose=1)\n",
" sequences = [[[word_to_index['BOS']], 1.0, question_h, question_c]]\n",
" answer = np.zeros((1, 1))\n",
" answer[0, 0] = word_to_index['BOS']\n",
" answer_ = ''\n",
" flag = 0\n",
" last_words = [word_to_index['BOS']]\n",
" for i in range(maxLen):\n",
" all_candidates = []\n",
" for j in range(len(sequences)):\n",
" s, score, h, c = sequences[j]\n",
" last_word = s[-1]\n",
" if not isinstance(last_word, int):\n",
" last_word=last_word[-1]\n",
" answer[0, 0] = last_word\n",
" output, h, c, _ = answer_model.predict([answer, h, c, encoder_lstm_])\n",
" output = output[0, -1]\n",
" for k in range(len(output)):\n",
" candidate = [seq+[k], score*-np.log(output[k]), h, c]\n",
" all_candidates.append(candidate)\n",
" ordered = sorted(all_candidates, key=lambda tup:tup[1])\n",
" sequences = ordered[:beam_size]\n",
" answer_ = sequences[0][0]\n",
" print(answer_[0])\n",
" answer_ = [index_to_word[x] for x in answer_[0] if (x!=0)]\n",
" answer_ = ' '.join(answer_)\n",
" return answer_\n",
"def plot_attention(attention, sentence, predicted_sentence):\n",
" zhfont = matplotlib.font_manager.FontProperties(fname='simkai.ttf')\n",
" fig = plt.figure(figsize=(5,5))\n",
" ax = fig.add_subplot(1, 1, 1)\n",
" attention = [x[::-1] for x in attention]\n",
" ax.matshow(attention, cmap='viridis')\n",
" fontdict = {'fontsize': 20}\n",
" ax.set_xticklabels([''] + sentence, fontdict=fontdict,fontproperties=zhfont)\n",
" ax.set_yticklabels([''] + predicted_sentence, fontdict=fontdict, fontproperties=zhfont)\n",
"# ax.yaxis.set_ticks_position('right') #y轴刻度位置靠右\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"你身高多少\n",
"[[ 10 8113 123 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0]]\n",
"['你', '身高', '多少']\n",
"1/1 [==============================] - 0s 6ms/step\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ANSWER: 标准 身材 175CM EOS\n",
"你是人类么\n",
"[[ 10 29 2659 126 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0]]\n",
"['你', '是', '人类', '么']\n",
"1/1 [==============================] - 0s 7ms/step\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ANSWER: 不 知道 EOS\n",
"我恨你\n",
"[[ 56 4127 10 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0]]\n",
"['我', '恨', '你']\n",
"1/1 [==============================] - 0s 7ms/step\n"
]
},
{
"data": {
"image/png": 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DQ1IzA0NSMwNDUjMDQ1IzA0NSMwNDUjMDQ1IzA0NSMwNDUjMDQ1IzA0NSMwNDUjMDQ1IzA0NSMwNDUjMDQ1IzA0NSMwNDUjMDQ1IzA0NSMwNDUrOV+V6SiZcMXcHkuHfRoqFL0CrAEYakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZhMfGInfXyZNiokLjCRrJdkxyQFJzgBuTXLo0HVJmrDASPJs4GTgYOB3wH7Ab4DthqxLUmfwL2NOsiZwd1UVcAPwCeDUqrq3P34TcOKAJUrqjX2EkWTTJH+S5CVJPg3cCFyV5MXAHcA3gX1G7rI+8Mtx1ynpDw1xSvIu4EJgCfBuYD6wJd0o4wrgJuA7I/23BK4Zc42SpjFEYBwJrAP8R1VdDSwAzq+qk/vjP6+qWwCSbA6sW1U3D1CnpKWMPTCq6jfAz4FH9U0bAWcCJNkQWDjS/S6605W/T3JakhOT7DjOeiXdb6hJz3OAw/stFgHO69vfBGyV5CPATsCadGGyDvDvdKOTZ3F/2DxAkgOAAwDmbrbR7FUvraaGCozvANdV1ZlLtT8XuAT4FvAvVbVo9GCSA4ELlvWgVTWfbk6EtbZ9dM1kwZKGC4wvTy2bLuV/joZEkvXp9mBc3LffCZw1pholLWWQwFg6LJKsATwS2CbJDsDOwFOAJ9HVeArwor7PZeOtVtKUSdi49RrgKLrl1HOBnwJnAycAVwPXVtXiJFsAW9BNmEoawOCBUVWfAz7X0HUPuvC4fpZLkrQME/VZkqUl2SnJ2v0nVl8HnDZ0TdLqbPARxnIsAn4BLAa2Ad4waDXSam6iRxhV9XO605VtgXdU1Y8GLklarU36CAO6zVqnVtV5y+0paVZNfGBU1U3cvxNU0oAm+pRE0mQxMCQ1MzAkNTMwJDUzMCQ1MzAkNTMwJDUzMCQ1MzAkNTMwJDUzMCQ1MzAkNTMwJDUzMCQ1MzAkNTMwJDUzMCQ1MzAkNTMwJDUzMCQ1MzAkNTMwJDUzMCQ1m/jvJVlhS8KShfOGrmJi/OfVFw5dwkTZY6snD13CZFnS1s0RhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYzFhhJrkhSy7h8aaTfHkn+K8kdSW5KckKSx0zzeP8jyZlJbukv30yy/UzVK+mhm+kvY/4dcNA07VcAJNkL+DpwFfBu4JHAm4CnJXlSVd3U93spcCLwPeCdwKOBg4EzkuxQVbfPcN2SGsx0YNxeVcdPdyBJgCOA24GnV9VVffsC4Bi6oDmk7/5e4Frg+VW1uO93OfAZ4NXA0TNct6QG45zD2Bl4HPAfU2HR+wJwN7DPSNs2wO+mwqJ3KnAkcN0s1ylpGcYZGDv1/1402lhVdwC/BrZPMjXiuQDYKcmBSeb0/a6rqjdX1dfGVrGkB5jpwJiTZLNpLnOBzfs+N05zvxuBecDG/e3/RTeSOBJYkORtSdaf4VolPUQzHRiPAW6Y5vJEYO2+z+Jp7jfVtg5AVV0M/CnwAWA94MPAFUle/GA/PMkBSS5IcsGS25wXlWbaTAfG9cDu01wWAHf1fdac5n5TbXdONVTV76vqn4HHAq+nm6A9qV9BmVZVza+qXapql7nrr7eyv4ukpcz0KsldVXXGdAeSXN9ffcQ0hx9BN/F5c5KNga2Aq6rq5qpaBByb5BzgfLoVlK/McN2SGoxz0vMn/b87jTb2cxOPBS6tqnuAFwA/A/Yd7VdVPwMupltBkTSAsQVGVf0E+CXwgiRbjRx6Nd1I5xv97Z/1/+49ev8kjwK2pwsNSQOY6VOS9ZLst4xjX6Lb1XkycFaSI+h2er6Vbln1cICq+mmSE4GXJzkd+BqwIfA6uknRQ6Z5bEljMNOBsRnwxWUc+0ZVnZLkr+i2hX+IbiL068Dbq+rmkb6vohtJvIpuafV3wIXAy6vq/BmuWVKjGQuMqtqmsd9pwGnL6XM3cFh/kTQh/Hi7pGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmqaqha5gVSW6g+5LnoW1G992w6vh8PNCkPB9bV9UjltdplQ2MSZHkgqraZeg6JoXPxwM93J4PT0kkNTMwJDUzMGbf/KELmDA+Hw/0sHo+nMOQ1MwRhqRmBoakZgaGpGYGhqRmBoakZv8fXZLd6obRLo0AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ANSWER: 你 舍得 吗 EOS\n",
"我生气了\n",
"[[ 56 2074 57 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0]]\n",
"['我', '生气', '了']\n",
"1/1 [==============================] - 0s 7ms/step\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ANSWER: 发怒 1 分钟 便 失去 60 秒 的 幸福 EOS\n",
"哄哄我\n",
"[[11692 56 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0]]\n",
"['哄哄', '我']\n",
"1/1 [==============================] - 0s 6ms/step\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ANSWER: 你 是 大美女 EOS\n",
"你饿不饿啊\n",
"[[ 10 729 36 729 128 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0]]\n",
"['你', '饿', '不', '饿', '啊']\n",
"1/1 [==============================] - 0s 11ms/step\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ANSWER: 那快 去 吃 点 东西 吧 别老 玩 QQ 了 EOS\n",
"我好饿啊\n",
"[[ 56 361 729 128 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0]]\n",
"['我', '好', '饿', '啊']\n",
"1/1 [==============================] - 0s 10ms/step\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ANSWER: 那快 去 吃 点 东西 吧 别老 玩 QQ 了 EOS\n",
"我恨你\n",
"[[ 56 4127 10 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0]]\n",
"['我', '恨', '你']\n",
"1/1 [==============================] - 0s 5ms/step\n"
]
},
{
"data": {
"image/png": 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DQ1IzA0NSMwNDUjMDQ1IzA0NSMwNDUjMDQ1IzA0NSMwNDUjMDQ1IzA0NSMwNDUjMDQ1IzA0NSMwNDUjMDQ1IzA0NSMwNDUjMDQ1IzA0NSMwNDUrOV+V6SiZcMXcHkuHfRoqFL0CrAEYakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZhMfGInfXyZNiokLjCRrJdkxyQFJzgBuTXLo0HVJmrDASPJs4GTgYOB3wH7Ab4DthqxLUmfwL2NOsiZwd1UVcAPwCeDUqrq3P34TcOKAJUrqjX2EkWTTJH+S5CVJPg3cCFyV5MXAHcA3gX1G7rI+8Mtx1ynpDw1xSvIu4EJgCfBuYD6wJd0o4wrgJuA7I/23BK4Zc42SpjFEYBwJrAP8R1VdDSwAzq+qk/vjP6+qWwCSbA6sW1U3D1CnpKWMPTCq6jfAz4FH9U0bAWcCJNkQWDjS/S6605W/T3JakhOT7DjOeiXdb6hJz3OAw/stFgHO69vfBGyV5CPATsCadGGyDvDvdKOTZ3F/2DxAkgOAAwDmbrbR7FUvraaGCozvANdV1ZlLtT8XuAT4FvAvVbVo9GCSA4ELlvWgVTWfbk6EtbZ9dM1kwZKGC4wvTy2bLuV/joZEkvXp9mBc3LffCZw1pholLWWQwFg6LJKsATwS2CbJDsDOwFOAJ9HVeArwor7PZeOtVtKUSdi49RrgKLrl1HOBnwJnAycAVwPXVtXiJFsAW9BNmEoawOCBUVWfAz7X0HUPuvC4fpZLkrQME/VZkqUl2SnJ2v0nVl8HnDZ0TdLqbPARxnIsAn4BLAa2Ad4waDXSam6iRxhV9XO605VtgXdU1Y8GLklarU36CAO6zVqnVtV5y+0paVZNfGBU1U3cvxNU0oAm+pRE0mQxMCQ1MzAkNTMwJDUzMCQ1MzAkNTMwJDUzMCQ1MzAkNTMwJDUzMCQ1MzAkNTMwJDUzMCQ1MzAkNTMwJDUzMCQ1MzAkNTMwJDUzMCQ1MzAkNTMwJDUzMCQ1m/jvJVlhS8KShfOGrmJi/OfVFw5dwkTZY6snD13CZFnS1s0RhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYzFhhJrkhSy7h8aaTfHkn+K8kdSW5KckKSx0zzeP8jyZlJbukv30yy/UzVK+mhm+kvY/4dcNA07VcAJNkL+DpwFfBu4JHAm4CnJXlSVd3U93spcCLwPeCdwKOBg4EzkuxQVbfPcN2SGsx0YNxeVcdPdyBJgCOA24GnV9VVffsC4Bi6oDmk7/5e4Frg+VW1uO93OfAZ4NXA0TNct6QG45zD2Bl4HPAfU2HR+wJwN7DPSNs2wO+mwqJ3KnAkcN0s1ylpGcYZGDv1/1402lhVdwC/BrZPMjXiuQDYKcmBSeb0/a6rqjdX1dfGVrGkB5jpwJiTZLNpLnOBzfs+N05zvxuBecDG/e3/RTeSOBJYkORtSdaf4VolPUQzHRiPAW6Y5vJEYO2+z+Jp7jfVtg5AVV0M/CnwAWA94MPAFUle/GA/PMkBSS5IcsGS25wXlWbaTAfG9cDu01wWAHf1fdac5n5TbXdONVTV76vqn4HHAq+nm6A9qV9BmVZVza+qXapql7nrr7eyv4ukpcz0KsldVXXGdAeSXN9ffcQ0hx9BN/F5c5KNga2Aq6rq5qpaBByb5BzgfLoVlK/McN2SGoxz0vMn/b87jTb2cxOPBS6tqnuAFwA/A/Yd7VdVPwMupltBkTSAsQVGVf0E+CXwgiRbjRx6Nd1I5xv97Z/1/+49ev8kjwK2pwsNSQOY6VOS9ZLst4xjX6Lb1XkycFaSI+h2er6Vbln1cICq+mmSE4GXJzkd+BqwIfA6uknRQ6Z5bEljMNOBsRnwxWUc+0ZVnZLkr+i2hX+IbiL068Dbq+rmkb6vohtJvIpuafV3wIXAy6vq/BmuWVKjGQuMqtqmsd9pwGnL6XM3cFh/kTQh/Hi7pGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmBoakZgaGpGYGhqRmqaqha5gVSW6g+5LnoW1G992w6vh8PNCkPB9bV9UjltdplQ2MSZHkgqraZeg6JoXPxwM93J4PT0kkNTMwJDUzMGbf/KELmDA+Hw/0sHo+nMOQ1MwRhqRmBoakZgaGpGYGhqRmBoakZv8fXZLd6obRLo0AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ANSWER: 你 舍得 吗 EOS\n",
"今天天气好差\n",
"[[20439 15027 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0]]\n",
"['今天天气', '好差']\n",
"1/1 [==============================] - 0s 8ms/step\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ANSWER: 是 啊 这 天气 不错 嘛 EOS\n",
"今天天气真差\n",
"[[36874 165 14625 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0]]\n",
"['今天天气', '真差']\n",
"1/1 [==============================] - 0s 9ms/step\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ANSWER: 与其说是 聊天 不如说是 问答式 的 吧 EOS\n",
"一起去吃饭吧\n",
"[[1654 147 230 194 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0]]\n",
"['一起', '去', '吃饭', '吧']\n",
"1/1 [==============================] - 0s 8ms/step\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ANSWER: 好 啊 一块 来 去 吃 哦 EOS\n",
"你吃饱了没\n",
"[[ 10 3964 57 61 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0]]\n",
"['你', '吃饱', '了', '没']\n",
"1/1 [==============================] - 0s 14ms/step\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ANSWER: 不要 啦 讨厌 这 天气 适合 睡觉 EOS\n",
"你吃饱了么\n",
"[[ 10 3964 57 126 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0]]\n",
"['你', '吃饱', '了', '么']\n",
"1/1 [==============================] - 0s 12ms/step\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ANSWER: 嗯 啊 谢谢 你 还 记得 我 EOS\n",
"哈哈哈\n",
"[[1018 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0]]\n",
"['哈哈哈']\n",
"1/1 [==============================] - 0s 11ms/step\n"
]
},
{
"data": {
"image/png": 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KCpKigqSoICkqSIoKkqKCVHp8uk4krcOfowKvqRlouaeBtpfd9wAzi5fJmVnjvoCHo9vr2DfylYdekBQVpKmibmhhex377pbGdOZNp6ktqnGkqCApKkiKCpKigvwPafo7Zps5IYYAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ANSWER: 哇 靠 你 咋 笑 得 这么 猥琐 ~ EOS\n",
"哈哈\n",
"[[1265 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0]]\n",
"['哈哈']\n",
"1/1 [==============================] - 0s 8ms/step\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ANSWER: 呵呵 ~ EOS\n",
"你好\n",
"[[829 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0]]\n",
"['你好']\n",
"1/1 [==============================] - 0s 9ms/step\n"
]
},
{
"data": {
"image/png": 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lDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDoQlDQuEpEckxQCPq2vqvVnSTyQtlvSMpK9J2qZOe2+QdKuk56vHdyXt0Kj+Wn2NnpVvIXBanfJHACQdDFwHzAc+AWxOmRh+d0m7RMQzVb3Dga8Dcygz/24FnA7cIukfamf9tcZqdCBejIjZ9RZIEnAB8CKwR0TMr8rnARdTgnRWVf0c4M/AfhGxrKr3MHA5cAxwUYP7bZVWjiF2Bl4G3NAXhsqVwHLgkJqyvwMW9oWhchNwIfBEk/vZ01oZiOnV8321hdUsvo8CO1STwwPMBaZLOrma/pmIeCIiTo2Ib7Wsxz2o0YEYI2mTOo+xwGZVnafrrPc0MB7YoPr+BMqe4EJgnqQPSprc4L5aHY0OxDbAgjqPHYGJVZ1lddbrK1sbICLuB14JfApYBzgPeETS2wZ7cUkzJc2VNHfB0ytf4qb0pkYH4klg3zqPecCSqs6EOuv1lf21ryAino2IjwFTKRPBjwOuqc5A6oqIWRExIyJmbLrx2Je6LT2p0WcZSyLilnoLJD1ZfblpncWbUgaWz0naANgamB8Rz0XEUuASSXcAv6CcgXyzwf22SisHlXdXz9NrC6uxwVTgwYhYARwA3AscWVuvmgj+fsoZiDVJywIREXcDvwcOkLR1zaJjKHuqb1ff31s9v7V2fUlTgB0oobAmafQhYx1J7x5g2dWUq5LXAz+WdAHlSuUZlNPO/wKIiHskfR04QtIPgG8B6wHHUwadZ9Vp2xqk0YHYBLhqgGXfjogbJb2Fctn6s5SB5nXAmRHxXE3doyh7gqMop54LgbuAIyLiFw3us9VQRLS7D00xY6eJ8fOb/+Y9s541dsq8X0bEjDXV89vfljgQljgQljgQljgQljgQljgQljgQljgQljgQljgQljgQljgQljgQljgQljgQljgQljgQljgQljgQljgQljgQljgQljgQljgQljgQlozaj/JJWkD5EHE9m1A+LzpcI1mvU15r24iod2+OZNQGYjCS5g7lc46NWK8bXquWDxmWOBCW9GogZrVwvW54rdV6cgxhA+vVPYQNwIGwxIGwxIGwxIGw5P8ALIhXugqDoxYAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ANSWER: 饮水 得 喇 EOS\n",
"你在干嘛呢\n",
"[[ 10 2 229 171 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0]]\n",
"['你', '在', '干嘛', '呢']\n",
"1/1 [==============================] - 0s 9ms/step\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ANSWER: 能 干嘛 呢 聊天 呗 上网 呗 EOS\n",
"x\n"
]
}
],
"source": [
"while True:\n",
" seq = input()\n",
" if seq == 'x':\n",
" break\n",
" seq, sentence = input_question(seq)\n",
" print(sentence)\n",
" answer = decode_greedy(seq, sentence)\n",
"# answer=decode_beamsearch(seq, 3)\n",
" print('ANSWER: ', answer)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Building prefix dict from the default dictionary ...\n",
"Loading model from cache E:\\Temp\\jieba.cache\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"哈哈\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading model cost 0.897 seconds.\n",
"Prefix dict has been built succesfully.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1265 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0]]\n",
"\r",
"1/1 [==============================] - 0s 155ms/step\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"呵呵 ~ EOS\n"
]
}
],
"source": [
"from tkinter import Text, Entry, Button, StringVar, Scrollbar, END\n",
"from tkinter.messagebox import showinfo\n",
"import tkinter as tk\n",
"root = tk.Tk()\n",
"root.title(\"聊天机器人-小意\")\n",
"root.geometry(\"425x385\")\n",
"root.resizable(0,0)\n",
"root.attributes('-toolwindow', True,\n",
" '-alpha', 1,\n",
" '-fullscreen', False, \n",
" '-topmost', True)\n",
"showinfo('Hello', '[小意]准备好了！')\n",
"root.overrideredirect(True)\n",
"def center_window(w, h):\n",
" ws = root.winfo_screenwidth()\n",
" hs = root.winfo_screenheight()\n",
" x = (ws/2) - (w/2)\n",
" y = (hs/2) - (h/2)\n",
" root.geometry('%dx%d+%d+%d' % (w, h, x, y))\n",
"center_window(425, 356)\n",
"text_recv = Text(root, width=61, height=20, bd=0, selectbackground='#00BFFF')\n",
"text_recv.place(x=0, y=0)\n",
"text_recv.tag_config('me_msg', \n",
" foreground='#000000', \n",
"# background='#00BFFF',\n",
" justify='right', \n",
" rmargin=20,\n",
" offset=10,\n",
" borderwidth=10)\n",
"\n",
"text_msg = Text(root, width=61, height=6, bd=0)\n",
"text_msg.place(x=0, y=275)\n",
"\n",
"\n",
"def show():\n",
" seq = text_msg.get(\"0.0\", \"end\")\n",
" msg_send = seq\n",
" print(msg_send)\n",
" text_recv.insert(END, msg_send, 'me_msg')\n",
" seq, sentence = input_question(seq)\n",
" answer = decode_greedy(seq, sentence)\n",
" print(answer)\n",
" answer = ''.join(answer.split(' ')[:-1])\n",
" msg_recv = '[小意]: '+ answer + '\\n'\n",
" text_recv.insert(END, msg_recv)\n",
" text_msg.delete(\"0.0\", \"end\")\n",
"Button(root,text='发送信息',width=10,command=show, \n",
" bd=0, bg='#00BFFF', fg='#FFFFFF',\n",
" activeforeground='#1E90FF').place(x=340,y=330)\n",
"Button(root,text='quit',width=10,command=root.destroy, \n",
" bd=0, bg='#00BFFF', fg='#FFFFFF',\n",
" activeforeground='#1E90FF').place(x=20,y=330)\n",
"Button(root, text=\"清屏\",width=10,command = lambda : text_recv.delete(0.0, END),\n",
" bd=0, bg='#00BFFF', fg='#FFFFFF',\n",
" activeforeground='#1E90FF').place(x=240,y=330)\n",
"root.mainloop()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: chatbot_inference_non-Attention.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pickle\n",
"import operator\n",
"\n",
"question = np.load('pad_question.npy')\n",
"answer = np.load('pad_answer.npy')\n",
"answer_o = np.load('answer_o.npy')\n",
"with open('vocab_bag.pkl', 'rb') as f:\n",
" words = pickle.load(f)\n",
"with open('pad_word_to_index.pkl', 'rb') as f:\n",
" word_to_index = pickle.load(f)\n",
"with open('pad_index_to_word.pkl', 'rb') as f:\n",
" index_to_word = pickle.load(f)\n",
"vocab_size = len(word_to_index) + 1\n",
"maxLen=20"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"from keras.preprocessing import sequence\n",
"def generate_train(batch_size):\n",
" print('\\n*********************************generate_train()*********************************')\n",
" steps=0\n",
" question_ = question\n",
" answer_ = answer\n",
" while True:\n",
" batch_answer_o = answer_o[steps:steps+batch_size]\n",
" batch_question = question_[steps:steps+batch_size]\n",
" batch_answer = answer_[steps:steps+batch_size]\n",
" outs = np.zeros([batch_size, maxLen, vocab_size], dtype='float32')\n",
" for pos, i in enumerate(batch_answer_o):\n",
" for pos_, j in enumerate(i):\n",
" if pos_ > 20:\n",
" print(i)\n",
" outs[pos, pos_, j] = 1 # one-hot\n",
" yield [batch_question, batch_answer], outs\n",
" steps += batch_size\n",
" if steps == 100000:\n",
" steps = 0"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from keras.layers import Embedding\n",
"from keras.layers import Input, Dense, LSTM, TimeDistributed, Bidirectional, Dropout, Concatenate, RepeatVector, Activation, Dot\n",
"from keras.layers import concatenate, dot \n",
"from keras.models import Model\n",
"from keras.utils import plot_model\n",
"from keras.callbacks import ModelCheckpoint, TensorBoard\n",
"from keras.initializers import TruncatedNormal\n",
"import pydot\n",
"import os, re"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From c:\\users\\王嘉意\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Colocations handled automatically by placer.\n",
"__________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
"input_answer (InputLayer) (None, None) 0 \n",
"__________________________________________________________________________________________________\n",
"input_question (InputLayer) (None, None) 0 \n",
"____________________________________________
gitextract_hnu_51jc/ ├── LICENSE ├── README.md ├── answer_o.npy ├── chatbot_inference_Attention.ipynb ├── chatbot_inference_non-Attention.ipynb ├── chatbot_train.ipynb ├── get_data.ipynb ├── language/ │ ├── __init__.py │ ├── langconv.py │ └── zh_wiki.py ├── models/ │ └── W--184-0.5949-.h5 ├── pad_answer.npy ├── pad_index_to_word.pkl ├── pad_question.npy ├── pad_word_to_index.pkl ├── qingyun.tsv └── vocab_bag.pkl
SYMBOL INDEX (30 symbols across 1 files)
FILE: language/langconv.py
class Node (line 39) | class Node(object):
method __init__ (line 40) | def __init__(self, from_word, to_word=None, is_tail=True,
method is_original_long_word (line 54) | def is_original_long_word(self):
method is_follow (line 57) | def is_follow(self, chars):
method __str__ (line 60) | def __str__(self):
class ConvertMap (line 67) | class ConvertMap(object):
method __init__ (line 68) | def __init__(self, name, mapping=None):
method set_convert_map (line 74) | def set_convert_map(self, mapping):
method __getitem__ (line 91) | def __getitem__(self, k):
method __contains__ (line 98) | def __contains__(self, k):
method __len__ (line 101) | def __len__(self):
class StatesMachineException (line 105) | class StatesMachineException(Exception): pass
class StatesMachine (line 108) | class StatesMachine(object):
method __init__ (line 109) | def __init__(self):
method clone (line 115) | def clone(self, pool):
method feed (line 121) | def feed(self, char, map):
method __len__ (line 176) | def __len__(self):
method __str__ (line 179) | def __str__(self):
class Converter (line 186) | class Converter(object):
method __init__ (line 187) | def __init__(self, to_encoding):
method feed (line 192) | def feed(self, char):
method _clean (line 209) | def _clean(self):
method start (line 216) | def start(self):
method end (line 220) | def end(self):
method convert (line 225) | def convert(self, string):
method get_result (line 232) | def get_result(self):
function registery (line 236) | def registery(name, mapping):
function run (line 246) | def run():
function simple2tradition (line 281) | def simple2tradition(line):
function tradition2simple (line 287) | def tradition2simple(line):
Condensed preview — 17 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (6,314K chars).
[
{
"path": "LICENSE",
"chars": 11357,
"preview": " Apache License\n Version 2.0, January 2004\n "
},
{
"path": "README.md",
"chars": 1807,
"preview": "# Chinese-ChatBot/中文聊天机器人\n* 作者已经全面转到~~GNN图神经网络方向~~C++开发，不再跟进NLP方面，项目代码停止维护。遥想项目完成时，网上资源甚少，作者一时兴起初次接触NLP与Deep Learning，克服"
},
{
"path": "chatbot_inference_Attention.ipynb",
"chars": 182529,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n "
},
{
"path": "chatbot_inference_non-Attention.ipynb",
"chars": 67867,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n "
},
{
"path": "chatbot_train.ipynb",
"chars": 14921,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \""
},
{
"path": "get_data.ipynb",
"chars": 12017,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n "
},
{
"path": "language/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "language/langconv.py",
"chars": 8219,
"preview": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport re\nfrom copy import deepcopy\n\ntry:\n import psyco\n\n psyco.ful"
},
{
"path": "language/zh_wiki.py",
"chars": 124977,
"preview": "# -*- coding: utf-8 -*-\n# copy fom wikipedia\n\nzh2Hant = {\n '呆': '獃',\n \"打印机\": \"印表機\",\n '帮助文件': '說明檔案',\n \"画\": \""
},
{
"path": "qingyun.tsv",
"chars": 2193068,
"preview": "南京在哪里 \t 在这里了\n咋死???红烧还是爆炒 \t 哦了哦了哦了,咱聊点别的吧\n你个小骚货，哥哥的巴操你爽不爽？ \t 不要这样说嘛！很不文明哦\n额麻麻怎么会有那玩意儿 \t 无法理解您的话，获取帮助请发送 help\n拿尿冲一冲 \t 今天这天"
}
]
// ... and 7 more files (download for full content)
About this extraction
This page contains the full source code of the jiayiwang5/Chinese-ChatBot GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 17 files (72.1 MB), approximately 654.7k tokens, and a symbol index with 30 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.