### folder structure and flask setup
> ls
data/ pytorch_chatbot/ save/ templates/ web.py
> ls templates/
template.html
> conda install Flask
> python web.py
* Serving Flask app "web" (lazy loading)
* Environment: production
WARNING: Do not use the development server in a production environment.
Use a production WSGI server instead.
* Debug mode: off
* Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)
<html>
<title>template.html</title>
<body>
<pre>
Test page for pytorch chatbot on seq2seq dataset
<form action='translate' method='post'>
model: <input type='text' name='model' value='{{param["model"]}}' />
epoch: <input type='text' name='epoch' value='{{param["epoch"]}}' />
topn: <input type='text' name='topn' value='{{param["topn"]}}' />
query: <input type='text' name='query' value='{{param["query"]}}'/>
<input type='submit' value='translate' />
</form>
{{param['result']}}
</pre>
</body>
</html>
##########################
# web.py
# > python web.py
#########################
from flask import Flask, request, render_template
import torch
import random
import pytorch_chatbot.main as pcm
import pytorch_chatbot.evaluate as pce
from pytorch_chatbot.train import indexesFromSentence
from pytorch_chatbot.load import loadPrepareData
from pytorch_chatbot.model import nn, EncoderRNN, LuongAttnDecoderRNN
import subprocess
import json
def predictLoad(corpus, modelFile, n_layers=1, hidden_size=512):
print('corpus={}\nmodelFile={}'.format(corpus,modelFile))
torch.set_grad_enabled(False)
voc, pairs = loadPrepareData(corpus)
embedding = nn.Embedding(voc.n_words, hidden_size)
encoder = EncoderRNN(voc.n_words, hidden_size, embedding, n_layers)
attn_model = 'dot'
decoder = LuongAttnDecoderRNN(attn_model, embedding, hidden_size, voc.n_words, n_layers)
checkpoint = torch.load(modelFile)
encoder.load_state_dict(checkpoint['en'])
decoder.load_state_dict(checkpoint['de'])
# train mode set to false, effect only on dropout, batchNorm
encoder.train(False)
decoder.train(False)
#try:
encoder = encoder.to(device)
decoder = decoder.to(device)
#except:
# print('cannot get encoder/decoder')
return encoder, decoder, voc
def predict(encoder, decoder, voc, question, top):
result_list = []
if(top==1):
beam_size = 1
output_words, _ = pce.evaluate(encoder, decoder, voc, question, beam_size)
answer = ' '.join(output_words)
answer = answer.replace('<EOS>','')
result_list.append(answer)
#print(output_words)
else:
beam_size = top
output_words_list = pce.evaluate(encoder, decoder, voc, question, beam_size)
count = 0;
for output_words, score in output_words_list:
count = count + 1
if(count <= top):
output_sentence = ' '.join(output_words)
output_sentence = output_sentence.replace('<EOS>','')
result_list.append(output_sentence)
#print(" {:.3f} < {}".format(score, output_sentence))
return result_list
def filter(voc, question):
words = question.split()
result = []
for w in words:
if(w in voc.word2index):
result.append(w)
return ' '.join(result)
# -------------------------------
def sentence_test(voc,en,de,top,sentence):
source = sentence.rstrip()
seg_source = source
fil_source = filter(voc, seg_source)
target = predict(en, de, voc, fil_source, top)
result = "\nsource: '%s'\nfilter: '%s'\n" % (seg_source,fil_source)
for answer in target:
result = result + "\t'%s'\n" % (answer)
result = result + '\n'
return result
def sentence_test_model(seg_corpus_name, iteration, top, sentence):
n_layers = 1
hidden_size = 512
modelFile = home_path + 'save/model/' + seg_corpus_name + '/1-1_512/' + str(iteration) + '_backup_bidir_model.tar'
en, de, voc = predictLoad(seg_corpus_name, modelFile, n_layers, hidden_size)
return sentence_test(voc,en,de,top,sentence)
def file_test(voc,en,de,top,test_file_name):
with open(test_file_name,"r") as f:
jp_data = f.readlines()
for i,source in enumerate(jp_data):
source = source.rstrip()
seg_source = source
fil_source = filter(voc, seg_source)
target = predict(en, de, voc, fil_source, top)
print("%d:\nsource: '%s'\nfilter: '%s'" % (i+1,seg_source,fil_source))
for answer in target:
print("\t'%s'" % (answer))
def file_test_model(seg_corpus_name, iteration, top, test_file_name):
n_layers = 1
hidden_size = 512
modelFile = home_path + 'save/model/' + seg_corpus_name + '/1-1_512/' + str(iteration) + '_backup_bidir_model.tar'
en, de, voc = predictLoad(seg_corpus_name, modelFile, n_layers, hidden_size)
file_test(voc,en,de,top,test_file_name)
def print_voc(voc):
print('tw+jp voc size=%d' % (len(voc.word2index)))
print(voc.index2word)
def list_models(seg_corpus_name=''):
if seg_corpus_name=='':
modelPath = home_path + 'save/model/'
else:
modelPath = home_path + 'save/model/' + seg_corpus_name + '/1-1_512'
out_bytes = subprocess.check_output(['ls','-l',modelPath],
stderr=subprocess.STDOUT)
out_text = out_bytes.decode('utf-8')
return out_text
def load_source(seg_corpus_name):
path = home_path + 'data/' + seg_corpus_name + '.txt'
with open(path) as inp:
data = inp.readlines()
print(len(data), len(data[0::2]), len(data[1::2]))
data = { 'source': data[0::2], 'target': data[1::2] }
return data
# --------------------------
app = Flask(__name__)
param0 = { 'model': 'translation2019_train_83k',
'epoch': 6000,
'topn' : 10,
'query' : 'what time is it?',
'result' : 'result area'
}
@app.route('/')
def forms():
return render_template('translate.html', param=param0)
@app.route('/translate/<model>/<int:epoch>/<int:topn>', methods=['GET', 'POST'])
def translate_long(model,epoch,topn):
if request.method == 'POST':
query = request.values['query']
elif request.method == 'GET':
query = request.args.get('query')
return translate(model,epoch,topn,query)
@app.route('/translate', methods=['GET', 'POST'])
def translate_short():
if request.method == 'POST':
query = request.values['query']
model = request.values['model']
epoch = request.values['epoch']
topn = request.values['topn']
elif request.method == 'GET':
query = request.args.get('query')
model = request.args.get('model')
epoch = request.args.get('epoch')
topn = request.args.get('topn')
return translate(model,epoch,topn,query)
def translate(model,epoch,topn,query):
epoch = int(epoch)
topn = int(topn)
try:
target = sentence_test_model(model,epoch,topn,query)
except:
target = 'internal error, retry a again'
result = 'query="{}"\nresult="{}"\n'.format(query,target)
param2 = { 'model': model,
'epoch': epoch,
'topn' : topn,
'query' : query,
'result' : result
}
return render_template('translate.html', param=param2)
@app.route('/list/<model>')
def list_model(model):
mlist = list_models(model)
return '<pre>{}</pre>'.format(mlist)
@app.route('/list/')
def list():
mlist = list_models()
return '<pre>{}</pre>'.format(mlist)
#######################################
USE_CUDA = torch.cuda.is_available()
device = torch.device("cuda" if USE_CUDA else "cpu")
home_path = './'
if __name__ == '__main__':
app.run(host='0.0.0.0',port=8080)
註: 本程式使用 GitHub JavaScript code prettifier 工具標示顏色。其方法如下:
1.參考 [Blogger] 如何在 Blogger 顯示程式碼 - Google Code Prettify
於【Blogger 版面配置 HTML/JavaScript小工具】安裝如下套件
<script src="https://cdn.jsdelivr.net/gh/google/code-prettify@master/loader/run_prettify.js"></script>
2.文章編輯再以HTML模式為程式包上如下標籤。
<code class="prettyprint lang-html linenums"> ... </code>
<code class="prettyprint lang-python linenums"> ... </code>
