-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathapplication.py
188 lines (163 loc) · 8.82 KB
/
application.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
import logging
import logging.handlers
from wsgiref.simple_server import make_server
from urlparse import parse_qs
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import LSTM
import numpy
# # Create logger
# logger = logging.getLogger(__name__)
# logger.setLevel(logging.INFO)
#
# # Handler
# LOG_FILE = '/opt/python/log/sample-app.log'
# handler = logging.handlers.RotatingFileHandler(LOG_FILE, maxBytes=1048576, backupCount=5)
# handler.setLevel(logging.INFO)
#
# # Formatter
# formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
#
# # Add Formatter to Handler
# handler.setFormatter(formatter)
#
# # add Handler to Logger
# logger.addHandler(handler)
SEQUENCE_LENGTH = 100
FILE = 'index.html'
BAD_TEXT = '/:;][{}-=+)(*&^%-_$#@!~1234567890'
WEIGHTS_DIRECTORY = "scripts/model_weights/"
author_vars = {
'tolkien': {
'y_shape': 42,
'weights_name': WEIGHTS_DIRECTORY + 'tolkien-0.9459.hdf5',
'char_to_int': {'\n': 0, '!': 2, ' ': 1, '"': 3, "'": 4, '\xa9': 39, '-': 6, ',': 5, '.': 7, '\xb3': 40, ';': 9, ':': 8, '?': 10, '\xc3': 41, '_': 11, 'a': 13, '`': 12, 'c': 15, 'b': 14, 'e': 17, 'd': 16, 'g': 19, 'f': 18, 'i': 21, 'h': 20, 'k': 23, 'j': 22, 'm': 25, 'l': 24, 'o': 27, 'n': 26, 'q': 29, 'p': 28, 's': 31, 'r': 30, 'u': 33, 't': 32, 'w': 35, 'v': 34, 'y': 37, 'x': 36, 'z': 38},
'int_to_char': {0: '\n', 1: ' ', 2: '!', 3: '"', 4: "'", 5: ',', 6: '-', 7: '.', 8: ':', 9: ';', 10: '?', 11: '_', 12: '`', 13: 'a', 14: 'b', 15: 'c', 16: 'd', 17: 'e', 18: 'f', 19: 'g', 20: 'h', 21: 'i', 22: 'j', 23: 'k', 24: 'l', 25: 'm', 26: 'n', 27: 'o', 28: 'p', 29: 'q', 30: 'r', 31: 's', 32: 't', 33: 'u', 34: 'v', 35: 'w', 36: 'x', 37: 'y', 38: 'z', 39: '\xa9', 40: '\xb3', 41: '\xc3'}
},
'dante': {
'y_shape': 33,
'weights_name': WEIGHTS_DIRECTORY + 'dante-1.4319.hdf5',
'char_to_int': {'\n': 0, ' ': 1, '"': 2, "'": 3, ',': 4, '.': 5, '?': 6, 'a': 7, 'c': 9, 'b': 8, 'e': 11, 'd': 10, 'g': 13, 'f': 12, 'i': 15, 'h': 14, 'k': 17, 'j': 16, 'm': 19, 'l': 18, 'o': 21, 'n': 20, 'q': 23, 'p': 22, 's': 25, 'r': 24, 'u': 27, 't': 26, 'w': 29, 'v': 28, 'y': 31, 'x': 30, 'z': 32},
'int_to_char': {0: '\n', 1: ' ', 2: '"', 3: "'", 4: ',', 5: '.', 6: '?', 7: 'a', 8: 'b', 9: 'c', 10: 'd', 11: 'e', 12: 'f', 13: 'g', 14: 'h', 15: 'i', 16: 'j', 17: 'k', 18: 'l', 19: 'm', 20: 'n', 21: 'o', 22: 'p', 23: 'q', 24: 'r', 25: 's', 26: 't', 27: 'u', 28: 'v', 29: 'w', 30: 'x', 31: 'y', 32: 'z'}
},
'shakespeare': {
'y_shape': 32,
'weights_name': WEIGHTS_DIRECTORY + 'shakespeare-1.3267.hdf5',
'char_to_int': {'\n': 0, ' ': 1, "'": 2, ',': 3, '.': 4, '?': 5, 'a': 6, 'c': 8, 'b': 7, 'e': 10, 'd': 9, 'g': 12, 'f': 11, 'i': 14, 'h': 13, 'k': 16, 'j': 15, 'm': 18, 'l': 17, 'o': 20, 'n': 19, 'q': 22, 'p': 21, 's': 24, 'r': 23, 'u': 26, 't': 25, 'w': 28, 'v': 27, 'y': 30, 'x': 29, 'z': 31},
'int_to_char': {0: '\n', 1: ' ', 2: "'", 3: ',', 4: '.', 5: '?', 6: 'a', 7: 'b', 8: 'c', 9: 'd', 10: 'e', 11: 'f', 12: 'g', 13: 'h', 14: 'i', 15: 'j', 16: 'k', 17: 'l', 18: 'm', 19: 'n', 20: 'o', 21: 'p', 22: 'q', 23: 'r', 24: 's', 25: 't', 26: 'u', 27: 'v', 28: 'w', 29: 'x', 30: 'y', 31: 'z'}
},
'rowling': {
'y_shape': 35,
'weights_name': WEIGHTS_DIRECTORY + 'rowling-1.2660.hdf5',
'char_to_int': {'\t': 0, '\n': 1, ' ': 2, '"': 3, "'": 4, ',': 5, '.': 6, '?': 7, '\\': 8, 'a': 9, 'c': 11, 'b': 10, 'e': 13, 'd': 12, 'g': 15, 'f': 14, 'i': 17, 'h': 16, 'k': 19, 'j': 18, 'm': 21, 'l': 20, 'o': 23, 'n': 22, 'q': 25, 'p': 24, 's': 27, 'r': 26, 'u': 29, 't': 28, 'w': 31, 'v': 30, 'y': 33, 'x': 32, 'z': 34},
'int_to_char': {0: '\t', 1: '\n', 2: ' ', 3: '"', 4: "'", 5: ',', 6: '.', 7: '?', 8: '\\', 9: 'a', 10: 'b', 11: 'c', 12: 'd', 13: 'e', 14: 'f', 15: 'g', 16: 'h', 17: 'i', 18: 'j', 19: 'k', 20: 'l', 21: 'm', 22: 'n', 23: 'o', 24: 'p', 25: 'q', 26: 'r', 27: 's', 28: 't', 29: 'u', 30: 'v', 31: 'w', 32: 'x', 33: 'y', 34: 'z'}
},
'poe': {
'y_shape': 34,
'weights_name': WEIGHTS_DIRECTORY + 'poe-1.0889.hdf5',
'char_to_int': {'\n': 0, ' ': 1, '"': 2, "'": 3, ',': 4, '.': 5, '?': 6, 'a': 8, '`': 7, 'c': 10, 'b': 9, 'e': 12, 'd': 11, 'g': 14, 'f': 13, 'i': 16, 'h': 15, 'k': 18, 'j': 17, 'm': 20, 'l': 19, 'o': 22, 'n': 21, 'q': 24, 'p': 23, 's': 26, 'r': 25, 'u': 28, 't': 27, 'w': 30, 'v': 29, 'y': 32, 'x': 31, 'z': 33},
'int_to_char': {0: '\n', 1: ' ', 2: '"', 3: "'", 4: ',', 5: '.', 6: '?', 7: '`', 8: 'a', 9: 'b', 10: 'c', 11: 'd', 12: 'e', 13: 'f', 14: 'g', 15: 'h', 16: 'i', 17: 'j', 18: 'k', 19: 'l', 20: 'm', 21: 'n', 22: 'o', 23: 'p', 24: 'q', 25: 'r', 26: 's', 27: 't', 28: 'u', 29: 'v', 30: 'w', 31: 'x', 32: 'y', 33: 'z'}
}
}
def application(environ, start_response):
path = environ['PATH_INFO'].lstrip('/') # get path
method = environ['REQUEST_METHOD']
if "js" in path: # handle query for
status = '200 OK' # files in /static
headers = [('Content-type', 'text/javascript')]
start_response(status, headers)
f2serv = file(path, 'r') # read file
return environ['wsgi.file_wrapper'](f2serv) # return file
if "css" in path: # handle query for
status = '200 OK' # files in /static
headers = [('Content-type', 'text/css')]
start_response(status, headers)
f2serv = file(path, 'r') # read file
return environ['wsgi.file_wrapper'](f2serv) # return file
if "svg" in path:
status = '200 OK' # files in /static
headers = [('Content-type', 'image/svg+xml')]
start_response(status, headers)
f2serv = file(path, 'r') # read file
return environ['wsgi.file_wrapper'](f2serv) # return file
if "img" in path:
status = '200 OK' # files in /static
headers = [('Content-type', 'image/jpg')]
start_response(status, headers)
f2serv = file(path, 'r') # read file
return environ['wsgi.file_wrapper'](f2serv) # return file
if method == 'POST':
try:
request_body_size = int(environ['CONTENT_LENGTH'])
request_body = environ['wsgi.input'].read(request_body_size)
except (TypeError, ValueError):
request_body = "0"
parsed_body = parse_qs(request_body)
text = parsed_body.get('text', [''])[0]
author = parsed_body.get('author', [''])[0]
output_length = int(parsed_body.get('length', [''])[0])
if text.strip() != "":
response_body = translate(author, text, output_length)
else:
response_body = "No input text."
status = '200 OK'
headers = [('Content-type', 'text/html')]
start_response(status, headers)
else:
response_body = open(FILE).read() # the html file itself
status = '200 OK'
headers = [('Content-type', 'text/html'),
('Content-Length', str(len(response_body)))]
start_response(status, headers)
return [response_body]
def translate(author, text, output_length):
vars = author_vars.get(author)
char_to_int = vars.get("char_to_int")
int_to_char = vars.get("int_to_char")
text = text.lower()
text = text.translate(None, BAD_TEXT)
if len(text) < 100:
num_spaces = 100 - len(text)
for i in range(num_spaces):
text = " " + text
else:
text = text[len(text) - 100:len(text)]
pattern = [char_to_int[letter] for letter in text]
# define the LSTM model
model = Sequential()
model.add(LSTM(256, input_shape=(100, 1), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(256))
model.add(Dropout(0.2))
model.add(Dense(vars.get("y_shape"), activation='softmax'))
model.load_weights(vars.get("weights_name"))
model.compile(loss='categorical_crossentropy', optimizer='adam')
return generate_chars(model, pattern, int_to_char, output_length)
def generate_ints(text, chars):
dataX = []
dataY = []
for i in range(len(text) - SEQUENCE_LENGTH):
seq_in = text[i:i + SEQUENCE_LENGTH]
seq_out = text[i + SEQUENCE_LENGTH]
dataX.append([chars[char] for char in seq_in])
dataY.append(chars[seq_out])
return dataX, dataY
def generate_chars(model, pattern, int_to_char, length):
output = ""
for _ in range(length):
x = numpy.reshape(pattern, (1, len(pattern), 1))
n_vocab = len(int_to_char)
x = x / float(n_vocab)
prediction = model.predict(x, verbose=0)
index = numpy.argmax(prediction)
result = int_to_char[index]
# seq_in = [int_to_char[value] for value in pattern]
output += result
pattern.append(index)
pattern = pattern[1:len(pattern)]
return output
if __name__ == '__main__':
httpd = make_server('', 8000, application)
print("Serving on port 8000...")
httpd.serve_forever()