-
Notifications
You must be signed in to change notification settings - Fork 0
/
lambda.py
64 lines (49 loc) · 1.53 KB
/
lambda.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
import ctypes
import os
from six.moves import urllib
import zipfile
import stat
import logging
import json
logging.basicConfig(level=logging.DEBUG)
print "logging"
def response(status_code, response_body):
return {
'statusCode': status_code,
'body': json.dumps(response_body) if response_body else json.dumps({}),
'headers': {
'Content-Type': 'application/json',
},
}
def vectorize_sequences(sequences, dimension):
results = np.zeros((len(sequences), dimension))
for i, sequence in enumerate(sequences):
results[i, sequence] = 1.
return results
for d, _, files in os.walk('lib'):
for f in files:
if f.endswith('.a') or f.endswith('.settings'):
continue
print('loading %s...' % f)
ctypes.cdll.LoadLibrary(os.path.join(d, f))
import keras
from keras.preprocessing.text import one_hot
from numpy import array
import numpy as np
model = keras.models.load_model(os.environ['MODEL_NAME'])
def handler(event, context):
print event
#params = event['queryStringParameters']
sms = event['body']
print sms
sentence = one_hot(str(sms), 87413)
print sentence
sentences = [sentence]
vector = vectorize_sequences(sentences,87413)
result = model.predict(vector)
print "Verdict: ", result
print "Shape: ", result.shape
print "Type: ", type(result)
result = float(np.array2string(result)[2:-2])
print(result)
return response(200, result)