-
Notifications
You must be signed in to change notification settings - Fork 0
/
svm_server.py
133 lines (122 loc) · 7.5 KB
/
svm_server.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
# :'######::'##::::'##:'##::::'##:::::::::::'######::'########:'########::'##::::'##:'########:'########::
# '##... ##: ##:::: ##: ###::'###::::::::::'##... ##: ##.....:: ##.... ##: ##:::: ##: ##.....:: ##.... ##:
# ##:::..:: ##:::: ##: ####'####:::::::::: ##:::..:: ##::::::: ##:::: ##: ##:::: ##: ##::::::: ##:::: ##:
# . ######:: ##:::: ##: ## ### ##::::::::::. ######:: ######::: ########:: ##:::: ##: ######::: ########::
# :..... ##:. ##:: ##:: ##. #: ##:::::::::::..... ##: ##...:::: ##.. ##:::. ##:: ##:: ##...:::: ##.. ##:::
# '##::: ##::. ## ##::: ##:.:: ##::::::::::'##::: ##: ##::::::: ##::. ##:::. ## ##::: ##::::::: ##::. ##::
# . ######::::. ###:::: ##:::: ##:'#######:. ######:: ########: ##:::. ##:::. ###:::: ########: ##:::. ##:
# :......::::::...:::::..:::::..::.......:::......:::........::..:::::..:::::...:::::........::..:::::..::
#
# https://github.com/OdysseyMomentum/Cryptomice-EonML
import time
import json
import os
import os.path
import subprocess
from flask import Flask, jsonify, request
from flask_socketio import SocketIO
from flask_cors import CORS
from markupsafe import escape
import svm_lib
import svm_utils
import svm_checks
configurations = {'NAME': 'SVM_SERVER', 'VERSION': '0.0.2-odyssey'}
app = Flask(__name__)
CORS(app)
socketio = SocketIO(app)
@app.route('/')
def home():
df = subprocess.check_output('df -h /', shell=True)
commands = []
commands.append({'path': '/', 'type': 'GET', 'description': 'Information about SVM_SERVER commands.' })
commands.append({'path': '/model', 'type': 'GET', 'description': 'Get a list of known models.' })
commands.append({'path': '/model/<modelname>', 'type': 'GET', 'description': 'Get informations connected to model <modelname>.' })
commands.append({'path': '/model/<modelname>/train', 'type': 'POST', 'description': 'Create a new model called <modelname>.' })
commands.append({'path': '/model/<modelname>/predict', 'type': 'GET/POST', 'description': 'Create a prediction using model <modelname> and passed dataset.' })
return jsonify({'name':configurations['NAME'], 'version':configurations['VERSION'], 'commands':commands})
@app.route('/model')
def models():
results = svm_lib.listElement(f'ls -l {configurations["svmModels"]}/*.model', configurations["svmModels"])
json_result = {'results': results}
return jsonify(json_result)
@app.route('/model/<mymodel>')
def model(mymodel):
model = escape(mymodel)
results = {}
results['models'] = svm_lib.listFile(f'ls -l {configurations["svmModels"]}/{model}.model')
results['trainings'] = svm_lib.listFile(f'ls -l {configurations["svmTrainings"]}/{model}.training')
results['datasets'] = svm_lib.listFile(f'ls -l {configurations["svmPredictions"]}/{model}*.data')
results['predictions'] = svm_lib.listFile(f'ls -l {configurations["svmPredictions"]}/{model}*.predictions')
return jsonify({'results': results})
def performTrain(received_dataset, model):
svm_lib.writeSVM(f'{configurations["svmTrainings"]}/{model}.training', received_dataset)
status = subprocess.check_output(f'{configurations["svmPath"]}/svm_learn -z r {configurations["svmTrainings"]}/{model}.training {configurations["svmModels"]}/{model}.model', shell=True).decode().splitlines()
results = svm_lib.readSVM(f'{configurations["svmModels"]}/{model}.model')
return status, results
@app.route('/model/<mymodel>/train', methods=['GET', 'POST'])
def train(mymodel):
if request.method == 'POST':
model = escape(mymodel)
timestr = time.strftime("%Y%m%d-%H%M%S")+'-'+str(time.time())
received_dataset = f'# dataset - {timestr}'
payload = request.get_json()
result, message = svm_checks.checkTrainingAll({'payload':payload, 'model':model}, configurations)
if not result:
return jsonify(message)
result, received_dataset = svm_checks.checkTrainingElements(payload['dataset'], received_dataset)
if not result:
return jsonify(received_dataset)
status, results = performTrain(received_dataset, model)
return jsonify({'status': status, 'results': results})
return jsonify({'results': 'Use a POST call.'})
def performPredict(received_dataset, obj):
model = obj['model']
timestr = obj['timestr']
svm_lib.writeSVM(f'{configurations["svmPredictions"]}/{model}-{timestr}.data', received_dataset)
status = subprocess.check_output(f'{configurations["svmPath"]}/svm_classify {configurations["svmPredictions"]}/{model}-{timestr}.data {configurations["svmModels"]}/{model}.model {configurations["svmPredictions"]}/{model}-{timestr}.predictions', shell=True).decode().splitlines()
dat = svm_lib.readSVM(f'{configurations["svmPredictions"]}/{model}-{timestr}.data')
res = svm_lib.readSVM(f'{configurations["svmPredictions"]}/{model}-{timestr}.predictions')
return status, dat, res
def performPredictGet(received_dataset, obj):
request = obj['request']
model = obj['model']
timestr = obj['timestr']
line = received_dataset+'\n'+svm_lib.elementsToSVM('1', request.values['data'].split(','))
svm_lib.writeSVM(f'{configurations["svmPredictions"]}/{model}-{timestr}.data', line)
status = subprocess.check_output(f'{configurations["svmPath"]}/svm_classify {configurations["svmPredictions"]}/{model}-{timestr}.data {configurations["svmModels"]}/{model}.model {configurations["svmPredictions"]}/{model}-{timestr}.predictions', shell=True).decode().splitlines()
results = svm_lib.readSVM(f'{configurations["svmPredictions"]}/{model}-{timestr}.predictions')
return results
@app.route('/model/<mymodel>/predict', methods=['GET', 'POST'])
def predict(mymodel):
model = escape(mymodel)
timestr = time.strftime("%Y%m%d-%H%M%S")+'-'+str(time.time())
received_dataset = f'# dataset - {timestr}'
if request.method == 'POST':
payload = request.get_json()
result, message = svm_checks.checkPredictAll({'payload':payload, 'model':model}, configurations)
if not result:
return jsonify(message)
result, received_dataset = svm_checks.checkPredictElements(payload['dataset'], received_dataset)
if not result:
return jsonify(received_dataset)
status, dat, res = performPredict(received_dataset, {'model':model, 'timestr':timestr})
results = []
for x in range(0,len(dat)-1):
results.append({'data':dat[x+1], 'result':res[x]})
return jsonify({'status': status, 'results': results})
elif request.method == 'GET': # if get
if 'data' in request.values:
"""line = received_dataset+'\n'+elementsToSVM('1', request.values['data'].split(','))
svm_lib.writeSVM(f'{configurations["svmPredictions"]}/{model}-{timestr}.data', line)
status = subprocess.check_output(f'{configurations["svmPath"]}/svm_classify {configurations["svmPredictions"]}/{model}-{timestr}.data {svmModels}/{configurations["svmModels"]}.model {configurations["svmPredictions"]}/{model}-{timestr}.predictions', shell=True).decode().splitlines()
results = svm_lib.readSVM(f'{configurations["svmPredictions"]}/{model}-{timestr}.predictions')"""
results = performPredictGet(received_dataset, {'request':request, 'model':model, 'timestr':timestr})
return jsonify({'results': results})
else:
return jsonify({'results': 'Missing data.'})
else:
return jsonify({'results': 'Use a POST call.'})
if __name__ == '__main__':
configuration = svm_utils.parseConfig(configurations)
app.import_name = '.'
socketio.run(app, host='0.0.0.0', port=5005)