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284 lines
6.4 KiB
Python
284 lines
6.4 KiB
Python
"""
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This is a RESTful interface implemented using Flask micro framework.
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The API is driven by configuration that is organized in terms of the monitoring classes
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The API is both restful and websocket/socketio enabled.
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We designed the classes to be reusable (and powered by labels):
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'monitoring-type':
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'class':'<class-name>'
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'config':<labeled-class-specific-configuration>'
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"""
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from flask import Flask, session, request, redirect, Response
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from flask.templating import render_template
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from flask_session import Session
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import time
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import sys
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import os
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import json
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import re
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import monitor
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import Queue
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from utils.transport import *
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from utils.workers import ThreadManager, Factory
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from utils.ml import ML,AnomalyDetection,AnalyzeAnomaly
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import utils.params as SYS_ARGS
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import atexit
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app = Flask(__name__)
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app.config['SECRET_KEY'] = '!h8-[0v8]247-4-360'
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#app.secret_key = 'A0Zr98j/3yX R~XHH!jmN]LWX=?RT'
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PARAMS = SYS_ARGS.PARAMS
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f = open(PARAMS['path'])
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CONFIG = json.loads(f.read())
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f.close()
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#
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#
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#from threading import Thread, RLock
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p = CONFIG['store']['args']
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class_read = CONFIG['store']['class']['read']
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class_write= CONFIG['store']['class']['write']
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factory = DataSourceFactory()
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# gReader = factory.instance(type=class_read,args=p)
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atexit.register(ThreadManager.stop)
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@app.route('/get/<id>')
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def procs(id):
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try:
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gReader = factory.instance(type=class_read,args=p)
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data = gReader.read()
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ahandler = AnalyzeAnomaly()
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learn = {}
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if 'learn' in data :
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for row in data['learn'] :
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label = row['label']
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learn[label] = row
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r = {}
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for label in data :
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if label not in ['learn','folders'] :
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index = len(data[label]) - 1
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row = data[label][index]
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r[label] = row
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#
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# Let us determine if this is a normal operation or not
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# We will update the status of the information ...
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#
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for row in r[label] :
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index = r[label].index(row)
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if row['label'] in learn:
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id = row['label']
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px = ahandler.predict([row],learn[id])
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if px :
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# row['anomaly'] = px[1]==1
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print ""
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print label,' *** ',index
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row = dict(row,**px)
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r[label][index] =row
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#
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# @TODO:
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# Compile a report here that will be sent to the mailing list
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#
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except Exception, e:
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print e
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r = []
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return json.dumps(r)
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"""
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This function/endpoint will assess n-virtual environments and return the results
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@TODO: Should this be stored for future mining (I don't think so but could be wrong)
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"""
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@app.route('/sandbox')
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def sandbox():
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global CONFIG
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if 'sandbox' in CONFIG: #CONFIG['monitor']:
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#handler = HANDLERS['sandbox']['class']
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#conf = HANDLERS['sandbox']['config']
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r = []
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p = Factory.instance('sandbox',CONFIG)
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handler = p['class']
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conf = p['config']
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for id in conf:
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try:
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handler.init(conf[id])
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r.append (dict(handler.composite(),**{"label":id}))
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except Exception,e:
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pass
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else:
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r = []
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return json.dumps(r)
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@app.route('/trends')
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def trends ():
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id = request.args.get('id')
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app = request.args.get('app').strip()
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p = CONFIG['store']['args']
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class_read = CONFIG['store']['class']['read']
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gReader = factory.instance(type=class_read,args=p)
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r = gReader.read()
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if id in r:
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r = r[id] #--matrix
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series = []
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for row in r:
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series += [item for item in row if str(item['label'])== app]
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if len(series) > 12 :
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beg = len(series) - 13
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series = series[beg:]
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return json.dumps(series)
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else:
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return "[]"
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@app.route('/download',methods=['POST'])
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def requirements():
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stream = request.form['missing']
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print stream
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stream = "\n".join(json.loads(stream))
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headers = {"content-disposition":"attachment; filename=requirements.txt"}
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return Response(stream,mimetype='text/plain',headers=headers)
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@app.route('/dashboard')
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def dashboard():
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context = PARAMS['context']
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if 'title' in PARAMS :
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title = PARAMS['title']
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else:
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title = 'Zulu Watch Tower'
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return render_template('dashboard.html',context=context,title=title)
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"""
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This function is designed to trigger learning for anomaly detection
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@TODO: forward this to a socket i.e non-blocking socket
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"""
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@app.route('/anomalies/get')
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def learn():
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global CONFIG
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p = CONFIG['store']['args']
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class_read = CONFIG['store']['class']['read']
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gReader = factory.instance(type=class_read,args=p)
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d = gReader.read()
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if 'learn' in d :
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info = d['learn']
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del d['learn']
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else :
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info = []
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r = []
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if 'id' in request.args:
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id = request.args['id']
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d = d[id]
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params = {}
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for item in info:
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label = item['label']
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params[label] = item
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#apps = list(set(ML.Extract(['label'],d)))
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r = []
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if params :
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#
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# If we have parameters available
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p = AnomalyDetection()
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apps = params.keys()
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for name in apps :
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if name not in params:
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continue
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_info = params[name]
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try:
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xo = ML.Filter('label',name,d)
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except Exception,e:
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xo = []
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#print name,e
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if len(xo) == 0:
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continue
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xo = [xo[ len(xo) -1]]
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value = p.predict(xo,_info)[0]
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if len(value):
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report = dict(_info,**{'predicton':value})
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r.append(report)
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#print app,value
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#if value is not None:
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# r.append(value)
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return json.dumps(r)
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"""
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This function returns anomalies for a given context or group of processes
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The information returned is around precision/recall and f-score and parameters
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"""
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@app.route('/anomalies/status')
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def anomalies_status():
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global CONFIG
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p = CONFIG['store']['args']
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class_read = CONFIG['store']['class']['read']
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gReader = factory.instance(type=class_read,args=p)
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d = gReader.read()
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if 'learn' in d :
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info = d['learn']
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del d['learn']
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else :
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info = []
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print info
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r = []
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if 'id' in request.args:
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id = request.args['id']
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r = info
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return json.dumps(r)
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@app.route('/folders')
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def get_folders():
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global CONFIG
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p = CONFIG['store']['args']
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class_read = CONFIG['store']['class']['read']
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gReader = factory.instance(type=class_read,args=p)
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d = gReader.read()
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if 'folders' in d:
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d = d['folders']
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r = []
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m = {}
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for row in d :
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key = row.keys()[0]
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row = row[key]
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if key not in m:
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r.append(row)
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m[key] = len(r) -1
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else:
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index = m[key]
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r[index] = row
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d = r
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else:
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d = []
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return json.dumps(d)
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if __name__== '__main__':
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# ThreadManager.start(CONFIG)
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if 'port' not in SYS_ARGS.PARAMS :
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SYS_ARGS.PARAMS['port'] = 5000
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PORT = int(SYS_ARGS.PARAMS['port'])
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app.run(host='0.0.0.0',port=PORT,debug=True,threaded=True)
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