from flask import Flask as fl, render_template, request, jsonify
import numpy as np
from Numerical_Method.numerical import Numerical
from Structured_Method_For_Platform.tool_for_freeman_code_gen import *
from Structured_Method_For_Platform.edit_distance_matrix_generation import *
from Autoencoders.network import *
import json
from flask import Response
import pandas as pd
from Sequence_Mining.sequence_mining import *
from Game.sudoku import *
#convert the image matrix to freeman code
app = fl(__name__)
@app.route('/')
def index():
return render_template("index.html")
"""[API calls to recieve predictions]
[the methods identify_digit_numerical and identify_digit_structural will return the prediced digit
for the image matrix input]
"""
@app.route('/identify-digit-numerical', methods=['GET', 'POST'])
def identify_digit_numerical():
from flask import Flask as fl
from flask import request, Response, render_template, jsonify
import MySQLdb as msql
import gevent
import gevent.monkey
from gevent.pywsgi import WSGIServer
from gevent.queue import Queue
import collections, json
from json import loads, dumps
app = fl(__name__)
def collect():
con = msql.connect(host='localhost', user='******', passwd='password', db='discover')
cur = con.cursor()
check = "SHOW TABLES LIKE 'nodes'"
cur.execute(check)
result = cur.fetchone()
if result:
pass
else:
cur.execute('CREATE TABLE nodes (Id INTEGER NOT NULL AUTO_INCREMENT,Hostname VARCHAR(30), MAC_Addr VARCHAR(18),IP_Addr VARCHAR(15), OS VARCHAR(10),OS_Vrs VARCHAR(10), OS_Type VARCHAR(30),OS_Acc VARCHAR(3),PRIMARY KEY (Id))')
cur.execute('SELECT * from nodes')
node_data = cur.fetchall()
return node_data
def collect_and_sort():
node_data = collect()
dictionary = collections.OrderedDict()
dt={}
count = 0
while True: