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: