app.py

from flask import Flask, redirect, url_for, render_template, request, jsonify
import flask
import asyncio
import io
import glob
import os
from flask_uploads import UploadSet, configure_uploads, IMAGES, patch_request_class
from flask_wtf import FlaskForm
from flask_wtf.file import FileField, FileRequired, FileAllowed
from wtforms import SubmitField
import sys
import time
import uuid
import requests
import sqlite3
from urllib.parse import urlparse
from io import BytesIO
from PIL import Image, ImageDraw
from azure.cognitiveservices.vision.face import FaceClient
from msrest.authentication import CognitiveServicesCredentials
from azure.cognitiveservices.vision.face.models import TrainingStatusType, Person, SnapshotObjectType, OperationStatusType
from global_variables import personGroupId
from web3 import Web3
import werkzeug
import json
import hashlib

app = Flask(__name__)
basedir = os.path.abspath(os.path.dirname(__file__))
app.config["IMAGE_UPLOADS"] = os.getcwd() + "/uploads"
app.config["LOGIN"] = os.getcwd() + "/login"
app.config["IMG_MEDICINES"] = os.getcwd() + "/medicines"


@app.route('/payment', methods=['POST'])
def payment():
    if request.method == 'POST' and request.files:
        imagex = request.files["image"]
        imagex.save(os.path.join(app.config["IMAGE_UPLOADS"], imagex.filename))

        KEY = '2698a28d0b3a47be9a0177011b4fca38'
        # Replace with your regional Base URL
        ENDPOINT = 'https://hackcovid.cognitiveservices.azure.com/'

        face_client = FaceClient(ENDPOINT, CognitiveServicesCredentials(KEY))
        test_image_array = glob.glob(os.path.join(
            app.config["IMAGE_UPLOADS"], imagex.filename))
        image = open(test_image_array[0], 'r+b')

        # Detect faces
        face_ids = []
        faces = face_client.face.detect_with_stream(image)
        for face in faces:
            face_ids.append(face.face_id)

        print(face_ids)
        # Identify faces
        results = face_client.face.identify(face_ids, personGroupId)
        print('Identifying faces in {}'.format(os.path.basename(image.name)))
        if not results:
            print('No person identified in the person group for faces from {}.'.format(
                os.path.basename(image.name)))
        res = 0
        for person in results:
            print('Person for face ID {} is identified in {} with a confidence of {}.'.format(
                person.face_id, os.path.basename(image.name), person.candidates[0].confidence))  # Get topmost confidence score
            print(person.candidates[0].person_id)
            res = person.candidates[0].person_id

        print(res)
        connect = sqlite3.connect("Face-DataBase")
        c = connect.cursor()
        c.execute("SELECT * FROM Students WHERE personID = ?", (res,))
        row = c.fetchone()
        print(row[1] + " recognized")

        c = connect.cursor()
        c.execute("SELECT * FROM Students WHERE personID = ?", (res,))
        row = c.fetchone()

        account_1 = request.form.get('publickey')
        # account_1 = account_1[1:-1]
        account_2 = row[4]

        private_key = request.form.get('privatekey')
        # private_key = private_key[1:-1]
        print('private_key', private_key)

        amount = float(request.form.get('amount'))
        print(amount)
        connect.commit()
        connect.close()
        ganache_url = "http://ec2-54-175-197-129.compute-1.amazonaws.com:8545"
        web3 = Web3(Web3.HTTPProvider(ganache_url))
        web3.eth.defaultAccount = account_1
        nonce = web3.eth.getTransactionCount(account_1)
        if int(web3.eth.getBalance(account_1)) < int(amount*(10**18)):
            return jsonify({"status": "error"})
        print(nonce)
        tx = {
            'nonce': nonce,
            'to': account_2,
            'value': web3.toWei(amount, 'ether'),
            'gas': 2000000,
            'gasPrice': web3.toWei('50', 'gwei'),
        }

        signed_tx = web3.eth.account.signTransaction(tx, private_key)

        tx_hash = web3.eth.sendRawTransaction(signed_tx.rawTransaction)
        print(web3.toHex(tx_hash))
        return jsonify({"status": "success", "tx_hash": web3.toHex(tx_hash)})
    else:
        return render_template('index.html')


@app.route('/login', methods=['POST'])
def login():
    if request.method == 'POST' and request.files:
        imagex = request.files["image"]
        imagex.save(os.path.join(app.config["LOGIN"], imagex.filename))

        KEY = '2698a28d0b3a47be9a0177011b4fca38'
        ENDPOINT = 'https://hackcovid.cognitiveservices.azure.com/'

        face_client = FaceClient(ENDPOINT, CognitiveServicesCredentials(KEY))
        test_image_array = glob.glob(os.path.join(
            app.config["LOGIN"], imagex.filename))
        image = open(test_image_array[0], 'r+b')

        # Detect faces
        face_ids = []
        faces = face_client.face.detect_with_stream(image)
        for face in faces:
            face_ids.append(face.face_id)

        print(face_ids)
        # Identify faces
        results = None
        if face_ids:
            results = face_client.face.identify(face_ids, personGroupId)
            print('Identifying faces in {}'.format(
                os.path.basename(image.name)))
        if not results:
            print('No person identified in the person group for faces from {}.'.format(
                os.path.basename(image.name)))
            return jsonify({"error": "none"})
            return
        res = 0
        for person in results:
            print('Person for face ID {} is identified in {} with a confidence of {}.'.format(
                person.face_id, os.path.basename(image.name), person.candidates[0].confidence))  # Get topmost confidence score
            print(person.candidates[0].person_id)
            res = person.candidates[0].person_id

        print(res)
        connect = sqlite3.connect("Face-DataBase")
        c = connect.cursor()
        c.execute("SELECT * FROM Students WHERE personID = ?", (res,))
        row = c.fetchone()
        print(row[1] + " recognized")

        c = connect.cursor()
        c.execute("SELECT * FROM Students WHERE personID = ?", (res,))
        row = c.fetchone()

        return jsonify({"name": row[1], "publickey": row[4], "privatekey": row[5], "type": row[9]})


@app.route('/getinfo', methods=['POST'])
def getinfo():
    if request.method == 'POST' and request.files:
        imagex = request.files["image"]
        imagex.save(os.path.join(app.config["IMG_MEDICINES"], imagex.filename))

        KEY = '2698a28d0b3a47be9a0177011b4fca38'
        ENDPOINT = 'https://hackcovid.cognitiveservices.azure.com/'

        face_client = FaceClient(ENDPOINT, CognitiveServicesCredentials(KEY))
        test_image_array = glob.glob(os.path.join(
            app.config["IMG_MEDICINES"], imagex.filename))
        image = open(test_image_array[0], 'r+b')

        # Detect faces
        face_ids = []
        faces = face_client.face.detect_with_stream(image)
        for face in faces:
            face_ids.append(face.face_id)

        print(face_ids)
        # Identify faces
        results = None
        if face_ids:
            results = face_client.face.identify(face_ids, personGroupId)
            print('Identifying faces in {}'.format(
                os.path.basename(image.name)))
        if not results:
            print('No person identified in the person group for faces from {}.'.format(
                os.path.basename(image.name)))
            return jsonify({"error": "none"})
            return
        res = 0
        for person in results:
            print('Person for face ID {} is identified in {} with a confidence of {}.'.format(
                person.face_id, os.path.basename(image.name), person.candidates[0].confidence))  # Get topmost confidence score
            print(person.candidates[0].person_id)
            res = person.candidates[0].person_id

        print(res)
        connect = sqlite3.connect("Face-DataBase")
        c = connect.cursor()
        c.execute("SELECT * FROM Students WHERE personID = ?", (res,))
        row = c.fetchone()
        print(row[1] + " recognized")

        c = connect.cursor()
        c.execute("SELECT * FROM Students WHERE personID = ?", (res,))
        row = c.fetchone()
        publickey = row[4]
        ganache_url = "http://ec2-54-175-197-129.compute-1.amazonaws.com:8545"
        web3 = Web3(Web3.HTTPProvider(ganache_url))
        abi = json.loads('[{"constant":false,"inputs":[{"name":"medicine","type":"string"}],"name":"addmedicines","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"stat","type":"int256"}],"name":"updatestatus","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":true,"inputs":[],"name":"getmedicines","outputs":[{"name":"","type":"string"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[],"name":"getstatus","outputs":[{"name":"","type":"int256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"","type":"address"}],"name":"medicines","outputs":[{"name":"","type":"string"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"","type":"address"}],"name":"status","outputs":[{"name":"","type":"int256"}],"payable":false,"stateMutability":"view","type":"function"}]')
        address = web3.toChecksumAddress(
            "0xC70003Ef71fdEa97A785183615B871A99cFB73b7")
        contract = web3.eth.contract(address=address, abi=abi)
        web3.eth.defaultAccount = publickey
        medicines = contract.functions.getmedicines().call()
        status = contract.functions.getstatus().call()
        if int(request.form.get('type')) == 1 or int(request.form.get('type')) == 2:
            return jsonify({"medicines": str(medicines), "status": status})
        else:
            return jsonify({"status": status})


@app.route('/addinfo', methods=['POST'])
def addinfo():
    if request.method == 'POST' and request.files and int(request.form.get('type')) == 2:
        imagex = request.files["image"]
        imagex.save(os.path.join(app.config["IMG_MEDICINES"], imagex.filename))

        KEY = '2698a28d0b3a47be9a0177011b4fca38'
        ENDPOINT = 'https://hackcovid.cognitiveservices.azure.com/'

        face_client = FaceClient(ENDPOINT, CognitiveServicesCredentials(KEY))
        test_image_array = glob.glob(os.path.join(
            app.config["IMG_MEDICINES"], imagex.filename))
        image = open(test_image_array[0], 'r+b')

        # Detect faces
        face_ids = []
        faces = face_client.face.detect_with_stream(image)
        for face in faces:
            face_ids.append(face.face_id)

        print(face_ids)
        # Identify faces
        results = None
        if face_ids:
            results = face_client.face.identify(face_ids, personGroupId)
            print('Identifying faces in {}'.format(
                os.path.basename(image.name)))
        if not results:
            print('No person identified in the person group for faces from {}.'.format(
                os.path.basename(image.name)))
            return jsonify({"error": "none"})
            return
        res = 0
        for person in results:
            print('Person for face ID {} is identified in {} with a confidence of {}.'.format(
                person.face_id, os.path.basename(image.name), person.candidates[0].confidence))  # Get topmost confidence score
            print(person.candidates[0].person_id)
            res = person.candidates[0].person_id

        print(res)
        connect = sqlite3.connect("Face-DataBase")
        c = connect.cursor()
        c.execute("SELECT * FROM Students WHERE personID = ?", (res,))
        row = c.fetchone()
        print(row[1] + " recognized")

        c = connect.cursor()
        c.execute("SELECT * FROM Students WHERE personID = ?", (res,))
        row = c.fetchone()
        publickey = row[4]
        ganache_url = "http://ec2-54-175-197-129.compute-1.amazonaws.com:8545"
        web3 = Web3(Web3.HTTPProvider(ganache_url))
        abi = json.loads('[{"constant":false,"inputs":[{"name":"medicine","type":"string"}],"name":"addmedicines","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"stat","type":"int256"}],"name":"updatestatus","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":true,"inputs":[],"name":"getmedicines","outputs":[{"name":"","type":"string"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[],"name":"getstatus","outputs":[{"name":"","type":"int256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"","type":"address"}],"name":"medicines","outputs":[{"name":"","type":"string"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"","type":"address"}],"name":"status","outputs":[{"name":"","type":"int256"}],"payable":false,"stateMutability":"view","type":"function"}]')
        address = web3.toChecksumAddress(
            "0xC70003Ef71fdEa97A785183615B871A99cFB73b7")
        contract = web3.eth.contract(address=address, abi=abi)
        web3.eth.defaultAccount = publickey

        medicines = request.form.get('medicines')
        contract.functions.addmedicines(medicines).transact()
        print(request.form.get('status'))
        status = int(request.form.get('status'))
        contract.functions.updatestatus(status).transact()

        return jsonify({"status": 'success'})


@app.route('/balance', methods=['POST'])
def balance():
    if request.method == 'POST':
        ganache_url = "http://ec2-54-175-197-129.compute-1.amazonaws.com:8545"
        web3 = Web3(Web3.HTTPProvider(ganache_url))
        balance = web3.eth.getBalance(request.form.get('publickey'))
        return jsonify({"balance": str(balance)})


@app.route('/emaillogin', methods=['POST'])
def emaillogin():
    if request.method == 'POST':
        email = request.form.get('email')
        password = request.form.get('password')
        hashcode = hashlib.md5(password.encode())
        passhash = hashcode.hexdigest()
        connect = sqlite3.connect("Face-DataBase")
        c = connect.cursor()
        c.execute("SELECT * FROM Students WHERE email = ?", (email,))
        row = c.fetchone()
        if row and row[7] == passhash:
            return jsonify({"message": "valid", "name": row[1], "publickey": row[4], "privatekey": row[5], "type": row[9]})
        else:
            return jsonify({"message": "invalid"})


if __name__ == '__main__':
    app.run(debug=True)