"""
Creating a server for activity recognition
"""
from flask import Flask, jsonify, request
import numpy as np
import pickle
from sklearn.preprocessing import normalize


app = Flask('__name__')
filename = 'D:/Vacation/ML/sangam/main/classifier.sav'
time = [0, 0, 0]
data = {"walking":str(time[0])+",0", "running":str(time[1])+",0", "standing":str(time[2])+",0"}

@app.route('/', methods = ['GET', 'POST'])
def index():
    global time
    global data
    arduino_data = request.get_json()

    if request.method=="GET":
        return jsonify(data)
    else:
        sensorValues = arduino_data["values"]
        print(sensorValues)
        X_test = np.array(sensorValues[0:9])
        classifier = pickle.load(open(filename, 'rb'))
        n = classifier.predict(normalize(X_test))

        matcher = {'1':"walking", '2':"running", '3':"standing"}
        print("")
        print("")
        print("The person appears to be " + matcher[str(int(n))] + ".")
        print("")
        print("")

        time[int(n)-1]+=0.04

        data = {"walking":str(time[0])+",0", "running":str(time[1])+",0", "standing":str(time[2])+",0"}

        return jsonify(data)
    
if __name__ == '__main__':
    app.run(host='0.0.0.0')
be8564d44fa487a0a27ed0ad2ef2b45a451d9e56
<mask token>


@app.route('/', methods=['GET', 'POST'])
def index():
    global time
    global data
    arduino_data = request.get_json()
    if request.method == 'GET':
        return jsonify(data)
    else:
        sensorValues = arduino_data['values']
        print(sensorValues)
        X_test = np.array(sensorValues[0:9])
        classifier = pickle.load(open(filename, 'rb'))
        n = classifier.predict(normalize(X_test))
        matcher = {'1': 'walking', '2': 'running', '3': 'standing'}
        print('')
        print('')
        print('The person appears to be ' + matcher[str(int(n))] + '.')
        print('')
        print('')
        time[int(n) - 1] += 0.04
        data = {'walking': str(time[0]) + ',0', 'running': str(time[1]) +
            ',0', 'standing': str(time[2]) + ',0'}
        return jsonify(data)


<mask token>

<mask token>


@app.route('/', methods=['GET', 'POST'])
def index():
    global time
    global data
    arduino_data = request.get_json()
    if request.method == 'GET':
        return jsonify(data)
    else:
        sensorValues = arduino_data['values']
        print(sensorValues)
        X_test = np.array(sensorValues[0:9])
        classifier = pickle.load(open(filename, 'rb'))
        n = classifier.predict(normalize(X_test))
        matcher = {'1': 'walking', '2': 'running', '3': 'standing'}
        print('')
        print('')
        print('The person appears to be ' + matcher[str(int(n))] + '.')
        print('')
        print('')
        time[int(n) - 1] += 0.04
        data = {'walking': str(time[0]) + ',0', 'running': str(time[1]) +
            ',0', 'standing': str(time[2]) + ',0'}
        return jsonify(data)


if __name__ == '__main__':
    app.run(host='0.0.0.0')

<mask token>
app = Flask('__name__')
filename = 'D:/Vacation/ML/sangam/main/classifier.sav'
time = [0, 0, 0]
data = {'walking': str(time[0]) + ',0', 'running': str(time[1]) + ',0',
    'standing': str(time[2]) + ',0'}


@app.route('/', methods=['GET', 'POST'])
def index():
    global time
    global data
    arduino_data = request.get_json()
    if request.method == 'GET':
        return jsonify(data)
    else:
        sensorValues = arduino_data['values']
        print(sensorValues)
        X_test = np.array(sensorValues[0:9])
        classifier = pickle.load(open(filename, 'rb'))
        n = classifier.predict(normalize(X_test))
        matcher = {'1': 'walking', '2': 'running', '3': 'standing'}
        print('')
        print('')
        print('The person appears to be ' + matcher[str(int(n))] + '.')
        print('')
        print('')
        time[int(n) - 1] += 0.04
        data = {'walking': str(time[0]) + ',0', 'running': str(time[1]) +
            ',0', 'standing': str(time[2]) + ',0'}
        return jsonify(data)


if __name__ == '__main__':
    app.run(host='0.0.0.0')

<mask token>
from flask import Flask, jsonify, request
import numpy as np
import pickle
from sklearn.preprocessing import normalize
app = Flask('__name__')
filename = 'D:/Vacation/ML/sangam/main/classifier.sav'
time = [0, 0, 0]
data = {'walking': str(time[0]) + ',0', 'running': str(time[1]) + ',0',
    'standing': str(time[2]) + ',0'}


@app.route('/', methods=['GET', 'POST'])
def index():
    global time
    global data
    arduino_data = request.get_json()
    if request.method == 'GET':
        return jsonify(data)
    else:
        sensorValues = arduino_data['values']
        print(sensorValues)
        X_test = np.array(sensorValues[0:9])
        classifier = pickle.load(open(filename, 'rb'))
        n = classifier.predict(normalize(X_test))
        matcher = {'1': 'walking', '2': 'running', '3': 'standing'}
        print('')
        print('')
        print('The person appears to be ' + matcher[str(int(n))] + '.')
        print('')
        print('')
        time[int(n) - 1] += 0.04
        data = {'walking': str(time[0]) + ',0', 'running': str(time[1]) +
            ',0', 'standing': str(time[2]) + ',0'}
        return jsonify(data)


if __name__ == '__main__':
    app.run(host='0.0.0.0')

"""
Creating a server for activity recognition
"""
from flask import Flask, jsonify, request
import numpy as np
import pickle
from sklearn.preprocessing import normalize


app = Flask('__name__')
filename = 'D:/Vacation/ML/sangam/main/classifier.sav'
time = [0, 0, 0]
data = {"walking":str(time[0])+",0", "running":str(time[1])+",0", "standing":str(time[2])+",0"}

@app.route('/', methods = ['GET', 'POST'])
def index():
    global time
    global data
    arduino_data = request.get_json()

    if request.method=="GET":
        return jsonify(data)
    else:
        sensorValues = arduino_data["values"]
        print(sensorValues)
        X_test = np.array(sensorValues[0:9])
        classifier = pickle.load(open(filename, 'rb'))
        n = classifier.predict(normalize(X_test))

        matcher = {'1':"walking", '2':"running", '3':"standing"}
        print("")
        print("")
        print("The person appears to be " + matcher[str(int(n))] + ".")
        print("")
        print("")

        time[int(n)-1]+=0.04

        data = {"walking":str(time[0])+",0", "running":str(time[1])+",0", "standing":str(time[2])+",0"}

        return jsonify(data)
    
if __name__ == '__main__':
    app.run(host='0.0.0.0')