import socket

import msvcrt

import csv

import pandas as pd 

import tensorflow as tf 

import matplotlib as plt

import glob

import numpy as np 

from tensorflow import keras

from keras import Sequential

from sklearn.utils import shuffle

import sklearn.model_selection

STEP_SIZE = 20

SENSOR_NUM = 6

Label = { 'STD':0, 'WAL':1, 'JOG':2 , 'JUM':3, 'FALL':4 , 'LYI':5,'RA':6} #, 'JUM':3, 'LYI':4, 'FOL':5, 'FKL':5, 'BSC':5, 'SDL':5, 'STU':6, 'STN':7, 'SCH':8, 'SIT':9, 'CHU':10, 'CSI':11, 'CSO':12}

class_names = { 0:'STD', 1:'WAL', 2:'JOG' , 3:'JUM', 4:'FALL', 5:'LYI',6:'RA'}#, 3:'JUM', 4:'LYI', 5:'Falling', 6:'STU', 7:'STN', 8:'SCH', 9:'SIT', 10:'CHU', 11:'CSI', 12:'CSO'}

inputSensor = []

model = tf.keras.models.load_model('./model_4/')

model.summary()

l = []

packet = []

p = ""

tp = []

s = socket.socket()         

s.bind(('0.0.0.0', 80 ))

s.listen(0)    

i = 0      

record = input("Press R to start recording...")

if record == 'r' :

    # while True:

    client, addr = s.accept()

    #     x = kbfunc()

    carry = ''   

    try:

        while True:

            content = client.recv(1)

            if len(content) ==0:

                # print("heree?")

                break

            else:

                #print(len(content))

                temp = ''

                content = content.decode("utf-8")

                if content == '!':

                    tp =[]

                    p = ''

                elif content == '@':

                    tp.append(float(p))

                    # print(type(tp[0]))

                    p = ''

                    inputSensor.append(tp)

                    if len(inputSensor) > STEP_SIZE:

                        inputSensor.pop(0)

                    if len(inputSensor) == STEP_SIZE:

                        temp = np.array(inputSensor).reshape(-1, STEP_SIZE, SENSOR_NUM)

                        #print(temp)

                        pred = model.predict(temp)

                        results = np.argmax(pred, axis=1)

                        print("prediction: ", class_names[results[i]])

                        # inputSensor = []

                elif content ==',':

                    p = float(p)

                    p = p

                    tp.append(p)

                    p = ''

                else:

                    p += content

    except KeyboardInterrupt:

        pass