readSave2.py

#from tensorflow import keras
import numpy as np
import keras
#from keras.datasets import imdb
#from keras.models import Sequential
#from keras.layers import LSTM
#from keras.layers.embeddings import Embedding
#from keras.preprocessing import sequence
from sklearn.metrics import confusion_matrix, precision_score
#from sklearn.model_selection import train_test_split
#from keras.layers import Dense,Dropout
#from keras.regularizers import l2
#import matplotlib.pyplot as plt
import pandas as pd
import numpy as np


model = keras.models.load_model('modelSave2')
print(model.summary())
while True:
    datafile = "testnew2"

    x_data = pd.read_csv(datafile+"X.csv",names=['s1','s2','s3','s4','s5'])
#    print(x_data)
#    y_dataR = np.genfromtxt(datafile+"Y.csv",delimiter=',',dtype=int)
#    num_classes = np.max(y_dataR) + 1
#    y_data = keras.utils.to_categorical(y_dataR, num_classes)
    #x_train, x_test, y_train, y_test = train_test_split(x_data,y_data, test_size=0.20, random_state=0)
    #x_train = x_data[:880]
    x_test = x_data[880:]
    #y_train = y_data[:880]
#    y_test = y_data[880:]
    #x_train.shape,y_train.shape,x_test.shape,y_test.shape
    
    #print('Training Accuracy : ' , np.mean(model.history["accuracy"]))
    #print('Validation Accuracy : ' , np.mean(model.history["val_accuracy"]))
    y_pred = model.predict(x_test)
    #print(x_test)
    #y_pred = model.predict(np.array([[400,400,400,400,400]]*50))
    rounded = [np.argmax(x) for x in y_pred]

#    y_testComp = [np.argmax(x) for x in y_test]
#    print(confusion_matrix(y_testComp,rounded))

    gestures = {
        1: "relax",
        2: "extend",
        3: "fist",
        4: "one",
        5: "transition"
    }
    print(rounded[-1])
    print("Gesture: "+gestures[rounded[-1]])