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loading models.py
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loading models.py
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from pandas import DataFrame, read_csv
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
import numpy as np
import serial
import pyttsx3
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score
from sklearn import svm
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import r2_score
from sklearn import tree
from sklearn.externals import joblib
#df = pd.read_csv('dataset.csv')
#X=df[['THUMB','INDEX','MIDDLE','RING','LITTLE','G1','G2','G3']]
#Y=df[['LABEL']]
ser = serial.Serial('COM5', baudrate = 9600, timeout =1)
for i in range (3):
arduinodata=ser.readline().strip()
values = arduinodata.decode('ascii').split(',')
a, b, c, d, e, f, g, h = [int(s) for s in values]
example = np.array([a, b, c, d, e, f, g, h])
example = example.reshape(1, -1)
# K Nearest Neighbor
#x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state = 60)
#model = KNeighborsClassifier()
#model.fit(x_train,y_train)
#joblib.dump(rf,'randomforest')
mj7 = joblib.load('KNN')
print(mj7.predict(example))
#predicted=model.predict(x_test)
#accuracy = accuracy_score(y_test,predicted)
#print("Accuracy for KNN is ",accuracy)
# Random Forest
#x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state = 60)
#rf = RandomForestRegressor(n_estimators = 1000)
#rf.fit(x_train,y_train)
#predictions = rf.predict(x_test)
#accuracy = r2_score(y_test,predictions)
#print('\nAccuracy for random forest:\n', round(accuracy, 2)*100, '%.')
#joblib.dump(rf,'randomforest')
mj = joblib.load('randomforest')
print(mj.predict(example))
#accuracy1 = r2_score(x_train,y_train)
#print('\nAccuracy for training random forest:\n', round(accuracy1, 2)*100, '%.')
# Naive Bayes
#
#x_train2, x_test2, y_train2, y_test2 = train_test_split(X, Y, test_size=0.3, random_state = 60)
#GNB = GaussianNB()
#GNB.fit(x_train,y_train)
#joblib.dump(GNB,'NaiveBayes')
mj1 = joblib.load('NaiveBayes')
print(mj1.predict(example))
#predicted2 = GNB.predict(x_test2)
#accuracy2 = accuracy_score(y_test2,predicted2)
#print("Accuracy for KNN is ",accuracy2)
# SVM
#x_train3, x_test3, y_train3, y_test3 = train_test_split(X, Y, test_size=0.3, random_state = 60)
#svc = svm.SVC(kernel='linear').fit(x_train,y_train)
#joblib.dump(svc,'SVM')
mj2 = joblib.load('SVM')
print(mj2.predict(example))
#predicted3 = svc.predict(x_test)
#accuracy3 = accuracy_score(y_test,predicted3)
#print("Accuracy for SVM is ",accuracy3)
# Logistic Regression
#x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state = 60)
#clf = LogisticRegression().fit(x_train,y_train)
#joblib.dump(clf,'logistic')
mj3 = joblib.load('logistic')
print(mj3.predict(example))
#predicted4 = clf.predict(x_test)
#accuracy4 = r2_score(y_test,predicted4)
#print("Accuracy for Logistic Regression is ",accuracy4)
# Decision Tree
#Dtree = tree.DecisionTreeClassifier().fit(x_train,y_train)
#joblib.dump(Dtree,'DecisionTree')
mj4 = joblib.load('DecisionTree')
print(mj4.predict(example))
#print(a)
#predicted5 = Dtree.predict(x_test)
#accuracy5 = accuracy_score(y_test,predicted5)
#print("Accuracy for Logistic Regression is ",accuracy5)
#if a <= 1:
#if a > 0 and a <=1 :
# abc = "victory"
# engine = pyttsx3.init()
# engine.say('Predicted Output')
# engine.say(abc)
# engine.runAndWait()
#elif a > 1 and a <=2 :
# abc = "Comehere"
# engine = pyttsx3.init()
# engine.say('Predicted Output')
# engine.say(abc)
# engine.runAndWait()
#elif a > 2 and a <=3 :
# abc = "Okay"
# engine = pyttsx3.init()
# engine.say('Predicted Output')
# engine.say(abc)
# engine.runAndWait()
#elif a > 3 and a <=4 :
# abc = "Stop"
# engine = pyttsx3.init()
# engine.say('Predicted Output')
# engine.say(abc)
# engine.runAndWait()
#elif a > 4 and a <=5 :
# abc = "You"
# engine = pyttsx3.init()
# engine.say('Predicted Output')
# engine.say(abc)
# engine.runAndWait()
#elif a > 5 and a <=6 :
# abc = "Hope"
# engine = pyttsx3.init()
# engine.say('Predicted Output')
# engine.say(abc)
# engine.runAndWait()
#elif a > 6 and a <=7 :
# abc = "Failure"
# engine = pyttsx3.init()
# engine.say('Predicted Output')
# engine.say(abc)
# engine.runAndWait()
#elif a > 7 and a <=8 :
# abc = "Really"
# engine = pyttsx3.init()
# engine.say('Predicted Output')
# engine.say(abc)
# engine.runAndWait()
#elif a > 8 and a <=9 :
# abc = "Quote"
# engine = pyttsx3.init()
# engine.say('Predicted Output')
# engine.say(abc)
# engine.runAndWait()
#elif a > 9 and a <=10 :
# abc = "No"
# engine = pyttsx3.init()
# engine.say('Predicted Output')
# engine.say(abc)
# engine.runAndWait()
#elif a > 10 and a <=11 :
# abc = "I Love you"
# engine = pyttsx3.init()
# engine.say('Predicted Output')
# engine.say(abc)
# engine.runAndWait()
#elif a > 11 and a <=12 :
# abc = "Livelong"
# engine = pyttsx3.init()
# engine.say('Predicted Output')
# engine.say(abc)
# engine.runAndWait()
#elif a > 12 and a <=13 :
# abc = "Thats it"
# engine = pyttsx3.init()
# engine.say('Predicted Output')
# engine.say(abc)
# engine.runAndWait()
#elif a > 13 and a <=14 :
# abc = "Solidarity"
# engine = pyttsx3.init()
# engine.say('Predicted Output')
# engine.say(abc)
# engine.runAndWait()
#elif a > 14 and a <=15 :
# abc = "Rock On"
# engine = pyttsx3.init()
# engine.say('Predicted Output')
# engine.say(abc)
# engine.runAndWait()
#elif a > 15 and a <=16 :
# abc = "Good Bye"
# engine = pyttsx3.init()
# engine.say('Predicted Output')
# engine.say(abc)
# engine.runAndWait()
#elif a > 16 and a <=17 :
# abc = "What is wrong"
# engine = pyttsx3.init()
# engine.say('Predicted Output')
# engine.say(abc)
# engine.runAndWait()
#elif a > 17 and a <=18 :
# abc = "Why"
# engine = pyttsx3.init()
# engine.say('Predicted Output')
# engine.say(abc)
# engine.runAndWait()
#elif a > 18 and a <= 19 :
# abc = "Rest room"
# engine = pyttsx3.init()
# engine.say('Predicted Output')
# engine.say(abc)
# engine.runAndWait()