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ML MODELS.PY
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#THE API INTEGRATION IS DONE USING DJANGO MODULE IN AWS LIGHTSAIL -THAT IS NOT INCLUDED
from sudo easy_install pip import *
import numpy as np
import pandas as pd
# from gui_stuff import *
l2=[]
for x in range(0,len(l1)):
l2.append(0)
# TESTING DATA df -------------------------------------------------------------------------------------
df=pd.read_csv("Training.csv")
df.replace({'prognosis':{'Fungal infection':0,'Allergy':1,'GERD':2,'Chronic cholestasis':3,'Drug Reaction':4,
'Peptic ulcer diseae':5,'AIDS':6,'Diabetes ':7,'Gastroenteritis':8,'Bronchial Asthma':9,'Hypertension ':10,
'Migraine':11,'Cervical spondylosis':12,
'Paralysis (brain hemorrhage)':13,'Jaundice':14,'Malaria':15,'Chicken pox':16,'Dengue':17,'Typhoid':18,'hepatitis A':19,
'Hepatitis B':20,'Hepatitis C':21,'Hepatitis D':22,'Hepatitis E':23,'Alcoholic hepatitis':24,'Tuberculosis':25,
'Common Cold':26,'Pneumonia':27,'Dimorphic hemmorhoids(piles)':28,'Heart attack':29,'Varicose veins':30,'Hypothyroidism':31,
'Hyperthyroidism':32,'Hypoglycemia':33,'Osteoarthristis':34,'Arthritis':35,
'(vertigo) Paroymsal Positional Vertigo':36,'Acne':37,'Urinary tract infection':38,'Psoriasis':39,
'Impetigo':40}},inplace=True)
# print(df.head())
X= df[l1]
y = df[["prognosis"]]
np.ravel(y)
# print(y)
# TRAINING DATA tr --------------------------------------------------------------------------------
tr.replace({'prognosis':{'Fungal infection':0,'Allergy':1,'GERD':2,'Chronic cholestasis':3,'Drug Reaction':4,
'Peptic ulcer diseae':5,'AIDS':6,'Diabetes ':7,'Gastroenteritis':8,'Bronchial Asthma':9,'Hypertension ':10,
'Migraine':11,'Cervical spondylosis':12,
'Paralysis (brain hemorrhage)':13,'Jaundice':14,'Malaria':15,'Chicken pox':16,'Dengue':17,'Typhoid':18,'hepatitis A':19,
'Hepatitis B':20,'Hepatitis C':21,'Hepatitis D':22,'Hepatitis E':23,'Alcoholic hepatitis':24,'Tuberculosis':25,
'Common Cold':26,'Pneumonia':27,'Dimorphic hemmorhoids(piles)':28,'Heart attack':29,'Varicose veins':30,'Hypothyroidism':31,
'Hyperthyroidism':32,'Hypoglycemia':33,'Osteoarthristis':34,'Arthritis':35,
'(vertigo) Paroymsal Positional Vertigo':36,'Acne':37,'Urinary tract infection':38,'Psoriasis':39,
'Impetigo':40}},inplace=True)
X_test= tr[l1]
y_test = tr[["prognosis"]]
np.ravel(y_test)
# ------------------------------------------------------------------------------------------------------
def DecisionTree():
from sklearn import tree
clf3 = tree.DecisionTreeClassifier() # empty model of the decision tree
clf3 = clf3.fit(X,y)
# calculating accuracy-------------------------------------------------------------------
from sklearn.metrics import accuracy_score
y_pred=clf3.predict(X_test)
print(accuracy_score(y_test, y_pred))
print(accuracy_score(y_test, y_pred,normalize=False))
# -----------------------------------------------------
psymptoms = [Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get()]
for k in range(0,len(l1)):
# print (k,)
for z in psymptoms:
if(z==l1[k]):
l2[k]=1
inputtest = [l2]
predict = clf3.predict(inputtest)
predicted=predict[0]
h='no'
for a in range(0,len(disease)):
if(predicted == a):
h='yes'
break
if (h=='yes'):
t1.delete("1.0", END)
t1.insert(END, disease[a])
else:
t1.delete("1.0", END)
t1.insert(END, "Not Found")
def randomforest():
from sklearn.ensemble import RandomForestClassifier
clf4 = RandomForestClassifier()
clf4 = clf4.fit(X,np.ravel(y))
# calculating accuracy-------------------------------------------------------------------
from sklearn.metrics import accuracy_score
y_pred=clf4.predict(X_test)
print(accuracy_score(y_test, y_pred))
print(accuracy_score(y_test, y_pred,normalize=False))
# -----------------------------------------------------
psymptoms = [Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get()]
for k in range(0,len(l1)):
for z in psymptoms:
if(z==l1[k]):
l2[k]=1
inputtest = [l2]
predict = clf4.predict(inputtest)
predicted=predict[0]
h='no'
for a in range(0,len(disease)):
if(predicted == a):
h='yes'
break
if (h=='yes'):
t2.delete("1.0", END)
t2.insert(END, disease[a])
else:
t2.delete("1.0", END)
t2.insert(END, "Not Found")
def NaiveBayes():
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
gnb=gnb.fit(X,np.ravel(y))
# calculating accuracy-------------------------------------------------------------------
from sklearn.metrics import accuracy_score
y_pred=gnb.predict(X_test)
print(accuracy_score(y_test, y_pred))
print(accuracy_score(y_test, y_pred,normalize=False))
# -----------------------------------------------------
psymptoms = [Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get()]
for k in range(0,len(l1)):
for z in psymptoms:
if(z==l1[k]):
l2[k]=1
inputtest = [l2]
predict = gnb.predict(inputtest)
predicted=predict[0]
h='no'
for a in range(0,len(disease)):
if(predicted == a):
h='yes'
break
if (h=='yes'):
t3.delete("1.0", END)
t3.insert(END, disease[a])
else:
t3.delete("1.0", END)
t3.insert(END, "Not Found")
# gui_stuff------------------------------------------------------------------------------------
root = Tk()
root.configure(background='blue')
# entry variables
Symptom1 = StringVar()
Symptom1.set(None)
Symptom2 = StringVar()
Symptom2.set(None)
Symptom3 = StringVar()
Symptom3.set(None)
Symptom4 = StringVar()
Symptom4.set(None)
Symptom5 = StringVar()
Symptom5.set(None)
Name = StringVar()
# Heading
w2 = Label(root, justify=LEFT, text="Disease Predictor using Machine Learning", fg="white", bg="blue")
w2.config(font=("Elephant", 30))
w2.grid(row=1, column=0, columnspan=2, padx=100)
w2 = Label(root, justify=LEFT, text="A Project by Yaswanth Sai Palaghat", fg="white", bg="blue")
w2.config(font=("Aharoni", 30))
w2.grid(row=2, column=0, columnspan=2, padx=100)
# labels
NameLb = Label(root, text="Name of the Patient", fg="yellow", bg="black")
NameLb.grid(row=6, column=0, pady=15, sticky=W)
S1Lb = Label(root, text="Symptom 1", fg="yellow", bg="black")
S1Lb.grid(row=7, column=0, pady=10, sticky=W)
S2Lb = Label(root, text="Symptom 2", fg="yellow", bg="black")
S2Lb.grid(row=8, column=0, pady=10, sticky=W)
S3Lb = Label(root, text="Symptom 3", fg="yellow", bg="black")
S3Lb.grid(row=9, column=0, pady=10, sticky=W)
S4Lb = Label(root, text="Symptom 4", fg="yellow", bg="black")
S4Lb.grid(row=10, column=0, pady=10, sticky=W)
S5Lb = Label(root, text="Symptom 5", fg="yellow", bg="black")
S5Lb.grid(row=11, column=0, pady=10, sticky=W)
lrLb = Label(root, text="DecisionTree", fg="white", bg="red")
lrLb.grid(row=15, column=0, pady=10,sticky=W)
destreeLb = Label(root, text="RandomForest", fg="white", bg="red")
destreeLb.grid(row=17, column=0, pady=10, sticky=W)
ranfLb = Label(root, text="NaiveBayes", fg="white", bg="red")
ranfLb.grid(row=19, column=0, pady=10, sticky=W)
# entries
OPTIONS = sorted(l1)
NameEn = Entry(root, textvariable=Name)
NameEn.grid(row=6, column=1)
S1En = OptionMenu(root, Symptom1,*OPTIONS)
S1En.grid(row=7, column=1)
S2En = OptionMenu(root, Symptom2,*OPTIONS)
S2En.grid(row=8, column=1)
S3En = OptionMenu(root, Symptom3,*OPTIONS)
S3En.grid(row=9, column=1)
S4En = OptionMenu(root, Symptom4,*OPTIONS)
S4En.grid(row=10, column=1)
S5En = OptionMenu(root, Symptom5,*OPTIONS)
S5En.grid(row=11, column=1)
dst = Button(root, text="DecisionTree", command=DecisionTree,bg="green",fg="yellow")
dst.grid(row=8, column=3,padx=10)
rnf = Button(root, text="Randomforest", command=randomforest,bg="green",fg="yellow")
rnf.grid(row=9, column=3,padx=10)
lr = Button(root, text="NaiveBayes", command=NaiveBayes,bg="green",fg="yellow")
lr.grid(row=10, column=3,padx=10)
#textfileds
t1 = Text(root, height=1, width=40,bg="orange",fg="black")
t1.grid(row=15, column=1, padx=10)
t2 = Text(root, height=1, width=40,bg="orange",fg="black")
t2.grid(row=17, column=1 , padx=10)
t3 = Text(root, height=1, width=40,bg="orange",fg="black")
t3.grid(row=19, column=1 , padx=10)
root.mainloop()