clean_code.py

from tkinter import *
import numpy as np

import pandas as pd
# from gui_stuff import *

l1=['0.O-blood-group','1.Other-blood-group','2.Child','3.Adult','4.Older-adult','5.Yes','6.Yes','7.Yes','8.Yes','9.No']

disease=[
    'Very-high','Pretty-High','High','Pretty-low','low']

l2=[]
for x in range(0,len(l1)):
    l2.append(0)

# TESTING DATA df -------------------------------------------------------------------------------------
df=pd.read_csv("test.csv")

df.replace({'prognosis':{'Very-high':0,'Pretty-High':1,'High':2,'Pretty-low':3,'low':4}},inplace=True)

# print(df.head())

X= df[l1]


y = df[["prognosis"]]
np.ravel(y)
# print(y)

# TRAINING DATA tr --------------------------------------------------------------------------------
tr=pd.read_csv("train.csv")
tr.replace({'prognosis':{'Very-high':0,'Pretty-High':1,'High':2,'Pretty-low':3,'low':4}},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() ,Symptom6.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)):# assigning value for variable "a" while checking the output number exists line 20 
        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() ,Symptom6.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() ,Symptom6.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='Red')

# 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)
Symptom6 = StringVar()
Symptom6.set(None)
Name = StringVar()

# Heading
w2 = Label(root, justify=LEFT, text="Disease Predictor using Machine Learning", fg="black", bg="red")
w2.config(font=("Elephant", 30))
w2.grid(row=1, column=0, columnspan=2, padx=100)
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)

#Poor GUI stuffs
S1Lb = Label(root, text="Choose Your Blood Group (either O-blood-group or Other-blood-group)", fg="yellow", bg="black")
S1Lb.grid(row=7, column=0, pady=10, sticky=W)

S2Lb = Label(root, text="Age Group (Child or Adult or Older-adult)", fg="yellow", bg="black")
S2Lb.grid(row=8, column=0, pady=10, sticky=W)

S3Lb = Label(root, text="Medical Complication (Choose 'No Med. Cond.' if None, Choose from Heart-Disease,Lung-Disease, Diabetes, Asthma)", fg="yellow", bg="black")
S3Lb.grid(row=9, column=0, pady=10, sticky=W)

S4Lb = Label(root, text="Past International travel History", fg="yellow", bg="black")
S4Lb.grid(row=10, column=0, pady=10, sticky=W)

S5Lb = Label(root, text="Contact with a Covid-19 positive Patient", fg="yellow", bg="black")
S5Lb.grid(row=11, column=0, pady=10, sticky=W)

S6Lb = Label(root, text="Are you Experiencing Dry cough, Vomiting, Nausea", fg="yellow", bg="black")
S6Lb.grid(row=12, column=0, pady=10, sticky=W)

lrLb = Label(root, text="Decision Tree", fg="white", bg="red")
lrLb.grid(row=15, column=0, pady=10,sticky=W)

destreeLb = Label(root, text="Random Forest", fg="white", bg="red")
destreeLb.grid(row=17, column=0, pady=10, sticky=W)

ranfLb = Label(root, text="Naive Bayes", 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)

S6En = OptionMenu(root, Symptom6,*OPTIONS)
S6En.grid(row=12, column=1)


dst = Button(root, text="Predict With Decision Tree", command=DecisionTree,bg="green",fg="yellow")
dst.grid(row=15, column=3,padx=10)

rnf = Button(root, text="Predict with Random Forest", command=randomforest,bg="green",fg="yellow")
rnf.grid(row=17, column=3,padx=10)

lr = Button(root, text="Predict with Naive Bayes", command=NaiveBayes,bg="green",fg="yellow")
lr.grid(row=19, 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()