main.py

import csv
import tkinter
from tkinter import *
from sklearn import feature_extraction
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.naive_bayes import  MultinomialNB
from sklearn.preprocessing import normalize
from sklearn.metrics import accuracy_score
from functools import partial
import pandas as p
from sklearn import feature_extraction
from csv import DictWriter
import matplotlib.pyplot as plt

BagOfWords = list()
def dataDisplay():
    data = p.read_csv("D:\spam.csv", encoding='latin-1')
    count_Class=p.value_counts(data["class"], sort= True)
    count_Class.plot(kind= 'bar', color= ["blue", "orange"])
    plt.ylabel('Emails')
    plt.title('Bar chart')
    plt.show()


def DataProcessing():
    data = p.read_csv("D:\spam.csv", encoding='latin-1')
  #  print(data['class'].value_counts(normalize=True))
    # data.drop_duplicates(inplace= True)
    data["email"].fillna("hello david how are you", inplace=True)
    data["class"].fillna("ham", inplace=True)
    x = data.iloc[:, 1]
    x = x.str.replace("[^0-9a-zA-Z]", " ",regex=True)
    x = x.str.replace("subject"," ")
  #  print(x.to_string)
    y = data["class"]
    y = y.map({"spam": 1, "ham": 0})
    features = feature_extraction.text.CountVectorizer(analyzer="word", stop_words="english", max_features=3000,
                                                       min_df=0.0001)
    x = features.fit_transform(x)

    BagOfWords = features.get_feature_names_out()
  #  print(BagOfWords)
    return x, y, BagOfWords


def writeNewEmailToCSV(label, email):
    print("in this method")
    newEntry =[]
    newEntry.append(label)
    newEntry.append(email)
    print(newEntry[0],newEntry[1])


    with open("D:\spam.csv", mode="a" ,newline='') as file:
        writer=csv.writer(file)
        writer.writerow(newEntry)
    # file.write(newEntry)
        file.close()





def testEmail(bagofWords, email):
    email1 = email
    email = [email]
    features = feature_extraction.text.CountVectorizer(vocabulary=bagofWords)
    email = features.fit_transform(email)
    return email


def TrainModel():
        x , y , BagOfWords= DataProcessing()
        X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = 0.25, random_state = 45)
        classifier = MultinomialNB(alpha=1)
        classifier.fit(normalize(X_train), y_train)
        y_pred = classifier.predict(X_test)
        Accuracy = accuracy_score(y_test, y_pred)*100
        return Accuracy, BagOfWords, classifier



def TestEmail(BagOfWords,classifier, email):
    
    email = testEmail(BagOfWords,email)
    result = classifier.predict(email)
    if result[0] == 1:
        # dataProcessing.writeNewEmailToCSV("spam", email1)
        return "email is spam","spam"
    else:
        # dataProcessing.writeNewEmailToCSV("ham", email1)
        return "email is ham", "ham"






dataDisplay()


from tkinter import *
from tkinter import messagebox

w = Tk()
w.geometry('350x500')
w.title(' SPAM FILTERATION ')
w.resizable(0, 0)

# Making gradient frame
j = 0
r = 10
for i in range(100):
    c = str(222222 + r)
    Frame(w, width=10, height=500, bg="#" + c).place(x=j, y=0)
    j = j + 10
    r = r + 1

Frame(w, width=250, height=400, bg='white').place(x=50, y=50)

l1 = Label(w, text='Enter Email to Check', bg='white')
l = ('Consolas', 13)
l1.config(font=l)
l1.place(x=80, y=200)

# e1 entry for username entry
e1 = Entry(w, width=20, border=0)
l = ('Consolas', 13)
e1.config(font=l)
e1.place(x=80, y=240)

###lineframe on entry

# Frame(w, width=180, height=2, bg='#141414').place(x=80, y=332)
Frame(w, width=180, height=2, bg='#141414').place(x=80, y=260)



# Command
def train():
    accuracy, BagOfWords, classifier = TrainModel()
    messagebox.showinfo("A C C U R A C Y",accuracy)
    # q = Tk()
    # q.mainloop()
    #print ham or spam
    print("check")

#accuracy
def check():
    accuracy, BagOfWords, classifier = TrainModel()
    email = e1.get()
    print(email)
    result, label = TestEmail(BagOfWords, classifier, email)

    messagebox.showinfo("T E S T  E M A I L", result)
    writeNewEmailToCSV(label, email)


# Button_with hover effect
def btn1(x, y, text, ecolor, lcolor):
    def on_entera(e):
        myButton1['background'] = ecolor  # ffcc66
        myButton1['foreground'] = lcolor  # 000d33

    def on_leavea(e):
        myButton1['background'] = lcolor
        myButton1['foreground'] = ecolor

    myButton1 = Button(w, text=text,
                       width=20,
                       height=2,
                       fg=ecolor,
                       border=0,
                       bg=lcolor,
                       activeforeground=lcolor,
                       activebackground=ecolor,
                       command=train)

    myButton1.bind("<Enter>", on_entera)
    myButton1.bind("<Leave>", on_leavea)

    myButton1.place(x=x, y=y)


btn1(100, 130, 'T R A I N', 'white', '#994422')

# Button_with hover effect
def btn2(x, y, text, ecolor, lcolor):
    def on_entera(e):
        myButton2['background'] = ecolor  # ffcc66
        myButton2['foreground'] = lcolor  # 000d33

    def on_leavea(e):
        myButton2['background'] = lcolor
        myButton2['foreground'] = ecolor

    myButton2 = Button(w, text=text,
                       width=20,
                       height=2,
                       fg=ecolor,
                       border=0,
                       bg=lcolor,
                       activeforeground=lcolor,
                       activebackground=ecolor,
                       command=check)

    myButton2.bind("<Enter>", on_entera)
    myButton2.bind("<Leave>", on_leavea)

    myButton2.place(x=x, y=y)

btn2(100, 300, 'C H E C K', 'white', '#994422')
w.mainloop()