# -*- coding: utf-8 -*-
"""epurethim.159

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1UmqJMfDY_e89v6dh4maq9aobuj5vz79k
"""

import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score

dataset = pd.read_csv('/content/emails.csv')

dataset.head()

vectorizer = CountVectorizer()
X = vectorizer.fit_transform(dataset['text'])

X_train, X_test, y_train, y_test = train_test_split(X, dataset['spam'], test_size=0.2)

model = MultinomialNB()
model.fit(X_train, y_train)

yPred = model.predict(X_test)

accuracy = accuracy_score(y_test, yPred)
print(accuracy)

def predictMessage(message):
  messageVector = vectorizer.transform([message])
  prediction = model.predict(messageVector)
  return 'Spam' if prediction[0] == 1 else 'Ham'

userMessage = input('Enter text to predict:')
prediction = predictMessage(userMessage)
print(f'The message is {prediction}')