Spamdetector.py

import pandas as pd 
import pickle
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
import joblib
from nltk.corpus import stopwords
from nltk.cluster.util import cosine_distance
import numpy as np
from gensim.summarization import summarize
from pprint import pprint as print




def predictSpam(comment):
    df= pd.read_csv("spam data merger.csv")
    df_data = df[["CONTENT","CLASS"]]
    # Features and Labels
    df_x = df_data['CONTENT']
    df_y = df_data.CLASS
    # Extract Feature With CountVectorizer
    corpus = df_x
    cv = CountVectorizer()
    X = cv.fit_transform(corpus) # Fit the Data
    from sklearn.model_selection import train_test_split
    X_train, X_test, y_train, y_test = train_test_split(X, df_y, test_size=0.33, random_state=42)
    #Naive Bayes Classifier
    from sklearn.naive_bayes import MultinomialNB
    clf = MultinomialNB()
    clf.fit(X_train,y_train)
    #clf.score(X_test,y_test)
  
    #read ability
    data = [comment]
    vect = cv.transform(data).toarray()
    my_prediction = clf.predict(vect)
       
    jugde=""
    
    if(my_prediction==0):
             jugde="Spam"
    elif(my_prediction==1):
             jugde="Ham"
       
    return jugde