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!pip3 install numpy
!pip3 install pandas
!pip3 install sklearn
!pip3 install nltk



import numpy as np 
import pandas as pd





import pandas as pd
import numpy as np
import re 
import nltk
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score



nltk.download('stopwords')


print(stopwords.words('english'))


from google.colab import drive
drive.mount('/content/drive')




news_df = pd.read_csv('/content/drive/MyDrive/Mini project/train.csv')



news_df.head()

news_df.shape

news_df.info()


news_df.isna().sum()


news_df = news_df.fillna('')
news_df['article'] = news_df['title'] + news_df['author']
news_df




news_df.drop(columns=['id'], inplace=True)



news_df



news_df["author"].value_counts()



X = news_df.drop(columns='label', axis=1)
Y = news_df['label']

X

Y



p_stemming = PorterStemmer()



def stemming(content):
    stemmed_word = re.sub('[^a-zA-Z]',' ',content)
    stemmed_word = stemmed_word.lower()
    stemmed_word = stemmed_word.split()
    stemmed_word = [p_stemming.stem(word) for word in stemmed_word if not word in stopwords.words('english')]
    stemmed_word = ' '.join(stemmed_word)
    return stemmed_word







news_df['article'] = news_df['article'].apply(stemming)






news_df['article']







X = news_df['article'].values
X



Y = news_df['label'].values
Y





X



vectorizer = TfidfVectorizer()
vectorizer.fit(X)
X = vectorizer.transform(X)







X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, stratify = Y, random_state = 1)




ml_model = LogisticRegression()



ml_model.fit(X_train, Y_train)




X_train_predict = ml_model.predict(X_train)
train_data_accuracy = accuracy_score(X_train_predict, Y_train)
percent_tr_accuracy = train_data_accuracy * 100
print("Accuracy for Train data: ", percent_tr_accuracy)





X_test_predict = ml_model.predict(X_test)
test_data_accuracy = accuracy_score(X_test_predict, Y_test)
percent_test_accuracy = test_data_accuracy * 100
print("Accuracy for Test data: ", percent_test_accuracy)





def Detection(index):
 index = int (index)
 X_new = X_test[index]
 new_predict = ml_model.predict(X_new)
 real_news= "The News is real" if(new_predict[0]==0) else "The News is fake" 
 return(real_news)
 Detection(index)





pip install gradio



import gradio as gr
demo = gr.Interface(fn=Detection, inputs='number', outputs="text")
demo.launch(share=True)