Create Hoaxie.py

Hoaxie.py

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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)