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Added fake news detection project
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‎MachineLearning Projects/Fakenews/.DS_Store

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‎MachineLearning Projects/Fakenews/Fakenews.ipynb

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‎MachineLearning Projects/Fakenews/app.py

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import numpy as np
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from flask import Flask, request,render_template
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from flask_cors import CORS
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import joblib
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import pickle
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import flask
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import os
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import newspaper
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from newspaper import Article
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import urllib.request
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import nltk
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nltk.download('punkt')
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#Loading Flask and assigning the model variable
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app = Flask(__name__)
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CORS(app)
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app=flask.Flask(__name__,template_folder='templates')
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with open('model.pickle', 'rb') as handle:
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model = pickle.load(handle)
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@app.route('/')
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def main():
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return render_template('index.html')
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#Receiving the input url from the user and using Web Scrapping to extract the news content
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@app.route('/predict',methods=['GET','POST'])
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def predict():
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url =request.get_data(as_text=True)[5:]
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url = urllib.parse.unquote(url)
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article = Article(str(url))
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article.download()
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article.parse()
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article.nlp()
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news = article.summary
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#Passing the news article to the model and returing whether it is Fake or Real
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pred = model.predict([news])
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return render_template('index.html', prediction_text='The news is "{}"'.format(pred[0]))
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if __name__=="__main__":
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port=int(os.environ.get('PORT',5000))
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app.run(port=port,debug=True,use_reloader=False)
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‎MachineLearning Projects/Fakenews/fake_news_detection.py

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#Importing the libraries
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import pandas as pd
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import numpy as np
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from sklearn.metrics import classification_report, confusion_matrix
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from sklearn.pipeline import Pipeline
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from sklearn.model_selection import train_test_split
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.feature_extraction.text import TfidfVectorizer
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import pickle
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#Importing the cleaned file containing the text and label
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news = pd.read_csv('news.csv')
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X = news['text']
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y = news['label']
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#Splitting the data into train
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
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#Creating a pipeline that first creates bag of words(after applying stopwords) & then applies Multinomial Naive Bayes model
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pipeline = Pipeline([('tfidf', TfidfVectorizer(stop_words='english')),
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('nbmodel', MultinomialNB())])
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#Training our data
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pipeline.fit(X_train, y_train)
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#Predicting the label for the test data
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pred = pipeline.predict(X_test)
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#Checking the performance of our model
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print(classification_report(y_test, pred))
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print(confusion_matrix(y_test, pred))
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#Serialising the file
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with open('model.pickle', 'wb') as handle:
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pickle.dump(pipeline, handle, protocol=pickle.HIGHEST_PROTOCOL)
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‎MachineLearning Projects/Fakenews/images/MAIN UI.png

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‎MachineLearning Projects/Fakenews/images/OUTPUT.png

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‎MachineLearning Projects/Fakenews/model.pickle

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‎MachineLearning Projects/Fakenews/news.csv

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‎MachineLearning Projects/Fakenews/static/.DS_Store

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‎MachineLearning Projects/Fakenews/static/css/style.css

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@import url(https://fonts.googleapis.com/css?family=Open+Sans);
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.btn { display: inline-block; *display: inline; *zoom: 1; padding: 4px 10px 4px; margin-bottom: 0;
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font-size: 13px; line-height: 18px; color: #A52A2A; text-align: center;text-shadow: 0 1px 1px
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rgba(255, 255, 255, 0.75); vertical-align: middle; background-color: #f5f5f5; background-image: -
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moz-linear-gradient(top, #ffffff, #e6e6e6); background-image: -ms-linear-gradient(top, #ffffff,
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#e6e6e6); background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ffffff), to(#e6e6e6));
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background-image: -webkit-linear-gradient(top, #ffffff, #e6e6e6); background-image: -o-linear-
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gradient(top, #ffffff, #e6e6e6); background-image: linear-gradient(top, #ffffff, #e6e6e6);
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background-repeat: repeat-x; filter:
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progid:dximagetransform.microsoft.gradient(startColorstr=#ffffff, endColorstr=#e6e6e6,
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GradientType=0); border-color: #e6e6e6 #e6e6e6 #e6e6e6; border-color: rgba(0, 0, 0, 0.1) rgba(0, 0,
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0, 0.1) rgba(0, 0, 0, 0.25); border: 1px solid #e6e6e6; -webkit-border-radius: 4px; -moz-border-
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radius: 4px; border-radius: 4px; -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px
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2px rgba(0, 0, 0, 0.05); -moz-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0,
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0, 0.05); box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); cursor:
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pointer; *margin-left: .3em; }
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.btn:hover, .btn:active, .btn.active, .btn.disabled, .btn[disabled] { background-color: #A52A2A; }
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.btn-large { padding: 9px 14px; font-size: 15px; line-height: normal; -webkit-border-radius: 5px; -
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moz-border-radius: 5px; border-radius: 5px; }
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.btn:hover { color: #A52A2A; text-decoration: none; background-color: #A52A2A; background-
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position: 0 -15px; -webkit-transition: background-position 0.1s linear; -moz-transition: background-
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position 0.1s linear; -ms-transition: background-position 0.1s linear; -o-transition: background-
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position 0.1s linear; transition: background-position 0.1s linear; }
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.btn-primary, .btn-primary:hover { text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); color: #ffffff; }
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.btn-primary.active { color: rgba(255, 255, 255, 0.75); }
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.btn-primary { background-color: #A52A2A; background-image: -moz-linear-gradient(top, #F5F5DC,
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#A52A2A); background-image: -ms-linear-gradient(top, #F5F5DC, #A52A2A); background-image: -
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webkit-gradient(linear, 0 0, 0 100%, from(#F5F5DC), to(#A52A2A)); background-image: -webkit-
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linear-gradient(top, #F5F5DC, #A52A2A); background-image: -o-linear-gradient(top, #F5F5DC,
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#A52A2A); background-image: linear-gradient(top, #F5F5DC, #A52A2A); background-repeat: repeat-
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x; filter: progid:dximagetransform.microsoft.gradient(startColorstr=#F5F5DC, endColorstr=#A52A2A,
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GradientType=0); border: 1px solid #3762bc; text-shadow: 1px 1px 1px rgba(0,0,0,0.4); box-shadow:
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inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.5); }
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.btn-primary:hover, .btn-primary:active, .btn-primary.active, .btn-primary.disabled, .btn-
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primary[disabled] { filter: none; background-color: #a52a2a; }
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.btn-block { width: 100%; display:block; }
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* { -webkit-box-sizing:border-box; -moz-box-sizing:border-box; -ms-box-sizing:border-box; -o-box-
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sizing:border-box; box-sizing:border-box; }
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html { width: 100%; height:100%; overflow:hidden; }
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body {
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width: 100%;
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height:100%;
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font-family: 'Open Sans', sans-serif;
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background: #FFE4B5;
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color: #000000;
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font-size: 18px;
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text-align:center;
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letter-spacing:1.2px;
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background: -webkit-linear-gradient(110deg, #fdcd3b 60%, #ffed4b 60%);
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background: -o-linear-gradient(110deg, #fdcd3b 60%, #ffed4b 60%);
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background: -moz-linear-gradient(110deg, #fdcd3b 60%, #ffed4b 60%);
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background: linear-gradient(110deg, #fdcd3b 60%, #ffed4b 60%);
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filter: progid:DXImageTransform.Microsoft.gradient( startColorstr='#3E1D6D',
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endColorstr='#092756',GradientType=1 ); filter: progid:DXImageTransform.Microsoft.gradient(
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startColorstr='#3E1D6D', endColorstr='#800000',GradientType=1 );
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}
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.login {
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position: absolute;
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top: 60%;
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left: 50%;
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margin: -150px 0 0 -150px;
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width:400px;
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height:400px;
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}
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.login h1 { color: #000000; text-shadow: 0 0 10px rgba(0,0,0,0.3); letter-spacing:1px; text-
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align:center; }
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input {
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width: 100%;
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margin-bottom: 10px;
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background: rgba(0,0,0,0.3);
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border: none;
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outline: none;
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padding: 10px;
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font-size: 25px;
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color: #000000;
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text-shadow: 1px 1px 1px rgba(0,0,0,0.3);
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border: 1px solid rgba(0,0,0,0.3);
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border-radius: 4px;
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box-shadow: inset 0 -5px 45px rgba(100,100,100,0.2), 0 1px 1px rgba(255,255,255,0.2);
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-webkit-transition: box-shadow .5s ease;
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-moz-transition: box-shadow .5s ease;
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-o-transition: box-shadow .5s ease;
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-ms-transition: box-shadow .5s ease;
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transition: box-shadow .5s ease;
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}
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input:focus { box-shadow: inset 0 -5px 45px rgba(100,100,100,0.4), 0 1px 1px rgba(255,255,255,0.2);
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}

‎MachineLearning Projects/Fakenews/templates/.DS_Store

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‎MachineLearning Projects/Fakenews/templates/index.html

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<!DOCTYPE html>
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<html >
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<!--From https://codepen.io/frytyler/pen/EGdtg-->
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<head>
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<meta charset="UTF-8">
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<title>Fake News Detection</title>
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<link href='https://fonts.googleapis.com/css?family=Pacifico' rel='stylesheet' type='text/css'>
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<link href='https://fonts.googleapis.com/css?family=Arimo' rel='stylesheet' type='text/css'>
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<link href='https://fonts.googleapis.com/css?family=Hind:300' rel='stylesheet' type='text/css'>
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<link href='https://fonts.googleapis.com/css?family=Open+Sans+Condensed:300' rel='stylesheet' type='text/css'>
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<link rel="stylesheet" href="{{ url_for('static', filename='css/style.css') }}">
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</head>
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<body>
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<div class="login">
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<h1>Predict Fake News</h1>
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<!-- Main Input For Receiving Query to our ML -->
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<form action="{{ url_for('predict')}}"method="post">
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<input type="text" name="news" placeholder="Enter the news url" required="required" />
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<button type="submit" class="btn btn-primary btn-block btn-large">Predict</button>
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</form>
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<br>
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<br>
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{{ prediction_text }}
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</div>
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</body>
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</html>

‎MachineLearning Projects/Fakenews/templates/main.html

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<!DOCTYPE html>
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<html >
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<!--From https://codepen.io/frytyler/pen/EGdtg-->
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<head>
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<meta charset="UTF-8">
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<title>Fake News Detection</title>
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<link href='https://fonts.googleapis.com/css?family=Pacifico' rel='stylesheet' type='text/css'>
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<link href='https://fonts.googleapis.com/css?family=Arimo' rel='stylesheet' type='text/css'>
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<link href='https://fonts.googleapis.com/css?family=Hind:300' rel='stylesheet' type='text/css'>
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<link href='https://fonts.googleapis.com/css?family=Open+Sans+Condensed:300' rel='stylesheet' type='text/css'>
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<link rel="stylesheet" href="{{ url_for('static', filename='css/style.css') }}">
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</head>
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<body>
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<div class="login">
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<h1>Predict Fake News</h1>
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<!-- Main Input For Receiving Query to our ML -->
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<form action="{{ url_for('predict')}}"method="post">
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<input type="text" name="news" placeholder="Enter the news url" required="required" />
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<button type="submit" class="btn btn-primary btn-block btn-large">Predict</button>
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</form>
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<br>
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<br>
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{{ prediction_text }}
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</div>
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</body>
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</html>

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