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| import gradio as gr # Library for building User Interfaces | |
| import nltk | |
| from sklearn.feature_extraction.text import CountVectorizer | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| from sklearn.preprocessing import StandardScaler | |
| import joblib | |
| from nltk.stem.porter import PorterStemmer | |
| from nltk.corpus import stopwords | |
| import re | |
| import numpy as np | |
| from gensim.models import Word2Vec | |
| nltk.download('stopwords') | |
| # import warnings | |
| # from sklearn.exceptions import InconsistentVersionWarning | |
| # warnings.filterwarnings("ignore", category=InconsistentVersionWarning) | |
| ps = PorterStemmer() | |
| vectorizer = joblib.load('./vectorizer.pkl') | |
| nb_classifier = joblib.load('./nb_classifier.pkl') | |
| tfidf_vectorizer = joblib.load('./tfidf_vectorizer.pkl') | |
| random_forest = joblib.load('./random_forest.pkl') | |
| word2vec_model = Word2Vec.load("./word2vec_model.bin") | |
| svm_classifier = joblib.load('./svm_classifier.pkl') | |
| scaler = joblib.load('./scaler.pkl') | |
| def preprocess_for_bow_and_tfidf(text): | |
| corpus = [] | |
| text = re.sub('[^a-zA-Z0-9$£€¥%]',' ',text) | |
| text = text.lower() | |
| text = text.split() | |
| text = [ps.stem(t) for t in text if t not in stopwords.words('english')] | |
| corpus.append(' '.join(text)) | |
| return corpus | |
| def preprocess_for_word2vec(text): | |
| corpus = [] | |
| text = re.sub('[^a-zA-Z0-9$£€¥%]',' ',text) | |
| text = text.lower() | |
| text = text.split() | |
| text = [ps.stem(t) for t in text if t not in stopwords.words('english')] | |
| corpus.append(text) | |
| return corpus | |
| def average_word_vectors(words, model, vocabulary, num_features): | |
| feature_vector = np.zeros((num_features,), dtype="float64") | |
| nwords = 0. | |
| for word in words: | |
| if word in vocabulary: | |
| nwords = nwords + 1. | |
| feature_vector = np.add(feature_vector, model.wv[word]) | |
| if nwords: | |
| feature_vector = np.divide(feature_vector, nwords) | |
| return feature_vector | |
| def classify(text,choice): | |
| corpus=[text] | |
| if(choice == 1): | |
| corpus = preprocess_for_bow_and_tfidf(text) | |
| features = vectorizer.transform(corpus).toarray() | |
| prediction = nb_classifier.predict(features) | |
| elif(choice == 2): | |
| corpus = preprocess_for_bow_and_tfidf(text) | |
| features = tfidf_vectorizer.transform(corpus).toarray() | |
| prediction = random_forest.predict(features) | |
| elif(choice == 3): | |
| corpus = preprocess_for_word2vec(text) | |
| X = [average_word_vectors(title, word2vec_model, word2vec_model.wv.key_to_index, 100) for title in corpus] | |
| X = np.array(X) | |
| X = scaler.transform(X) | |
| prediction = svm_classifier.predict(X) | |
| if(prediction == 1): | |
| return "Fake News" | |
| else: | |
| return "Not Fake News" | |
| GUI = gr.Interface( | |
| inputs = [ | |
| gr.Textbox(label = "Input" , placeholder = "Enter News Title here"), | |
| gr.Radio( choices = [("Naive Bayes",1) , ("Random Forest",2) , ("Support Vector Machine",3) ] , value = 1 , label = "Model") ], | |
| outputs = ['text'], | |
| fn = classify, | |
| title = "Fake News Detection System" | |
| ) | |
| GUI.launch(debug = True) |