Fake-News-Dectection/app.py at main · VSTARprojects/Fake-News-Dectection · GitHub

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# -*- coding: utf-8 -*-
"""
Created on Wed Sept 26 02:20:31 2022
@authors: Supriya, Akash, Tejaswini, Rukmananda
"""

# -*- coding: utf-8 -*-
"""
Created on Wed Sept 26 02:20:31 2022
@authors: Supriya, Akash, Tejaswini, Rukmananda
"""


from urllib import response
import numpy as np
import pickle
import pandas as pd
import streamlit as st
import requests
import sentence_transformers

from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.metrics.pairwise import cosine_similarity
from collections import defaultdict

class InputTransformer(BaseEstimator, TransformerMixin):
    def __init__(self, vectorizer):
        self.vectorizer = vectorizer
        print("initalized InputTransformer")

    def fit(self, X, y=None):
        print('fit')
        return self

    def transform(self, X, y=None):
        X_ = X.copy()
        print('trasform')
        transformedX = self.vectorizer.transform(X_)
        return transformedX

pickle_in = open("classifier_final.pkl","rb")
classifier=pickle.load(pickle_in)

# from urllib.request import urlopen
# pickle_in_2 = urlopen("https://github.com/2711-bharath/test-upload-large-files-to-gitHub/blob/main/sentence_transformer_model.pkl?raw=true")
pickle_in_2 = open("sentence_transformer_model.pkl","rb")
sentence_transformer = pickle.load(pickle_in_2)

def predict(text):
    prediction=classifier.predict([text])
    print(prediction)
    return prediction

def encode(sentences):
    prediction=sentence_transformer.encode(sentences)
    print(prediction)
    return prediction

def main():
    st.title("Fake News Classifier")
    text = st.text_area("Text", height=200)

    result=""
    if st.button("Predict"):
        result=predict(text)
        temp = requests.get('https://factchecktools.googleapis.com/v1alpha1/claims:search?key=AIzaSyBRuXbKLveIy-0H5LkCaoDLlm8BxFcs44Q&query=' + text)
        temp = temp.json()
        if len(temp) > 0:
            claims_list = []
            for x in temp['claims']:
                sentences = [
                    x['text'],
                    text
                ]
                sentence_embeddings = encode(sentences)
                t = cosine_similarity(
                    [sentence_embeddings[0]],
                    sentence_embeddings[1:]
                )
                print(t)
                if t > 0.44:
                    label_dict = defaultdict(lambda: -1)
                    label_dict['Half True'] = 0
                    label_dict['Mostly True'] = 0
                    label_dict['False'] = 1
                    label_dict['True'] = 0
                    label_dict['Barely True'] = 1
                    label_dict['Pants on Fire'] = 1
                    label_dict['Distorts the Facts'] = 1
                    label_dict['Partly False'] = 1
                    claims_list.append({'claimText': x['text'], 'rating': label_dict[x['claimReview'][0]['textualRating']], 'textualRating': x['claimReview'][0]['textualRating']})
            if len(claims_list) > 0:
                st.subheader('Based on Known Facts')
                for claim in claims_list:
                    result = claim['rating']
                    if int(result) == 1:
                        st.error('"' + claim['claimText'] + '" ' + ' - Fake')
                    elif int(result) == 0:
                        st.success('"' + claim['claimText'] + '" ' + ' ' + ' - Real')
                    else:
                        st.info(claim['claimText'] + '  - ' + claim['textualRating'])
        st.subheader('Based on the words used')
        if int(result) == 1:
            st.error('Our model predicts: The news could be Fake')
        else:
            st.success('Our model predicts: The news could be Real')


if __name__=='__main__':
    main()