Spaces:
Sleeping
Sleeping
File size: 2,178 Bytes
8529e9a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
import streamlit as st
import streamlit.components.v1 as com
#import libraries
from transformers import AutoModelForSequenceClassification,AutoTokenizer, AutoConfig
import numpy as np
#convert logits to probabilities
from scipy.special import softmax
#import the model
tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')
model_path = f"penscola/news-d-bert"
config = AutoConfig.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
#Set the page configs
st.set_page_config(page_title='Fake News Detection',page_icon='😎',layout='wide')
#welcome Animation
st.markdown('<h1> Fake News Detection </h1>',unsafe_allow_html=True)
#Create a form to take user inputs
with st.form(key='tweet',clear_on_submit=True):
text=st.text_area('Copy and paste the news or type one')
submit=st.form_submit_button('submit')
#create columns to show outputs
col2,col3=st.columns(2)
col2.title('Fake or Legit')
col3.title('Confidence of this prediction')
if submit:
print('submitted')
#pass text to preprocessor
def preprocess(text):
#initiate an empty list
new_text = []
#split text by space
for t in text.split(" "):
#set username to @user
t = '@user' if t.startswith('@') and len(t) > 1 else t
#set tweet source to http
t = 'http' if t.startswith('http') else t
#store text in the list
new_text.append(t)
#change text from list back to string
return " ".join(new_text)
#pass text to model
#change label id
config.id2label = {0: 'Fake', 1: 'Legit'}
text = preprocess(text)
# PyTorch-based models
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
scores = softmax(scores)
#Process scores
ranking = np.argsort(scores)
ranking = ranking[::-1]
l = config.id2label[ranking[0]]
s = scores[ranking[0]]
#output
if l==1:
col2.write('Legit')
col3.write(f'{s}%')
else:
col2.write('Fake')
col3.write(f'{s}%') |