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import streamlit as st
import numpy as np
import re
import time
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer
from Scraper import Scrap
st.set_page_config(layout="wide")
model_checkpoint = "Rifky/FND"
label = {0: "valid", 1: "fake"}
@st.cache(show_spinner=False, allow_output_mutation=True)
def load_model():
model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=2)
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, fast=True)
return Trainer(model=model), tokenizer
def sigmoid(x):
return 1 / (1 + np.exp(-x))
input_column, reference_column = st.columns(2)
input_column.write('# Fake News Detection AI')
with st.spinner("Loading Model..."):
model, tokenizer = load_model()
user_input = input_column.text_input("Article url")
submit = input_column.button("submit")
if submit:
last_time = time.time()
with st.spinner("Reading Article..."):
if user_input:
if user_input[:4] == 'http':
text = Scrap(user_input)
else:
text = user_input
if text:
text = re.sub(r'\n', ' ', text)
with st.spinner("Computing..."):
text = text.split()
text_len = len(text)
sequences = []
for i in range(text_len // 512):
sequences.append(" ".join(text[i * 512: (i + 1) * 512]))
sequences.append(" ".join(text[text_len - (text_len % 512) : text_len]))
sequences = [tokenizer(i, max_length=512, truncation=True, padding="max_length") for i in sequences]
predictions = model.predict(sequences)[0]
result = [
np.sum([sigmoid(i[0]) for i in predictions]) / len(predictions),
np.sum([sigmoid(i[1]) for i in predictions]) / len(predictions)
]
print (f'\nresult: {result}')
input_column.markdown(f"<small>Compute Finished in {int(time.time() - last_time)} seconds</small>", unsafe_allow_html=True)
prediction = np.argmax(result, axis=-1)
input_column.success(f"This news is {label[prediction]}.")
st.text(f"{int(result[prediction]*100)}% confidence")
input_column.progress(result[prediction])
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