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import streamlit as st
import transformers
import torch
import requests
from PIL import Image
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# Setting the page configurations
st.set_page_config(
page_title="Fake News Detection App",
page_icon="fas fa-exclamation-triangle",
layout="wide",
initial_sidebar_state="auto")
# Load the model and tokenizer
tokenizer_name = AutoTokenizer.from_pretrained("jy46604790/Fake-News-Bert-Detect")
model_name = AutoModelForSequenceClassification.from_pretrained("jy46604790/Fake-News-Bert-Detect")
# Define the CSS style for the app
st.markdown(
"""
<style>
body {
background-color: #f5f5f5;
}
h1 {
color: #4e79a7;
}
</style>
""",
unsafe_allow_html=True
)
# Define the function for detecting fake news
@st.cache_resource
def detect_fake_news(text):
# Load the pipeline.
pipeline = transformers.pipeline("text-classification",
model=model_name,
tokenizer=tokenizer_name)
# Predict the sentiment.
prediction = pipeline(text)
sentiment = prediction[0]["label"]
score = prediction[0]["score"]
return sentiment, score
st.markdown("<h1 style='text-align: center;margin-top:0px;'>A fake news detection app</h1>",
unsafe_allow_html=True)
st.markdown("<h1 style='text-align: center;'>Welcome</h1>",
unsafe_allow_html=True)
st.markdown("<p style='text-align: center;'>This is a Fake News Detection App.</p>",
unsafe_allow_html=True)
# Get user input
text = st.text_input("Enter some text and we'll tell you if it's likely to be fake news or not!")
if st.button('Predict'):
# Show fake news detection output
if text:
with st.spinner('Checking if news is Fake...'):
label, score = detect_fake_news(text)
if label == "FAKE":
st.error(f"The text is likely to be fake news with a confidence score of {score*100:.2f}%!")
else:
st.success(f"The text is likely to be genuine with a confidence score of {score*100:.2f}%!")
else:
with st.spinner('Checking if news is Fake...'):
st.warning("Please enter some text to detect fake news.") |