File size: 4,098 Bytes
b291470 7047b28 b291470 47a2e1b b291470 a1d0b3c 7047b28 a1d0b3c b291470 a1d0b3c b291470 a1d0b3c b291470 a1d0b3c b291470 a1d0b3c 64b7e2b a1d0b3c b291470 a1d0b3c |
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 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 |
import streamlit as st
import transformers
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from predict_function import detect_fake_news
# Load the model and tokenizer
model_name = AutoModelForSequenceClassification.from_pretrained("ikoghoemmanuell/finetuned_fake_news_roberta")
tokenizer_name = AutoTokenizer.from_pretrained("ikoghoemmanuell/finetuned_fake_news_roberta")
# Loading GIF
gif_url = "https://raw.githubusercontent.com/Gilbert-B/Forecasting-Sales/main/app/salesgif.gif"
"https://docs.gato.txst.edu/78660/w/2000/a_1dzGZrL3bG/fake-fact.jpg"
# Set up sidebar
st.sidebar.header('Navigation')
menu = ['Home', 'About']
choice = st.sidebar.selectbox("Select an option", menu)
# 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
# Get user input
text = st.text_input("Enter some text here:")
# Home section
if choice == 'Home':
st.image(gif_url,
use_column_width=True,
width=400)
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)
# Set Page Title
st.title('TRUTH- A fake news detection app')
st.markdown("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)
print(label, score)
if label == "LABEL_1":
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...'):
sales_data = pd.DataFrame({})
try:
st.success(f"")
except ValueError as e:
st.error(str(e))
# 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")
# Define the CSS style for the app
st.markdown(
"""
<style>
body {
background-color: #f5f5f5;
}
h1 {
color: #4e79a7;
}
</style>
""",
unsafe_allow_html=True
)
# About section
elif choice == 'About':
# Load the banner image
banner_image_url = "https://raw.githubusercontent.com/Gilbert-B/Forecasting-Sales/0d7b869515bniVmJZZxhyQ8Fee6m6SCLi64M8Ba72c/app/seer.png"
banner_image = Image.open(requests.get(banner_image_url, stream=True).raw)
# Display the banner image
st.image(banner_image, use_column_width=True)
st.markdown('''
<p style='font-size: 20px; font-style: italic;font-style: bold;'>
TRUTH is a cutting-edge application specifically designed to combat the spread of fake news.
Using state-of-the-art algorithms and advanced deep learning techniques,
our app empowers users to detect and verify the authenticity of news articles.
TRUTH provides accurate assessments of the reliability of news content.
With its user-friendly interface and intuitive design,
the app enables users to easily navigate and obtain trustworthy information in real-time.
With TRUTH, you can take control of the news you consume and make informed decisions based on verified facts.
</p>
''', unsafe_allow_html=True)
|