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import streamlit as st
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import requests
import json
from googletrans import Translator
st.set_page_config(page_title="News Prediction", page_icon=":earth_africa:")
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("hamzab/roberta-fake-news-classification")
model = AutoModelForSequenceClassification.from_pretrained("hamzab/roberta-fake-news-classification")
translator = Translator()
def translate_to_english(text):
try:
translated = translator.translate(text, dest='en')
return translated.text
except Exception as e:
return f"Error in translation: {e}"
def predict_fake(title, text):
input_str = "<title>" + title + "<content>" + text + "<end>"
input_ids = tokenizer.encode_plus(input_str, max_length=512, padding="max_length", truncation=True, return_tensors="pt")
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model.to(device)
with torch.no_grad():
output = model(input_ids["input_ids"].to(device), attention_mask=input_ids["attention_mask"].to(device))
return dict(zip(["Fake", "Real"], [x.item() for x in list(torch.nn.Softmax()(output.logits)[0])]))
def fact_check_with_google(api_key, query):
url = f"https://factchecktools.googleapis.com/v1alpha1/claims:search"
params = {
"query": query,
"key": api_key
}
response = requests.get(url, params=params)
if response.status_code == 200:
return response.json()
else:
return {"error": f"Unable to fetch results from Google Fact Check API. HTTP {response.status_code}: {response.text}"}
def main():
st.title("Fake News Prediction")
# Load Google API key from a secure location or environment variable
try:
google_api_key = "AIzaSyAf5v5380xkpo0Rk3kBiSxpxYVBQwcDi2A"
except KeyError:
st.error("Google Fact Check API key is missing. Please add it to secrets.")
return
# Create the form for user input
with st.form("news_form"):
st.subheader("Enter News Details")
title = st.text_input("Title")
text = st.text_area("Text")
language = st.selectbox("Select Language", options=["English", "Other"])
submit_button = st.form_submit_button("Submit")
# Process form submission and make prediction
if submit_button:
if language == "Other":
title = translate_to_english(title)
text = translate_to_english(text)
prediction = predict_fake(title, text)
st.subheader("Prediction:")
st.write("Prediction: ", prediction)
if prediction.get("Real") > 0.5:
st.write("This news is predicted to be **real** :muscle:")
else:
st.write("This news is predicted to be **fake** :shit:")
# Fact-check the news using Google Fact Check API
st.subheader("Fact-Checking Results:")
query = title if title else text[:100] # Use title or first 100 chars of text as query
fact_check_results = fact_check_with_google(google_api_key, query)
if "error" in fact_check_results:
st.error(fact_check_results["error"])
else:
claims = fact_check_results.get("claims", [])
if claims:
for claim in claims:
st.write(f"**Claim:** {claim.get('text', 'N/A')}")
claim_review = claim.get("claimReview", [])
if claim_review:
for review in claim_review:
st.write(f"- **Publisher:** {review.get('publisher', {}).get('name', 'N/A')}")
st.write(f" **Rating:** {review.get('textualRating', 'N/A')}")
st.write(f" **URL:** [More Info]({review.get('url', '#')})")
else:
st.write("No fact-checking information found for this query.")
if __name__ == "__main__":
main()
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