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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() | |