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Browse files- a.py +101 -0
- requirements.txt +6 -0
a.py
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import streamlit as st
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import torch
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import requests
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import json
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from googletrans import Translator
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st.set_page_config(page_title="News Prediction", page_icon=":earth_africa:")
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# Load tokenizer and model
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tokenizer = AutoTokenizer.from_pretrained("hamzab/roberta-fake-news-classification")
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model = AutoModelForSequenceClassification.from_pretrained("hamzab/roberta-fake-news-classification")
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translator = Translator()
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def translate_to_english(text):
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try:
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translated = translator.translate(text, dest='en')
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return translated.text
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except Exception as e:
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return f"Error in translation: {e}"
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def predict_fake(title, text):
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input_str = "<title>" + title + "<content>" + text + "<end>"
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input_ids = tokenizer.encode_plus(input_str, max_length=512, padding="max_length", truncation=True, return_tensors="pt")
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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model.to(device)
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with torch.no_grad():
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output = model(input_ids["input_ids"].to(device), attention_mask=input_ids["attention_mask"].to(device))
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return dict(zip(["Fake", "Real"], [x.item() for x in list(torch.nn.Softmax()(output.logits)[0])]))
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def fact_check_with_google(api_key, query):
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url = f"https://factchecktools.googleapis.com/v1alpha1/claims:search"
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params = {
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"query": query,
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"key": api_key
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}
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response = requests.get(url, params=params)
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if response.status_code == 200:
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return response.json()
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else:
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return {"error": f"Unable to fetch results from Google Fact Check API. HTTP {response.status_code}: {response.text}"}
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def main():
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st.title("Fake News Prediction")
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# Load Google API key from a secure location or environment variable
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try:
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google_api_key = "AIzaSyAf5v5380xkpo0Rk3kBiSxpxYVBQwcDi2A"
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except KeyError:
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st.error("Google Fact Check API key is missing. Please add it to secrets.")
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return
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# Create the form for user input
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with st.form("news_form"):
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st.subheader("Enter News Details")
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title = st.text_input("Title")
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text = st.text_area("Text")
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language = st.selectbox("Select Language", options=["English", "Other"])
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submit_button = st.form_submit_button("Submit")
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# Process form submission and make prediction
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if submit_button:
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if language == "Other":
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title = translate_to_english(title)
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text = translate_to_english(text)
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prediction = predict_fake(title, text)
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st.subheader("Prediction:")
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st.write("Prediction: ", prediction)
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if prediction.get("Real") > 0.5:
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st.write("This news is predicted to be **real** :muscle:")
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else:
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st.write("This news is predicted to be **fake** :shit:")
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# Fact-check the news using Google Fact Check API
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st.subheader("Fact-Checking Results:")
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query = title if title else text[:100] # Use title or first 100 chars of text as query
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fact_check_results = fact_check_with_google(google_api_key, query)
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if "error" in fact_check_results:
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st.error(fact_check_results["error"])
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else:
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claims = fact_check_results.get("claims", [])
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if claims:
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for claim in claims:
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st.write(f"**Claim:** {claim.get('text', 'N/A')}")
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claim_review = claim.get("claimReview", [])
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if claim_review:
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for review in claim_review:
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st.write(f"- **Publisher:** {review.get('publisher', {}).get('name', 'N/A')}")
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st.write(f" **Rating:** {review.get('textualRating', 'N/A')}")
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st.write(f" **URL:** [More Info]({review.get('url', '#')})")
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else:
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st.write("No fact-checking information found for this query.")
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if __name__ == "__main__":
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main()
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requirements.txt
ADDED
@@ -0,0 +1,6 @@
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1 |
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torch
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transformers
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gradio
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googletrans==4.0.0-rc1
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requests
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