app.py

import streamlit as st
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import requests
import json


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")


from deep_translator import GoogleTranslator

def translate_to_english(text):
    try:
        return GoogleTranslator(source='auto', target='en').translate(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
    

    # 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:")

       
if __name__ == "__main__":
    main()