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# app.py
import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# Load model and tokenizer
model_name = "hamzab/roberta-fake-news-classification"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
# Prediction function
def predict_fake(title, text):
input_str = f"<title>{title}<content>{text}<end>"
inputs = tokenizer.encode_plus(
input_str,
max_length=512,
padding="max_length",
truncation=True,
return_tensors="pt"
)
with torch.no_grad():
outputs = model(
inputs["input_ids"].to(device),
attention_mask=inputs["attention_mask"].to(device)
)
probs = torch.nn.functional.softmax(outputs.logits, dim=1)[0]
return {"Fake": float(probs[0]), "Real": float(probs[1])}
# Gradio interface
iface = gr.Interface(
fn=predict_fake,
inputs=[
gr.Textbox(label="Title"),
gr.Textbox(label="Content", lines=6)
],
outputs=gr.Label(num_top_classes=2),
title="Fake News Detector",
description="Enter a news headline and content to classify as Real or Fake using a RoBERTa model."
)
if __name__ == "__main__":
iface.launch()
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