app.py

import torch
from transformers import (
    AutoTokenizer,
    AutoModelForSequenceClassification
)
import gradio as gr


# ---------------------------------------------------------
# 1. Load DeBERTa text model
# ---------------------------------------------------------
text_model_path = "./DeBERTa"

tokenizer = AutoTokenizer.from_pretrained(text_model_path)
text_model = AutoModelForSequenceClassification.from_pretrained(text_model_path)
text_model.eval()


# ---------------------------------------------------------
# 2. Prediction function (text only)
# ---------------------------------------------------------

def predict_text(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)

    with torch.no_grad():
        outputs = text_model(**inputs)
        probs = torch.softmax(outputs.logits, dim=1).squeeze().tolist()

    return {
        "Real News": float(probs[0]),
        "Fake News": float(probs[1])
    }


# ---------------------------------------------------------
# 3. Gradio UI (single tab)
# ---------------------------------------------------------

app = gr.Interface(
    fn=predict_text,
    inputs=gr.Textbox(lines=4, placeholder="Enter news article or headline..."),
    outputs=gr.Label(num_top_classes=2),
    title="Text Fake News Detector",
)


# ---------------------------------------------------------
# 4. Launch
# ---------------------------------------------------------

if __name__ == "__main__":
    app.launch()