app.py

import gradio as gr
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("VietTung04/results")
model = AutoModelForSequenceClassification.from_pretrained("VietTung04/results")

import torch
torch.manual_seed(42)
def is_paraphrased(sentence1, sentence2):
    encoding = tokenizer(
        sentence1,
        sentence2,
        truncation=True,
        max_length=128,
        return_tensors='pt'
    )

    outputs = model(**encoding)
    logits = outputs.logits
    sigmoid = torch.nn.Sigmoid()
    probs = sigmoid(logits.squeeze().cpu())
    label = torch.argmax(probs)
    return [{
        'label': 'Paraphrased' if label.item() == 1 else 'Not paraphrased',
        'score': probs[label].item()
    }]

iface = gr.Interface(fn=is_paraphrased, inputs=["text", "text"], outputs=['json'], title='Paraphrase Identification')
iface.launch(inline=False)