app.py

import gradio as gr
### used to evaluate whether the LLM is jailbroken
from transformers import RobertaForSequenceClassification, RobertaTokenizer
import torch

# os.environ["CUDA_VISIBLE_DEVICES"] = "7"
# Load model and tokenizer
print("Loading RoBERTa Checkpoint...")
ckpt_path = 'hubert233/GPTFuzz'
model = RobertaForSequenceClassification.from_pretrained(ckpt_path)
tokenizer = RobertaTokenizer.from_pretrained(ckpt_path)
print("Loading Done!")

def predict(sequence):
    sequences = [sequence]
    # Encoding sequences
    inputs = tokenizer(sequences, padding=True, truncation=True, max_length=512, return_tensors="pt")

    # Compute token embeddings
    with torch.no_grad():
        outputs = model(**inputs)

    # Get predictions
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    # print(predictions.shape)

    # If you want the most likely classes:
    _, predicted_classes = torch.max(predictions, dim=1)

    # print("Predicted probabilities:", predictions)
    # print("Predicted classes:", predicted_classes)

    return predicted_classes[0].item()


iface = gr.Interface(fn=predict, inputs="text", outputs="text")
iface.launch()