app.py

import gradio as gr
import torch
from transformers import BartForConditionalGeneration, BartTokenizer


model = BartForConditionalGeneration.from_pretrained("hyechanjun/interview-question-remake")
tok = BartTokenizer.from_pretrained("hyechanjun/interview-question-remake")

def genQuestion(context):
    inputs = tok(context, return_tensors="pt")
    output = model.generate(inputs["input_ids"], num_beams=4, max_length=64, min_length=9, num_return_sequences=4, diversity_penalty =1.0, num_beam_groups=2)
    final_output = ''

    for i in range(4):
        final_output +=  [tok.decode(beam, skip_special_tokens=True, clean_up_tokenization_spaces=False) for beam in output][i] + "\n"

    return final_output
    
iface = gr.Interface(fn=genQuestion, inputs="text", outputs="text")
iface.launch()