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)
    final_output = ''
    all_beams = []

    for i in range(4):
        all_beams.append([tok.decode(beam, skip_special_tokens=True, clean_up_tokenization_spaces=False) for beam in output][i])

    for i in len(all_beams)
        final_output += all_beams[i] + "\n"
    return final_output
    
iface = gr.Interface(fn=genQuestion, inputs="text", outputs="text")
iface.launch()