import gradio as grad
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

def load_prompter():
  prompter_model = AutoModelForCausalLM.from_pretrained("microsoft/Promptist")
  tokenizer = AutoTokenizer.from_pretrained("gpt2")
  tokenizer.pad_token = tokenizer.eos_token
  tokenizer.padding_side = "left"
  return prompter_model, tokenizer

prompter_model, prompter_tokenizer = load_prompter()

def generate(plain_text):
    input_ids = prompter_tokenizer(plain_text.strip()+" Rephrase:", return_tensors="pt").input_ids
    eos_id = prompter_tokenizer.eos_token_id
    outputs = prompter_model.generate(input_ids, do_sample=False, max_new_tokens=75, num_beams=8, num_return_sequences=8, eos_token_id=eos_id, pad_token_id=eos_id, length_penalty=-1.0)
    output_texts = prompter_tokenizer.batch_decode(outputs, skip_special_tokens=True)
    res = output_texts[0].replace(plain_text+" Rephrase:", "").strip()
    print("[I] Prompter input: %s" % plain_text)
    print("[I] Prompter output: %s \n------------\n" % res)
    return res

txt = grad.Textbox(lines=1, label="Initial Text", placeholder="Input Prompt")
out = grad.Textbox(lines=1, label="Optimized Prompt")

grad.Interface(fn=generate,
               inputs=txt,
               outputs=out,
               title="Promptist",
               allow_flagging='never',
               cache_examples=False,
               theme="default").launch(enable_queue=True, share=True, debug=True)