import os
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread
import torch

tok = AutoTokenizer.from_pretrained("distilgpt2")
model = AutoModelForCausalLM.from_pretrained("distilgpt2")

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = 0 if torch.cuda.is_available()==False else torch.cuda.device_count()
model.to(device)

def generate(text = "", max_new_tokens = 128):  
  streamer = TextIteratorStreamer(tok, timeout=10.)
  if len(text) == 0:
    text = "  "
  inputs = tok([text], return_tensors="pt").to(device)
  generation_kwargs = dict(inputs, streamer=streamer, repetition_penalty=2.0, do_sample=True, top_k=40, top_p=0.97, max_new_tokens=max_new_tokens, pad_token_id = model.config.eos_token_id, early_stopping=True, no_repeat_ngram_size=4)
  thread = Thread(target=model.generate, kwargs=generation_kwargs)
  thread.start()
  generated_text = ""
  for new_text in streamer:
    yield generated_text + new_text    
    generated_text += new_text
    if tok.eos_token in generated_text:
      generated_text = generated_text[: generated_text.find(tok.eos_token) if tok.eos_token else None]
      streamer.end()
      yield generated_text
      return
  return generated_text

demo = gr.Interface(
    title="TextIteratorStreamer + Gradio demo",
    fn=generate,
    inputs=[gr.Textbox(lines=5, label="Input Text"),
            gr.Slider(value=128,minimum=5, maximum=256, step=1, label="Maximum number of new tokens")],
    outputs=gr.Textbox(label="Generated Text"),
    allow_flagging="never"
)

demo.queue()
demo.launch()