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import os
os.system("pip install git+https://github.com/huggingface/transformers")
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 = ""):
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=128, 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.inputs.Textbox(lines=5, label="Input Text"),
outputs=gr.outputs.Textbox(label="Generated Text"),
)
demo.queue()
demo.launch() |