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import gradio as gr, random, re
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline, set_seed
tokenizer_en_es = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-es-en")
model_en_es = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-es-en")
en_es_translator = pipeline("translation_es_to_en", model = model_en_es, tokenizer = tokenizer_en_es)
gpt2_pipe = pipeline('text-generation', model='Gustavosta/MagicPrompt-Stable-Diffusion', tokenizer='gpt2')
with open("ideas.txt", "r") as f:
line = f.readlines()
def generate(inputs):
resultado = en_es_translator(inputs)
starting_text = resultado[0]['translation_text']
for count in range(4):
seed = random.randint(100, 1000000)
set_seed(seed)
if starting_text == "":
starting_text: str = line[random.randrange(0, len(line))].replace("\n", "").lower().capitalize()
starting_text: str = re.sub(r"[,:\-–.!;?_]", '', starting_text)
print(starting_text)
response = gpt2_pipe(starting_text, max_length=(len(starting_text) + random.randint(60, 90)), num_return_sequences=4)
response_list = []
for x in response:
resp = x['generated_text'].strip()
if resp != starting_text and len(resp) > (len(starting_text) + 4) and resp.endswith((":", "-", "—")) is False:
response_list.append(resp+'\n')
response_end = "\n".join(response_list)
response_end = re.sub('[^ ]+\.[^ ]+','', response_end)
response_end = response_end.replace("<", "").replace(">", "")
if response_end != "":
return response_end
if count == 4:
return response_end
txt = gr.Textbox(lines=1, label="Texto inicial", placeholder="Texto en Español")
out = gr.Textbox(lines=4, label="Sugerencia generada")
title = "Generador de sugerencia para Stable Diffusion (SD)"
description = 'Esta es una demostración de la serie de modelos: "MagicPrompt", en este caso, dirigida a: Stable Diffusion. Para utilizarlo, simplemente envíe su texto.'
article = ""
gr.Interface(fn=generate,
inputs=txt,
outputs=out,
title=title,
description=description,
article=article,
allow_flagging='never',
cache_examples=False,
theme="default").launch(enable_queue=True, debug=True)
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