app.py

from transformers import pipeline, set_seed
import gradio as grad, random, re


gpt2_pipe = pipeline('text-generation', model='Gustavosta/MagicPrompt-Stable-Diffusion', tokenizer='gpt2')
with open("ideas.txt", "r") as f:
    line = f.readlines()


def generate(starting_text):
    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)

    response = gpt2_pipe(starting_text, max_length=(len(starting_text) + random.randint(70, 90)), num_return_sequences=1)
    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


txt = grad.Textbox(lines=1, label="Initial Text", placeholder="English Text here")
out = grad.Textbox(lines=4, label="Generated Prompts")

examples = []
for x in range(8):
    examples.append(line[random.randrange(0, len(line))].replace("\n", "").lower().capitalize())

title = "Stable Diffusion Prompt Generator"
description = 'This is a demo of the model series: "MagicPrompt", in this case, aimed at: "Stable Diffusion". To use it, simply submit your text or click on one of the examples.  You can also just click on the "Submit" button without entering any text at all and it will pull from a list of thousands of ideas. To learn more about the model, [click here](https://huggingface.co/Gustavosta/MagicPrompt-Stable-Diffusion).<br>'

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