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from transformers import pipeline, set_seed
import gradio as grad, random, re
import os
import sys
gpt2_pipe = pipeline('text-generation', model='Gustavosta/MagicPrompt-Stable-Diffusion', tokenizer='gpt2')
def generate(starting_text):
with open("ideas.txt", "r") as f:
line = f.readlines()
seed = random.randint(100, 1000000)
set_seed(seed)
if starting_text == "":
starting_text: str = line[random.randrange(0, len(line))].replace("\n", "").capitalize()
starting_text: str = re.sub(r"\.", '', starting_text)
response = gpt2_pipe(starting_text, max_length=(len(starting_text) + random.randint(60, 80)), 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)
response_end = "\n".join(response_list)
response_end = re.sub('[^ ]+\.[^ ]+','', response_end)
response_end = response_end.replace("<", "").replace(">", "")
if response_end != "":
return response_end
#with grad.Blocks(css='css') as demo:
# grad.HTML(
# """
# <div style="text-align: center; max-width: 650px; margin: 0 auto;">
# <div>
# <h5 style="font-weight: 900; font-size: 3rem; margin-bottom:20px;">
# Our Prompt Generator!
# </h5><h5>Input a prompt idea and watch it preform a great prompt</h5>
# </div>
# </p>
# </div>
# """
# )
with grad.Column(elem_id="col-container"):
with grad.Row(variant="compact"):
txt = grad.Textbox(
label="Initial Text",
show_label=False,
max_lines=1,
placeholder="Enter a basic idea",
).style(
container=False,
)
run = grad.Button("✨ Magic Prompt ✨").style(full_width=False)
with grad.Row(variant="compact"):
out = grad.Textbox(
label="Generated Text",
show_label=False,
lines=5,
).style(
container=False,
)
run.click(generate, inputs=[txt], outputs=[out])
with grad.Row():
grad.HTML(
"""
<div class="footer">
<p> Powered by <a href="https://huggingface.co/Gustavosta">Gustavosta</a> Stable Diffusion model
</p>
</div>
"""
)
fn=generate,
run=generate,
inputs=txt,
outputs=out
demo.launch(enable_queue=False, inline=True) |