Upload app.py
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app.py
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from transformers import pipeline, set_seed
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import gradio as grad, random, re
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gpt2_pipe = pipeline('text-generation', model='Gustavosta/MagicPrompt-Stable-Diffusion', tokenizer='gpt2')
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with open("ideas.txt", "r") as f:
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line = f.readlines()
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def generate(starting_text):
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for count in range(4):
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seed = random.randint(100, 1000000)
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set_seed(seed)
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if starting_text == "":
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starting_text: str = line[random.randrange(0, len(line))].replace("\n", "").lower().capitalize()
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starting_text: str = re.sub(r"[,:\-β.!;?_]", '', starting_text)
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print(starting_text)
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response = gpt2_pipe(starting_text, max_length=random.randint(60, 90), num_return_sequences=4)
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response_list = []
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for x in response:
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resp = x['generated_text'].strip()
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if resp != starting_text and len(resp) > (len(starting_text) + 4) and resp.endswith((":", "-", "β")) is False:
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response_list.append(resp+'\n')
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response_end = "\n".join(response_list)
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response_end = re.sub('[^ ]+\.[^ ]+','', response_end)
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response_end = response_end.replace("<", "").replace(">", "")
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if response_end != "":
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return response_end
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if count == 4:
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return response_end
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txt = grad.Textbox(lines=1, label="Initial Text", placeholder="English Text here")
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out = grad.Textbox(lines=4, label="Generated Prompts")
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examples = []
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for x in range(8):
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examples.append(line[random.randrange(0, len(line))].replace("\n", "").lower().capitalize())
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title = "Stable Diffusion Prompt Generator"
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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.<b><br><br>To learn more about the model, go to the link: https://huggingface.co/Gustavosta/MagicPrompt-Stable-Diffusion<br>'
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article = "<div><center><img src='https://visitor-badge.glitch.me/badge?page_id=magicprompt_Stable Diffusion' alt='visitor badge'></center></div>"
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grad.Interface(fn=generate,
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inputs=txt,
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outputs=out,
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examples=examples,
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title=title,
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description=description,
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article=article,
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allow_flagging='never',
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cache_examples=False,
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theme="default").launch(enable_queue=True, debug=True)
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