import gradio as gr demo = gr.load("Helsinki-NLP/opus-mt-en-es", src="models") demo.launch() import nltk import string from transformers import GPT2LMHeadModel, GPT2Tokenizer, GenerationConfig, set_seed import random nltk.download('punkt') response_length = 200 sentence_detector = nltk.data.load('tokenizers/punkt/english.pickle') tokenizer = GPT2Tokenizer.from_pretrained("gpt2-medium") tokenizer.truncation_side = 'right' # model = GPT2LMHeadModel.from_pretrained('checkpoint-50000') model = GPT2LMHeadModel.from_pretrained('coffeeee/nsfw-story-generator2') generation_config = GenerationConfig.from_pretrained('gpt2-medium') generation_config.max_new_tokens = response_length generation_config.pad_token_id = generation_config.eos_token_id def generate_response(outputs, new_prompt): story_so_far = "\n".join(outputs[:int(1024 / response_length + 1)]) if outputs else "" set_seed(random.randint(0, 4000000000)) inputs = tokenizer.encode(story_so_far + "\n" + new_prompt if story_so_far else new_prompt, return_tensors='pt', truncation=True, max_length=1024 - response_length) output = model.generate(inputs, do_sample=True, generation_config=generation_config) response = clean_paragraph(tokenizer.batch_decode(output)[0][(len(story_so_far) + 1 if story_so_far else 0):]) outputs.append(response) return { user_outputs: outputs, story: (story_so_far + "\n" if story_so_far else "") + response, prompt: None } def undo(outputs): outputs = outputs[:-1] if outputs else [] return { user_outputs: outputs, story: "\n".join(outputs) if outputs else None } def clean_paragraph(entry): paragraphs = entry.split('\n') for i in range(len(paragraphs)): split_sentences = nltk.tokenize.sent_tokenize(paragraphs[i], language='english') if i == len(paragraphs) - 1 and split_sentences[:1][-1] not in string.punctuation: paragraphs[i] = " ".join(split_sentences[:-1]) return capitalize_first_char("\n".join(paragraphs)) def reset(): return { user_outputs: [], story: None } def capitalize_first_char(entry): for i in range(len(entry)): if entry[i].isalpha(): return entry[:i] + entry[i].upper() + entry[i + 1:] return entry with gr.Blocks(theme=gr.themes.Default(text_size='lg', font=[gr.themes.GoogleFont("Bitter"), "Arial", "sans-serif"])) as demo: placeholder_text = ''' Disclaimer: everything this model generates is a work of fiction. Content from this model WILL generate inappropriate and potentially offensive content. Use at your own discretion. Please respect the Huggingface code of conduct.''' story = gr.Textbox(label="Story", interactive=False, lines=20, placeholder=placeholder_text) story.style(show_copy_button=True) user_outputs = gr.State([]) prompt = gr.Textbox(label="Prompt", placeholder="Start a new story, or continue your current one!", lines=3, max_lines=3) with gr.Row(): gen_button = gr.Button('Generate') undo_button = gr.Button("Undo") res_button = gr.Button("Reset") prompt.submit(generate_response, [user_outputs, prompt], [user_outputs, story, prompt], scroll_to_output=True) gen_button.click(generate_response, [user_outputs, prompt], [user_outputs, story, prompt], scroll_to_output=True) undo_button.click(undo, user_outputs, [user_outputs, story], scroll_to_output=True) res_button.click(reset, [], [user_outputs, story], scroll_to_output=True) # for local server; comment out for deploy demo.launch(inbrowser=True, server_name='0.0.0.0')