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import gradio as gr
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')
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