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,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')