import gradio as gr
from random import randint
from all_models import models

from externalmod import gr_Interface_load

import asyncio
import os
from threading import RLock
lock = RLock()
HF_TOKEN = os.environ.get("HF_TOKEN") if os.environ.get("HF_TOKEN") else None # If private or gated models aren't used, ENV setting is unnecessary.


def load_fn(models):
    global models_load
    models_load = {}
    
    for model in models:
        if model not in models_load.keys():
            try:
                m = gr_Interface_load(f'models/{model}', hf_token=HF_TOKEN)
            except Exception as error:
                print(error)
                m = gr.Interface(lambda: None, ['text'], ['image'])
            models_load.update({model: m})

load_fn(models)


num_models = 1
default_models = models[:num_models]
inference_timeout = 600

MAX_SEED=3999999999



def extend_choices(choices):
    return choices + (num_models - len(choices)) * ['NA']


def update_imgbox(choices):
    choices_plus = extend_choices(choices)
    return [gr.Image(None, label = m, visible = (m != 'NA')) for m in choices_plus]

def gen_fn(model_str, prompt):
    if model_str == 'NA':
        return None
    noise = str('') #str(randint(0, 99999999999))
    return models_load[model_str](f'{prompt} {noise}')









async def infer(model_str, prompt, seed=1, timeout=inference_timeout):
    from pathlib import Path
    kwargs = {}
    noise = ""
    kwargs["seed"] = seed
    task = asyncio.create_task(asyncio.to_thread(models_load[model_str].fn,
                               prompt=f'{prompt} {noise}', **kwargs, token=HF_TOKEN))
    await asyncio.sleep(0)
    try:
        result = await asyncio.wait_for(task, timeout=timeout)
    except (Exception, asyncio.TimeoutError) as e:
        print(e)
        print(f"Task timed out: {model_str}")
        if not task.done(): task.cancel()
        result = None
    if task.done() and result is not None:
        with lock:
            png_path = "image.png"
            result.save(png_path)
            image = str(Path(png_path).resolve())
        return image
    return None

def gen_fnseed(model_str, prompt, seed=1):
    if model_str == 'NA':
        return None
    try:
        loop = asyncio.new_event_loop()
        result = loop.run_until_complete(infer(model_str, prompt, seed, inference_timeout))
    except (Exception, asyncio.CancelledError) as e:
        print(e)
        print(f"Task aborted: {model_str}")
        result = None
    finally:
        loop.close()
    return result

def gen_fnsix(model_str, prompt):
    if model_str == 'NA':
        return None
    noisesix = str(randint(1941, 2023)) #str(randint(0, 99999999999))
    return models_load[model_str](f'{prompt} {noisesix}')

with gr.Blocks() as demo:
    gr.HTML(
    """
        <div>
        <p> <center><img src="https://huggingface.co/Yntec/OpenGenDiffusers/resolve/main/pp.png" style="height:128px; width:482px; margin-top: -22px; margin-bottom: -44px;" span title="Free ai art image generator Printing Press"></center>
        </p>
    """
)
    gr.HTML(
    """
        <div>
        <p> <center>For negative prompts, Width and Height, and other features visit John6666's <a href="https://huggingface.co/spaces/John6666/PrintingPress4">Printing Press 4</a>!</center>
        </p></div>
    """
)  
    with gr.Tab('Up To Six'):
        model_choice2 = gr.Dropdown(models, label = f'Choose a model from the {len(models)} available! Try clearing the box and typing on it to filter them!', value = models[0], filterable = True)
        txt_input2 = gr.Textbox(label = 'Your prompt:')
        
        max_images = 6
        num_images = gr.Slider(1, max_images, value = max_images, step = 1, label = 'Number of images (if you want less than 6 decrease them slowly until they match the boxes below)')
        width_images = gr.Slider(128, 1440, value = 1024, step = 32, label = 'Image width')
        height_images = gr.Slider(128, 1440, value = 1024, step = 32, label = 'Image height')
        
        gen_button2 = gr.Button('Generate up to 6 images in up to 3 minutes total')
        #stop_button2 = gr.Button('Stop', variant = 'secondary', interactive = False)
        gen_button2.click(lambda s: gr.update(interactive = True), None)
        gr.HTML(
        """
            <div style="text-align: center; max-width: 1200px; margin: 0 auto;">
              <div>
                <body>
                <div class="center"><p style="margin-bottom: 10px; color: #000000;">Scroll down to see more images (they generate in a random order).</p>
                </div>
                </body>
              </div>
            </div>
        """
               )
        with gr.Column():
            output2 = [gr.Image(label = '') for _ in range(max_images)]

        for i, o in enumerate(output2):
            img_i = gr.Number(i, visible = False)
            num_images.change(lambda i, n: gr.update(visible = (i < n)), [img_i, num_images], o, show_progress = False)
            gen_event2 = gen_button2.click(lambda i, n, m, t: gen_fnsix(m, t) if (i < n) else None, [img_i, num_images, model_choice2, txt_input2], o, concurrency_limit=None, queue=False)
            #stop_button2.click(lambda s: gr.update(interactive = False), None, stop_button2, cancels = [gen_event2])
        with gr.Row():
            gr.HTML(
    """
        <div class="footer">
        <p> Based on the <a href="https://huggingface.co/spaces/derwahnsinn/TestGen">TestGen</a> Space by derwahnsinn, the <a href="https://huggingface.co/spaces/RdnUser77/SpacIO_v1">SpacIO</a> Space by RdnUser77, Omnibus's Maximum Multiplier and <a href="https://huggingface.co/spaces/Yntec/ToyWorld">Toy World</a>!
        </p>
    """
)

demo.queue(default_concurrency_limit=200, max_size=200)
demo.launch(show_api=False, max_threads=400)