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

from externalmod import gr_Interface_load, randomize_seed

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 = 6

default_models = models[:num_models]
inference_timeout = 600
MAX_SEED=3999999999
starting_seed = randint(1941, 2024)

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


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

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

with gr.Blocks(theme="Nymbo/Nymbo_Theme") as demo:
    gr.HTML("<h1>My Gradio Interface</h1>")  # Add this line to include the HTML title
    with gr.Tab('Compare-6'): 
        txt_input = gr.Textbox(label='Your prompt:', lines=4)
        gen_button = gr.Button('Generate up to 6 images in up to 3 minutes total')
        with gr.Row():
            seed = gr.Slider(label="Use a seed to replicate the same image later (maximum 3999999999)", minimum=0, maximum=MAX_SEED, step=1, value=starting_seed, scale=3)
            seed_rand = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary", scale=1)    
        seed_rand.click(randomize_seed, None, [seed], queue=False)
        #stop_button = gr.Button('Stop', variant = 'secondary', interactive = False)
               
        gen_button.click(lambda s: gr.update(interactive = True), None)
        
    with gr.Tab("Advanced Settings"):
        with gr.Row():
            # Textbox for specifying elements to exclude from the image
            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="What should not be in the image", value="(deformed, distorted, disfigured), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, misspellings, typos", lines=3, elem_id="negative-prompt-text-input")
        with gr.Row():
            # Slider for selecting the image width
            width = gr.Slider(label="Width", value=1024, minimum=64, maximum=1216, step=32)
            # Slider for selecting the image height
            height = gr.Slider(label="Height", value=1024, minimum=64, maximum=1216, step=32)
        with gr.Row():
            # Slider for setting the number of sampling steps
            steps = gr.Slider(label="Sampling steps", value=35, minimum=1, maximum=100, step=1)
        with gr.Row():
            # Slider for adjusting the CFG scale (guidance scale)
            cfg = gr.Slider(label="CFG Scale", value=7, minimum=1, maximum=20, step=1)
        with gr.Row():
            # Slider for adjusting the transformation strength
            strength = gr.Slider(label="Strength", value=0.7, minimum=0, maximum=1, step=0.001)
        with gr.Row():
            # Slider for setting the seed for reproducibility
            seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1)
        with gr.Row():
            # Radio buttons for selecting the sampling method
            method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])

        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 and select models.</p>
                </div>
                </body>
              </div>
            </div>
        """
               )
        with gr.Row():
            output = [gr.Image(label = m, min_width=480) for m in default_models]
            current_models = [gr.Textbox(m, visible = False) for m in default_models]
                        
            for m, o in zip(current_models, output):
                gen_event = gr.on(triggers=[gen_button.click, txt_input.submit], fn=gen_fnseed,
                            inputs=[m, txt_input, seed], outputs=[o], concurrency_limit=None, queue=False)
                #stop_button.click(lambda s: gr.update(interactive = False), None, stop_button, cancels = [gen_event])
        with gr.Accordion('Model selection'):
            model_choice = gr.CheckboxGroup(models, label = f'Choose up to {int(num_models)} different models from the {len(models)} available!', value=default_models, interactive=True)
            #model_choice = gr.CheckboxGroup(models, label = f'Choose up to {num_models} different models from the 2 available! Untick them to only use one!', value = default_models, multiselect = True, max_choices = num_models, interactive = True, filterable = False)
            model_choice.change(update_imgbox, model_choice, output)
            model_choice.change(extend_choices, model_choice, current_models)
        with gr.Row():
            gr.HTML(
)

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