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

# Create a lock to ensure thread safety when accessing shared resources
lock = RLock()

# Load Hugging Face token from environment variable, if available
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.

# Function to load all models specified in the 'models' list
def load_fn(models):
    global models_load
    models_load = {}

    # Iterate through all models to load them
    for model in models:
        if model not in models_load.keys():
            try:
                print(f"Attempting to load model: {model}")
                m = gr_Interface_load(f'models/{model}', hf_token=HF_TOKEN)
                print(f"Successfully loaded model: {model}")
            except Exception as error:
                print(f"Error loading model {model}: {error}")
                m = gr.Interface(lambda: None, ['text'], ['image'])
            models_load.update({model: m})

print("Loading models...")
load_fn(models)
print("Models loaded successfully.")

num_models = 6

# Set the default models to use for inference
default_models = models[:num_models]
inference_timeout = 600
MAX_SEED = 3999999999
starting_seed = randint(1941, 2024)
print(f"Starting seed: {starting_seed}")

# Extend the choices list to ensure it contains 'num_models' elements
def extend_choices(choices):
    print(f"Extending choices: {choices}")
    extended = choices[:num_models] + (num_models - len(choices[:num_models])) * ['NA']
    print(f"Extended choices: {extended}")
    return extended

# Update the image boxes based on selected models
def update_imgbox(choices):
    print(f"Updating image boxes with choices: {choices}")
    choices_plus = extend_choices(choices[:num_models])
    imgboxes = [gr.Image(None, label=m, visible=(m != 'NA')) for m in choices_plus]
    print(f"Updated image boxes: {imgboxes}")
    return imgboxes

# Asynchronous function to perform inference on a given model
async def infer(model_str, prompt, seed=1, batch_size=1, output_format="PNG", priority="medium", timeout=inference_timeout):
    from pathlib import Path
    kwargs = {}
    noise = ""
    kwargs["seed"] = seed
    kwargs["batch_size"] = batch_size
    kwargs["priority"] = priority
    print(f"Starting inference for model: {model_str} with prompt: '{prompt}' and seed: {seed}, batch_size: {batch_size}, priority: {priority}")
    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)
        print(f"Inference completed for model: {model_str}")
    except (Exception, asyncio.TimeoutError) as e:
        print(f"Error during inference for model {model_str}: {e}")
        if not task.done():
            task.cancel()
            print(f"Task cancelled for model: {model_str}")
        result = None
    if task.done() and result is not None:
        with lock:
            png_path = f"image.{output_format.lower()}"
            result.save(png_path)
            image = str(Path(png_path).resolve())
            print(f"Result saved as image: {image}")
        return image
    print(f"No result for model: {model_str}")
    return None

# Function to generate an image based on the given model, prompt, and seed
def gen_fnseed(model_str, prompt, seed=1, batch_size=1, output_format="PNG", priority="medium"):
    if model_str == 'NA':
        print(f"Model is 'NA', skipping generation.")
        return None
    try:
        print(f"Generating image for model: {model_str} with prompt: '{prompt}', seed: {seed}, batch_size: {batch_size}, priority: {priority}")
        loop = asyncio.new_event_loop()
        result = loop.run_until_complete(
            infer(model_str, prompt, seed, batch_size=batch_size, output_format=output_format, priority=priority)
        )
    except (Exception, asyncio.CancelledError) as e:
        print(f"Error during generation for model {model_str}: {e}")
        result = None
    finally:
        loop.close()
        print(f"Event loop closed for model: {model_str}")
    return result

# Create the Gradio Blocks interface with a custom theme
print("Creating Gradio interface...")
with gr.Blocks(theme="Nymbo/Nymbo_Theme") as demo:
    gr.HTML("<center><h1>Multi-models-prompt-to-image-generation</h1></center>")
    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="Seed (max 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)

        # Add batch size slider
        batch_size_slider = gr.Slider(label="Batch Size", minimum=1, maximum=10, step=1, value=1)
        output_format_dropdown = gr.Dropdown(["PNG", "JPEG"], label="Output Format", value="PNG")
        priority_dropdown = gr.Dropdown(["low", "medium", "high"], label="Model Priority", value="medium")

        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):
                print(f"Setting up generation event for model: {m.value}")
                gen_event = gr.on(
                    triggers=[gen_button.click, txt_input.submit],
                    fn=gen_fnseed,
                    inputs=[m, txt_input, seed, batch_size_slider, output_format_dropdown, priority_dropdown],
                    outputs=[o],
                    concurrency_limit=None,
                    queue=False
                )
        with gr.Accordion('Model selection'):
            model_choice = gr.CheckboxGroup(models, label=f'Choose up to {int(num_models)} different models!', value=default_models, interactive=True)
            model_choice.change(update_imgbox, model_choice, output)
            model_choice.change(extend_choices, model_choice, current_models)
        with gr.Row():
            gr.HTML("<p>Additional UI elements can go here</p>")

print("Setting up queue...")
demo.queue(default_concurrency_limit=200, max_size=200)
print("Launching Gradio interface...")
demo.launch(show_api=False, max_threads=400)
print("Gradio interface launched successfully.")