import json
import random
import gradio as gr
from pathlib import Path
import os
import requests
from PIL import Image
import io
import pathlib


API_TOKEN = os.environ.get("HF_READ_TOKEN")

base_dir = "."
dropdown_options_file = Path(base_dir, "json/dropdown_options.json")
category_data_file = Path(base_dir, "json/category_data.json")
style_data_file = Path(base_dir, "json/style_data.json")
prefix_data_file = Path(base_dir, "json/prefix_data.json")
lightning_data_file = Path(base_dir, "json/lightning_data.json")
lens_data_file = Path(base_dir, "json/lens_data.json")


class Model:
    '''
    Small strut to hold data for the text generator
    '''

    def __init__(self, name) -> None:
        self.name = name
        pass


def populate_dropdown_options():
    path = dropdown_options_file
    with open(path, 'r') as f:
        data = json.load(f)
    category_choices = data["category"]
    style_choices = data["style"]
    lightning_choices = data["lightning"]
    lens_choices = data["lens"]
    return tuple(category_choices), tuple(style_choices), tuple(lightning_choices), tuple(lens_choices),


def add_to_prompt(*args):
    prompt, use_default_negative_prompt, base_prompt, negative_base_prompt = args
    default_negative_prompt = "(worst quality:1.2), (low quality:1.2), (lowres:1.1), (monochrome:1.1), (greyscale), multiple views, comic, sketch, (((bad anatomy))), (((deformed))), (((disfigured))), watermark, multiple_views, mutation hands, mutation fingers, extra fingers, missing fingers, watermark"
    if(use_default_negative_prompt):
        return "{} {}".format(base_prompt ,prompt), default_negative_prompt
    else:
        return "{} {}".format(base_prompt ,prompt), ""


def get_random_prompt(data):
    random_key = random.choice(list(data.keys()))
    random_array = random.choice(data[random_key])
    random_strings = random.sample(random_array, 3)
    return random_strings

def get_correct_prompt(data, selected_dropdown):
    correct_array = data[selected_dropdown]
    random_array = random.choice(correct_array)
    random_strings = random.sample(random_array, 3)
    random_strings.insert(0, selected_dropdown)

    return random_strings

def generate_prompt_output(*args):
    #all imported files
    prefix_path = prefix_data_file
    category_path = category_data_file
    style_path = style_data_file
    lightning_path = lightning_data_file
    lens_path = lens_data_file

    #destructure args
    category, style, lightning, lens, negative_prompt = args

    # Convert variables to lowercase
    category = category.lower()
    style = style.lower()
    lightning = lightning.lower()
    lens = lens.lower()

    # Open category_data.json and grab correct text
    with open(prefix_path, 'r') as f:
        prefix_data = json.load(f)
        prefix_prompt = random.sample(prefix_data, 6)
        modified_prefix_prompt = [f"(({item}))" for item in prefix_prompt]


    # Open category_data.json and grab correct text
    with open(category_path, 'r') as f2:
        category_data = json.load(f2)

    if category == "none":
        category_prompt = ""
    elif category == "random":
        category_prompt = get_random_prompt(category_data)
    else:
        category_prompt = get_correct_prompt(category_data, category)


    # Open style_data.json and grab correct text
    with open(style_path, 'r') as f3:
        style_data = json.load(f3)

    if style == "none":
        style_prompt = ""
    elif style == "random":
        style_prompt = get_random_prompt(style_data)
    else:
        style_prompt = get_correct_prompt(style_data, style)

    # Open lightning_data.json and grab correct text
    with open(lightning_path, 'r') as f4:
        lightning_data = json.load(f4)

    if lightning == "none":
        lightning_prompt = ""
    elif lightning == "random":
        lightning_prompt = get_random_prompt(lightning_data)
    else:
        lightning_prompt = get_correct_prompt(lightning_data, lightning)

    # Open lens_data.json and grab correct text
    with open(lens_path, 'r') as f5:
        lens_data = json.load(f5)

    if lens == "none":
        lens_prompt = ""
    elif lens == "random":
        lens_prompt = get_random_prompt(lens_data)
    else:
        lens_prompt = get_correct_prompt(lens_data, lens)


    prompt_output = modified_prefix_prompt, category_prompt, style_prompt, lightning_prompt, lens_prompt
    prompt_strings = []

    for sublist in prompt_output:
        # Join the sublist elements into a single string
        prompt_string = ", ".join(str(item) for item in sublist)
        if prompt_string:  # Check if the prompt_string is not empty
            prompt_strings.append(prompt_string)

    # Join the non-empty prompt_strings
    final_output = ", ".join(prompt_strings)

    return final_output

list_models = [
    "SDXL-1.0",
    "SD-1.5",
    "OpenJourney-V4",
    "Anything-V4",
    "Disney-Pixar-Cartoon",
    "Pixel-Art-XL",
    "Dalle-3-XL",
    "Midjourney-V4-XL",
]

def generate_txt2img(current_model, prompt, is_negative=False, image_style="None style", steps=50, cfg_scale=7,
                     seed=None, API_TOKEN = API_TOKEN):
    print("call {} {} one time".format(current_model, prompt))
    '''
    import shutil
    im_save_dir = "local_img_dir"
    if not os.path.exists(im_save_dir):
        #shutil.rmtree(im_save_dir)
        os.mkdir(im_save_dir)
    '''

    if current_model == "SD-1.5":
        API_URL = "https://api-inference.huggingface.co/models/runwayml/stable-diffusion-v1-5"
    elif current_model == "SDXL-1.0":
        API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0"
    elif current_model == "OpenJourney-V4":
        API_URL = "https://api-inference.huggingface.co/models/prompthero/openjourney"
    elif current_model == "Anything-V4":
        API_URL = "https://api-inference.huggingface.co/models/xyn-ai/anything-v4.0"
    elif current_model == "Disney-Pixar-Cartoon":
        API_URL = "https://api-inference.huggingface.co/models/stablediffusionapi/disney-pixar-cartoon"
    elif current_model == "Pixel-Art-XL":
        API_URL = "https://api-inference.huggingface.co/models/nerijs/pixel-art-xl"
    elif current_model == "Dalle-3-XL":
        API_URL = "https://api-inference.huggingface.co/models/openskyml/dalle-3-xl"
    elif current_model == "Midjourney-V4-XL":
        API_URL = "https://api-inference.huggingface.co/models/openskyml/midjourney-v4-xl"

    #API_TOKEN = os.environ.get("HF_READ_TOKEN")
    headers = {"Authorization": f"Bearer {API_TOKEN}"}

    if type(prompt) != type(""):
        prompt = DEFAULT_PROMPT

    if image_style == "None style":
        payload = {
            "inputs": prompt + ", 8k",
            "is_negative": is_negative,
            "steps": steps,
            "cfg_scale": cfg_scale,
            "seed": seed if seed is not None else random.randint(-1, 2147483647)
        }
    elif image_style == "Cinematic":
        payload = {
            "inputs": prompt + ", realistic, detailed, textured, skin, hair, eyes, by Alex Huguet, Mike Hill, Ian Spriggs, JaeCheol Park, Marek Denko",
            "is_negative": is_negative + ", abstract, cartoon, stylized",
            "steps": steps,
            "cfg_scale": cfg_scale,
            "seed": seed if seed is not None else random.randint(-1, 2147483647)
        }
    elif image_style == "Digital Art":
        payload = {
            "inputs": prompt + ", faded , vintage , nostalgic , by Jose Villa , Elizabeth Messina , Ryan Brenizer , Jonas Peterson , Jasmine Star",
            "is_negative": is_negative + ", sharp , modern , bright",
            "steps": steps,
            "cfg_scale": cfg_scale,
            "seed": seed if seed is not None else random.randint(-1, 2147483647)
        }
    elif image_style == "Portrait":
        payload = {
            "inputs": prompt + ", soft light, sharp, exposure blend, medium shot, bokeh, (hdr:1.4), high contrast, (cinematic, teal and orange:0.85), (muted colors, dim colors, soothing tones:1.3), low saturation, (hyperdetailed:1.2), (noir:0.4), (natural skin texture, hyperrealism, soft light, sharp:1.2)",
            "is_negative": is_negative,
            "steps": steps,
            "cfg_scale": cfg_scale,
            "seed": seed if seed is not None else random.randint(-1, 2147483647)
        }

    image_bytes = requests.post(API_URL, headers=headers, json=payload).content
    image = Image.open(io.BytesIO(image_bytes))
    '''
    from uuid import uuid1
    path = os.path.join(im_save_dir ,"{}.png".format(uuid1()))
    image.save(path)
    return path
    '''
    return image
    #yield image
    #return [image]

def on_ui_tabs():
    '''
    # UI structure
    txt2img_prompt = modules.ui.txt2img_paste_fields[0][0]
    img2img_prompt = modules.ui.img2img_paste_fields[0][0]
    txt2img_negative_prompt = modules.ui.txt2img_paste_fields[1][0]
    img2img_negative_prompt = modules.ui.img2img_paste_fields[1][0]
    '''

    with gr.Blocks(css = '''
    .header img {
          float: middle;
          width: 33px;
          height: 33px;
        }
        .header h1 {
          top: 18px;
          left: 10px;
        }
    '''
    ) as prompt_generator:
        gr.HTML(
        '''
        <center>
        <div class="header">
        <h1 class = "logo"> <img src="https://huggingface.co/spaces/svjack/Next-Diffusion-Prompt-Generator/resolve/main/images/nextdiffusion_logo.png" alt="logo" /> 🧑‍🎨 Next Diffusion Prompt On Stable Diffuison </h1>
        </center>
        ''')

        with gr.Tab("Prompt Generator"):
            with gr.Row():  # Use Row to arrange two columns side by side
                with gr.Column():  # Left column for dropdowns
                    category_choices, style_choices, lightning_choices, lens_choices = populate_dropdown_options()

                    with gr.Row():
                        gr.HTML('''<h2 id="input_header">Input 👇</h2>''')
                    with gr.Row():
                        # Create a dropdown to select
                        with gr.Row():
                            txt2img_prompt = gr.Textbox(label = "txt2img_prompt", interactive = True)
                            txt2img_negative_prompt = gr.Textbox(label = "txt2img_negative_prompt", interactive = True)
                        '''
                        with gr.Row():
                            img2img_prompt = gr.Textbox(label = "img2img_prompt", interactive = True)
                            img2img_negative_prompt = gr.Textbox(label = "img2img_negative_prompt", interactive = True)
                        '''
                    with gr.Row():
                        current_model = gr.Dropdown(label="Current Model", choices=list_models, value=list_models[1])
                        text_button = gr.Button("Generate image by Stable Diffusion")
                    with gr.Row():
                        image_output = gr.Image(label="Output Image", type = "filepath", elem_id="gallery", height = 512,
                            show_share_button = True
                        )
                        #image_gallery = gr.Gallery(height = 512, label = "Output Gallery")
                        #image_file = gr.File(label="Output Image File")

                with gr.Column():  # Right column for result_textbox and generate_button
                    # Add a Textbox to display the generated text
                    with gr.Row():
                        gr.HTML('''<h2 id="output_header">Prompt Extender by Rule 👋 (aid Input 👈)</h2>''')
                    with gr.Row().style(equal_height=True):  # Place dropdowns side by side
                        category_dropdown = gr.Dropdown(
                            choices=category_choices,
                            value=category_choices[1],
                            label="Category", show_label=True
                        )

                        style_dropdown = gr.Dropdown(
                            choices=style_choices,
                            value=style_choices[1],
                            label="Style", show_label=True
                        )
                    with gr.Row():
                        lightning_dropdown = gr.Dropdown(
                            choices=lightning_choices,
                            value=lightning_choices[1],
                            label="Lightning", show_label=True
                        )

                        lens_dropdown = gr.Dropdown(
                            choices=lens_choices,
                            value=lens_choices[1],
                            label="Lens", show_label=True
                        )
                    result_textbox = gr.Textbox(label="Generated Prompt", lines=3)
                    use_default_negative_prompt = gr.Checkbox(label="Include Negative Prompt", value=True, interactive=True, elem_id="negative_prompt_checkbox")
                    setattr(use_default_negative_prompt,"do_not_save_to_config",True)
                    with gr.Row():
                        generate_button = gr.Button(value="Generate", elem_id="generate_button")
                        clear_button = gr.Button(value="Clear")
                    with gr.Row():
                        txt2img = gr.Button("Send to txt2img")
                        #img2img = gr.Button("Send to img2img")
                    with gr.Row():
                        gr.HTML('''
                        <hr class="rounded" id="divider">
                    ''')
                    with gr.Row():
                        gr.HTML('''<h2 id="input_header">Links</h2>''')
                    with gr.Row():
                        gr.HTML('''
                        <h3>Stable Diffusion Tutorials⚡</h3>
                        <container>
                            <a href="https://nextdiffusion.ai" target="_blank">
                                <button id="website_button" class="external-link">Website</button>
                            </a>
                            <a href="https://www.youtube.com/channel/UCd9UIUkLnjE-Fj-CGFdU74Q?sub_confirmation=1" target="_blank">
                                <button id="youtube_button" class="external-link">YouTube</button>
                            </a>
                        </container>
                    ''')

                    '''
                    with gr.Accordion("Advanced settings", open=True):
                        negative_prompt = gr.Textbox(label="Negative Prompt", value="text, blurry, fuzziness", lines=1, elem_id="negative-prompt-text-input")
                        image_style = gr.Dropdown(label="Style", choices=["None style", "Cinematic", "Digital Art", "Portrait"], value="None style", allow_custom_value=False)    with gr.Row():
                    '''

                    # Create a button to trigger text generation
                    txt2img.click(add_to_prompt, inputs=[result_textbox, use_default_negative_prompt, txt2img_prompt, txt2img_negative_prompt], outputs=[txt2img_prompt, txt2img_negative_prompt ])
                    #img2img.click(add_to_prompt, inputs=[result_textbox, use_default_negative_prompt, img2img_prompt, img2img_negative_prompt], outputs=[img2img_prompt, img2img_negative_prompt])

                    clear_button.click(lambda x: [""] * 3 + ["Random", "Random", "Random", "Random"], None,
                    [result_textbox, txt2img_prompt, txt2img_negative_prompt,
                    category_dropdown, style_dropdown, lightning_dropdown, lens_dropdown
                    ])

                    text_button.click(generate_txt2img, inputs=[current_model, txt2img_prompt, txt2img_negative_prompt], outputs=image_output,

                    )

        # Register the callback for the Generate button
        generate_button.click(fn=generate_prompt_output, inputs=[category_dropdown, style_dropdown, lightning_dropdown, lens_dropdown, use_default_negative_prompt], outputs=[result_textbox])

        gr.Examples(
            [
            #["A lovely cat", "low quality, blur", "OpenJourney-V4", "Anime", "Drawing", "Bloom light", "F/14"],
            ["Forest house", "low quality, blur", "SD-1.5", "None", "Photograph", "Beautifully lit", "800mm lens"],
            ["A girl in pink", "low quality, blur", "SD-1.5", "Anime", "3D style", "None", "Random"],
            ],
            inputs = [txt2img_prompt, txt2img_negative_prompt, current_model, category_dropdown, style_dropdown, lightning_dropdown, lens_dropdown]
        )

    return prompt_generator


with on_ui_tabs() as demo:
    pass

demo.launch(show_api = False)