import gradio as gr
import requests
import io
from PIL import Image
import json
from image_processing import downscale_image, limit_colors, convert_to_grayscale, convert_to_black_and_white

class SomeClass:
    def __init__(self):
        self.images = []

with open('loras.json', 'r') as f:
    loras = json.load(f)

def update_selection(selected_state):
    selected_lora_index = selected_state.index
    selected_lora = loras[selected_lora_index]
    new_placeholder = f"Type a prompt for {selected_lora['title']}"
    lora_repo = selected_lora["repo"]
    updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨"
    return (gr.update(placeholder=new_placeholder), updated_text, selected_state)

def run_lora(prompt, selected_state, progress=gr.Progress(track_tqdm=True)):
    selected_lora_index = selected_state.index
    selected_lora = loras[selected_lora_index]
    api_url = f"https://api-inference.huggingface.co/models/{selected_lora['repo']}"
    payload = {"inputs": f"{prompt} {selected_lora['trigger_word']}", "parameters": {"negative_prompt": "bad art, ugly, watermark, deformed"}}
    response = requests.post(api_url, json=payload)
    if response.status_code == 200:
        original_image = Image.open(io.BytesIO(response.content))
        processed = SomeClass()
        processed.images = [original_image]
        refined_image = processed.images[-1]
        return original_image, refined_image

def apply_post_processing(image, downscale, limit_colors, grayscale, black_and_white):
    processed_image = image.copy()
    if downscale > 1:
        processed_image = downscale_image(processed_image, downscale)
    if limit_colors:
        processed_image = limit_colors(processed_image)
    if grayscale:
        processed_image = convert_to_grayscale(processed_image)
    if black_and_white:
        processed_image = convert_to_black_and_white(processed_image)
    return processed_image

with gr.Blocks() as app:
    title = gr.Markdown("# artificialguybr LoRA portfolio")
    description = gr.Markdown("### This is a Pixel Art Generator using SD Loras.")
    selected_state = gr.State()
    with gr.Row():
        gallery = gr.Gallery([(item["image"], item["title"]) for item in loras], label="LoRA Gallery", allow_preview=False, columns=3)
        with gr.Column():
            prompt_title = gr.Markdown("### Click on a LoRA in the gallery to create with it")
            selected_info = gr.Markdown("")
            with gr.Row():
                prompt = gr.Textbox(label="Prompt", show_label=False, lines=1, max_lines=1, placeholder="Type a prompt after selecting a LoRA")
                button = gr.Button("Run")
            result = gr.Image(interactive=False, label="Generated Image")
            refined_result = gr.Image(interactive=False, label="Refined Generated Image")
            post_processed_result = gr.Image(interactive=False, label="Post-Processed Image")
            with gr.Row():
                downscale = gr.Slider(minimum=1, maximum=10, step=1, label="Downscale")
                limit_colors = gr.Checkbox(label="Limit Colors")
                grayscale = gr.Checkbox(label="Grayscale")
                black_and_white = gr.Checkbox(label="Black and White")
            post_process_button = gr.Button("Apply Post-Processing")
    gallery.select(update_selection, outputs=[prompt, selected_info, selected_state])
    prompt.submit(fn=run_lora, inputs=[prompt, selected_state], outputs=[result, refined_result])
    post_process_button.click(fn=apply_post_processing, inputs=[refined_result, downscale, limit_colors, grayscale, black_and_white], outputs=[post_processed_result])

app.queue(max_size=20, concurrency_count=5)
app.launch()