app.py

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, resize_image, DITHER_METHODS, QUANTIZATION_METHODS
import logging

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

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

def update_selection(selected_state: gr.SelectData):
    logging.debug(f"Inside update_selection, selected_state: {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)):
    logging.debug(f"Inside run_lora, selected_state: {selected_state}")
    if not selected_state:
        logging.error("selected_state is None or empty.")
        raise gr.Error("You must select a LoRA before proceeding.")  # Popup error when no LoRA is selected

    selected_lora_index = selected_state.index  # Changed this line
    selected_lora = loras[selected_lora_index]
    api_url = f"https://api-inference.huggingface.co/models/{selected_lora['repo']}"
    trigger_word = selected_lora["trigger_word"]
    token = os.getenv("API_TOKEN")
    payload = {
        "inputs": f"{prompt} {trigger_word}",
        "parameters":{"negative_prompt": "bad art, ugly, watermark, deformed"},
    }
    headers = {"Authorization": f"Bearer {token}"}
    
    # Add a print statement to display the API request
    print(f"API Request: {api_url}")
    #print(f"API Headers: {headers}")
    print(f"API Payload: {payload}")

    error_count = 0
    pbar = tqdm(total=None, desc="Loading model")
    while(True):
        response = requests.post(api_url, json=payload)
        if response.status_code == 200:
            return Image.open(io.BytesIO(response.content))
        elif response.status_code == 503:
            #503 is triggered when the model is doing cold boot. It also gives you a time estimate from when the model is loaded but it is not super precise
            time.sleep(1)
            pbar.update(1)
        elif response.status_code == 500 and error_count < 5:
            print(response.content)
            time.sleep(1)
            error_count += 1
            continue
        else:
            logging.error(f"API Error: {response.status_code}")
            raise gr.Error("API Error: Unable to fetch the image.")  # Raise a Gradio error here

def postprocess(
    image, 
    enabled,
    downscale,
    need_rescale,
    enable_color_limit,
    number_of_colors,
    quantization_method,
    dither_method,
    use_k_means,
    is_grayscale,
    number_of_shades,
    quantization_method_grayscale,
    dither_method_grayscale,
    use_k_means_grayscale,
    is_black_and_white,
    is_inversed_black_and_white,
    black_and_white_threshold,
    use_color_palette,
    palette_image,
    palette_colors,
    dither_method_palette
):
    if not enabled:
        return image

    processed_image = image.copy()

    if downscale > 1:
        processed_image = downscale_image(processed_image, downscale)

    if enable_color_limit:
        processed_image = limit_colors(
            image=processed_image,
            limit=number_of_colors,
            quantize=QUANTIZATION_METHODS[quantization_method],
            dither=DITHER_METHODS[dither_method],
            use_k_means=use_k_means
        )

    if is_grayscale:
        processed_image = convert_to_grayscale(processed_image)
        processed_image = limit_colors(
            image=processed_image,
            limit=number_of_shades,
            quantize=QUANTIZATION_METHODS[quantization_method_grayscale],
            dither=DITHER_METHODS[dither_method_grayscale],
            use_k_means=use_k_means_grayscale
        )

    if is_black_and_white:
        processed_image = convert_to_black_and_white(processed_image, black_and_white_threshold, is_inversed_black_and_white)

    if use_color_palette:
        processed_image = limit_colors(
            image=processed_image,
            palette=palette_image,
            palette_colors=palette_colors,
            dither=DITHER_METHODS[dither_method_palette]
        )

    if need_rescale:
        processed_image = resize_image(processed_image, image.size)

    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=1)
        
        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")
            post_processed_result = gr.Image(interactive=False, label="Post-Processed Image")

    # Accordion moved here, inside the same gr.Blocks context
    with gr.Accordion(label="Pixel art", open=True):
        with gr.Row():
            enabled = gr.Checkbox(label="Enable", value=False)
            downscale = gr.Slider(label="Downscale", minimum=1, maximum=32, step=2, value=8)
            need_rescale = gr.Checkbox(label="Rescale to original size", value=True)
        with gr.Tabs():
            with gr.TabItem("Color"):
                enable_color_limit = gr.Checkbox(label="Enable", value=False)
                palette_size_color = gr.Slider(label="Palette Size", minimum=1, maximum=256, step=1, value=16)
                quantization_methods_color = gr.Radio(choices=["Median Cut", "Maximum Coverage", "Fast Octree"], label="Colors Quantization Method", value="Median Cut")
                dither_methods_color = gr.Radio(choices=["None", "Floyd-Steinberg"], label="Colors Dither Method", value="None")
                k_means_color = gr.Checkbox(label="Enable k-means for color quantization", value=True)
            
            with gr.TabItem("Grayscale"):
                enable_grayscale = gr.Checkbox(label="Enable", value=False)
                palette_size_gray = gr.Slider(label="Palette Size", minimum=1, maximum=256, step=1, value=16)
                quantization_methods_gray = gr.Radio(choices=["Median Cut", "Maximum Coverage", "Fast Octree"], label="Colors Quantization Method", value="Median Cut")
                dither_methods_gray = gr.Radio(choices=["None", "Floyd-Steinberg"], label="Colors Dither Method", value="None")
                k_means_gray = gr.Checkbox(label="Enable k-means for color quantization", value=True)
            
            with gr.TabItem("Black and white"):
                enable_black_and_white = gr.Checkbox(label="Enable", value=False)
                inverse_black_and_white = gr.Checkbox(label="Inverse", value=False)
                threshold_black_and_white = gr.Slider(label="Threshold", minimum=1, maximum=256, step=1, value=128)
            
            with gr.TabItem("Custom color palette"):
                enable_custom_palette = gr.Checkbox(label="Enable", value=False)
                palette_image = gr.Image(label="Color palette image", type="pil")
                palette_size_custom = gr.Slider(label="Palette Size", minimum=1, maximum=256, step=1, value=16)
                dither_methods_custom = gr.Radio(choices=["None", "Floyd-Steinberg"], label="Colors Dither Method", value="None")
                
                post_process_button = gr.Button("Apply Post-Processing")

    # The rest of your code for setting up the app
    gallery.select(update_selection, outputs=[prompt, selected_info, selected_state])
    prompt.submit(fn=run_lora, inputs=[prompt, selected_state], outputs=[result, post_processed_result])
    post_process_button.click(
    fn=postprocess, 
    inputs=[
        post_processed_result, 
        enabled, 
        downscale, 
        need_rescale, 
        enable_color_limit, 
        palette_size_color,  # Updated this
        quantization_methods_color,  # Updated this
        dither_methods_color,  # Updated this
        k_means_color,  # Updated this
        enable_grayscale,  # Updated this
        palette_size_gray,  # Updated this
        quantization_methods_gray,  # Updated this
        dither_methods_gray,  # Updated this
        k_means_gray,  # Updated this
        enable_black_and_white,  # Updated this
        inverse_black_and_white,  # Updated this
        threshold_black_and_white,  # Updated this
        enable_custom_palette,  # Updated this
        palette_image,  # Updated this
        palette_size_custom,  # Updated this
        dither_methods_custom  # Updated this
    ], 
    outputs=[post_processed_result]
)
    
    app.queue(max_size=20, concurrency_count=5)
    app.launch()