app.py

import os
import gradio as gr
import numpy as np
import random
from huggingface_hub import AsyncInferenceClient
from translatepy import Translator
from gradio_client import Client, handle_file
from PIL import Image
from huggingface_hub import login
from themes import IndonesiaTheme  # Import custom IndonesiaTheme

MAX_SEED = np.iinfo(np.int32).max
HF_TOKEN = "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"
HF_TOKEN_UPSCALER = "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"

# Function to enable LoRA if selected
def enable_lora(lora_add, basemodel):
    print(f"[-] Menentukan model: LoRA {'diaktifkan' if lora_add else 'tidak diaktifkan'}, model dasar: {basemodel}")
    return basemodel if not lora_add else lora_add

# Function to generate image
async def generate_image(prompt, model, lora_word, width, height, scales, steps, seed):
    try:
        if seed == -1:
            seed = random.randint(0, MAX_SEED)
        seed = int(seed)

        print(f"[-] Menerjemahkan prompt: {prompt}")
        text = str(Translator().translate(prompt, 'English')) + "," + lora_word
        
        print(f"[-] Generating image with prompt: {text}, model: {model}")
        client = AsyncInferenceClient()
        image = await client.text_to_image(prompt=text, height=height, width=width, guidance_scale=scales, num_inference_steps=steps, model=model)
        return image, seed
    except Exception as e:
        print(f"[-] Error generating image: {e}")
        return None, None

# Function to upscale image
def get_upscale_finegrain(prompt, img_path, upscale_factor):
    try:
        print(f"[-] Memulai proses upscaling dengan faktor {upscale_factor} untuk gambar {img_path}")
        client = Client("finegrain/finegrain-image-enhancer", hf_token=HF_TOKEN_UPSCALER)
        result = client.predict(
            input_image=handle_file(img_path), 
            prompt=prompt, 
            negative_prompt="worst quality, low quality, normal quality",
            upscale_factor=upscale_factor,
            controlnet_scale=0.6,
            controlnet_decay=1,
            condition_scale=6,
            denoise_strength=0.35, 
            num_inference_steps=18,
            solver="DDIM", 
            api_name="/process"
        )
        print(f"[-] Proses upscaling berhasil.")
        return result[1]  # Return upscale image path
    except Exception as e:
        print(f"[-] Error scaling image: {e}")
        return None
        
# Main function to generate images and optionally upscale
async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora):
    print(f"[-] Memulai generasi gambar dengan prompt: {prompt}")
    
    model = enable_lora(lora_model, basemodel) if process_lora else basemodel
    print(f"[-] Menggunakan model: {model}")

    image, seed = await generate_image(prompt, model, "", width, height, scales, steps, seed)
    
    if image is None:
        print("[-] Image generation failed.")
        return []

    image_path = "temp_image.jpg"
    print(f"[-] Menyimpan gambar sementara di: {image_path}")
    image.save(image_path, format="JPEG")

    upscale_image_path = None
    if process_upscale:
        print(f"[-] Memproses upscaling dengan faktor: {upscale_factor}")
        upscale_image_path = get_upscale_finegrain(prompt, image_path, upscale_factor)
        if upscale_image_path is not None and os.path.exists(upscale_image_path):
            print(f"[-] Proses upscaling selesai. Gambar tersimpan di: {upscale_image_path}")
            return [image_path, upscale_image_path]  # Return both images
        else:
            print("[-] Upscaling gagal, jalur gambar upscale tidak ditemukan.")

    return [image_path]

# CSS for styling the interface
css = """
#col-left, #col-mid, #col-right {
    margin: 0 auto;
    max-width: 400px;
    padding: 10px;
    border-radius: 15px;
    background-color: #f9f9f9;
    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
}
#banner {
    width: 100%;
    text-align: center;
    margin-bottom: 20px;
}
#run-button {
    background-color: #ff4b5c;
    color: white;
    font-weight: bold;
    padding: 10px;
    border-radius: 10px;
    cursor: pointer;
    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);
}
#footer {
    text-align: center;
    margin-top: 20px;
    color: silver;
}
"""

# Creating Gradio interface
with gr.Blocks(css=css, theme=IndonesiaTheme()) as WallpaperFluxMaker:
    # Displaying the application title
    gr.HTML('<div id="banner">✨ Flux MultiMode Generator + Upscaler ✨</div>')

    with gr.Column(elem_id="col-container"):
        # Output section (replacing ImageSlider with gr.Gallery)
        with gr.Row():
            output_res = gr.Gallery(label="⚡ Flux / Upscaled Image ⚡", elem_id="output-res", columns=2, height="auto")

        # User input section split into two columns
        with gr.Row():
            # Column 1: Input prompt, LoRA, and base model
            with gr.Column(scale=1, elem_id="col-left"):
                prompt = gr.Textbox(
                    label="📜 Deskripsi Gambar", 
                    placeholder="Tuliskan prompt Anda dalam bahasa apapun, yang akan langsung diterjemahkan ke bahasa Inggris.",
                    elem_id="textbox-prompt"
                )

                basemodel_choice = gr.Dropdown(
                    label="🖼️ Pilih Model", 
                    choices=[
                        "black-forest-labs/FLUX.1-schnell", 
                        "black-forest-labs/FLUX.1-DEV", 
                        "enhanceaiteam/Flux-uncensored", 
                        "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro", 
                        "Shakker-Labs/FLUX.1-dev-LoRA-add-details", 
                        "city96/FLUX.1-dev-gguf"
                    ], 
                    value="black-forest-labs/FLUX.1-schnell"
                )

                lora_model_choice = gr.Dropdown(
                    label="🎨 Pilih LoRA", 
                    choices=[
                        "Shakker-Labs/FLUX.1-dev-LoRA-add-details", 
                        "XLabs-AI/flux-RealismLora", 
                        "enhanceaiteam/Flux-uncensored"
                    ], 
                    value="XLabs-AI/flux-RealismLora"
                )

                process_lora = gr.Checkbox(label="🎨 Aktifkan LoRA")
                process_upscale = gr.Checkbox(label="🔍 Aktifkan Peningkatan Resolusi")
                upscale_factor = gr.Radio(label="🔍 Faktor Peningkatan Resolusi", choices=[2, 4, 8], value=2)

            # Column 2: Advanced options (always open)
            with gr.Column(scale=1, elem_id="col-right"):
                with gr.Accordion(label="⚙️ Opsi Lanjutan", open=True):
                    width = gr.Slider(label="Lebar", minimum=512, maximum=1280, step=8, value=1280)
                    height = gr.Slider(label="Tinggi", minimum=512, maximum=1280, step=8, value=768)
                    scales = gr.Slider(label="Skala", minimum=1, maximum=20, step=1, value=8)
                    steps = gr.Slider(label="Langkah", minimum=1, maximum=100, step=1, value=8)
                    seed = gr.Number(label="Seed", value=-1)

        # Button to generate image
        btn = gr.Button("🚀 Buat Gambar", elem_id="generate-btn")

        # Running the `gen` function when "Generate" button is pressed
        btn.click(fn=gen, inputs=[
            prompt, basemodel_choice, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora
        ], outputs=output_res)

# Launching the Gradio app
WallpaperFluxMaker.queue(api_open=False).launch(show_api=False)