app.py

import gradio as gr

# Daftar model dan ControlNet
models = ["Model A", "Model B", "Model C"]
vae = ["VAE A", "VAE B", "VAE C"]
controlnet_types = ["Canny", "Depth", "Normal", "Pose"]

# Fungsi placeholder
def load_model(selected_model):
    return f"Model {selected_model} telah dimuat."

def generate_image(prompt, neg_prompt, width, height, scheduler, num_steps, num_images, cfg_scale, seed, model):
    # Logika untuk menghasilkan gambar dari teks menggunakan model
    return [f"Gambar {i+1} untuk prompt '{prompt}' dengan model '{model}'" for i in range(num_images)], {"prompt": prompt, "neg_prompt": neg_prompt}

def process_image(image, prompt, neg_prompt, model):
    # Logika untuk memproses gambar menggunakan model
    return f"Proses gambar dengan prompt '{prompt}' dan model '{model}'"

def controlnet_process(image, controlnet_type, model):
    # Logika untuk memproses gambar menggunakan ControlNet
    return f"Proses gambar dengan ControlNet '{controlnet_type}' dan model '{model}'"

#wd tagger
# Dataset v3 series of models:
SWINV2_MODEL_DSV3_REPO = "SmilingWolf/wd-swinv2-tagger-v3"
CONV_MODEL_DSV3_REPO = "SmilingWolf/wd-convnext-tagger-v3"
VIT_MODEL_DSV3_REPO = "SmilingWolf/wd-vit-tagger-v3"
VIT_LARGE_MODEL_DSV3_REPO = "SmilingWolf/wd-vit-large-tagger-v3"
EVA02_LARGE_MODEL_DSV3_REPO = "SmilingWolf/wd-eva02-large-tagger-v3"

# Dataset v2 series of models:
MOAT_MODEL_DSV2_REPO = "SmilingWolf/wd-v1-4-moat-tagger-v2"
SWIN_MODEL_DSV2_REPO = "SmilingWolf/wd-v1-4-swinv2-tagger-v2"
CONV_MODEL_DSV2_REPO = "SmilingWolf/wd-v1-4-convnext-tagger-v2"
CONV2_MODEL_DSV2_REPO = "SmilingWolf/wd-v1-4-convnextv2-tagger-v2"
VIT_MODEL_DSV2_REPO = "SmilingWolf/wd-v1-4-vit-tagger-v2"

dropdown_list = [
        SWINV2_MODEL_DSV3_REPO,
        CONV_MODEL_DSV3_REPO,
        VIT_MODEL_DSV3_REPO,
        VIT_LARGE_MODEL_DSV3_REPO,
        EVA02_LARGE_MODEL_DSV3_REPO,
        MOAT_MODEL_DSV2_REPO,
        SWIN_MODEL_DSV2_REPO,
        CONV_MODEL_DSV2_REPO,
        CONV2_MODEL_DSV2_REPO,
        VIT_MODEL_DSV2_REPO,
]

with gr.Blocks(css= "style.css") as app:
    # Dropdown untuk memilih model di luar tab dengan lebar kecil
    with gr.Row():
        model_dropdown = gr.Dropdown(choices=models, label="Model", value="Model B", scale=0.3)
        vae_dropdown = gr.Dropdown(choices=vae, label="VAE", value="VAE C", scale=0.3)

    # Prompt dan Neg Prompt
    with gr.Row():
        with gr.Column(scale=1):  # Scale 1 ensures full width
            prompt_input = gr.Textbox(label="Prompt", placeholder="Masukkan prompt teks", lines=2, elem_id="prompt-input")
            neg_prompt_input = gr.Textbox(label="Neg Prompt", placeholder="Masukkan negasi prompt", lines=2, elem_id="neg-prompt-input")
           
        generate_button = gr.Button("Generate", elem_id="generate-button", scale=0.13)

    
    # Tab untuk Text-to-Image
    with gr.Tab("Text-to-Image"):
        
        with gr.Row():
            with gr.Column():
                # Konfigurasi
                scheduler_input = gr.Dropdown(choices=["Euler", "LMS", "DDIM"], label="Sampling method")
                num_steps_input = gr.Slider(minimum=1, maximum=100, step=1, label="Sampling steps", value=20)
                width_input = gr.Slider(minimum=128, maximum=2048, step=128, label="Width", value=512)
                height_input = gr.Slider(minimum=128, maximum=2048, step=128, label="Height", value=512)
                cfg_scale_input = gr.Slider(minimum=1, maximum=20, step=1, label="CFG Scale", value=7)
                seed_input = gr.Number(label="Seed", value=-1)
                batch_size = gr.Slider(minimum=1, maximum=24, step=1, label="Batch size", value=1)
                batch_count = gr.Slider(minimum=1, maximum=24, step=1, label="Batch Count", value=1)
            
            
            with gr.Column():
                # Gallery untuk output gambar
                output_gallery = gr.Gallery(label="Hasil Gambar")
                # Output teks JSON di bawah gallery
                output_text = gr.Textbox(label="Output JSON", placeholder="Hasil dalam format JSON", lines=2)

        def update_images(prompt, neg_prompt, width, height, scheduler, num_steps, num_images, cfg_scale, seed, model):
            # Update fungsi sesuai kebutuhan
            return generate_image(prompt, neg_prompt, width, height, scheduler, num_steps, num_images, cfg_scale, seed, model)
        
        generate_button.click(fn=update_images, inputs=[prompt_input, neg_prompt_input, width_input, height_input, scheduler_input, num_steps_input, batch_size, batch_count, cfg_scale_input, seed_input, model_dropdown, vae_dropdown], outputs=[output_gallery, output_text])

    # Tab untuk Image-to-Image
    with gr.Tab("Image-to-Image"):
        with gr.Row():
            with gr.Column():
                image_input = gr.Image(label="Unggah Gambar")
                prompt_input_i2i = gr.Textbox(label="Prompt", placeholder="Masukkan prompt teks", lines=2)
                neg_prompt_input_i2i = gr.Textbox(label="Neg Prompt", placeholder="Masukkan negasi prompt", lines=2)
                generate_button_i2i = gr.Button("Proses Gambar")
            
            with gr.Column():
                output_image_i2i = gr.Image(label="Hasil Gambar")
        
        def process_image_func(image, prompt, neg_prompt, model):
            # Update fungsi sesuai kebutuhan
            return process_image(image, prompt, neg_prompt, model)
        
        generate_button_i2i.click(fn=process_image_func, inputs=[image_input, prompt_input_i2i, neg_prompt_input_i2i, model_dropdown, vae_dropdown], outputs=output_image_i2i)

    # Tab untuk ControlNet
    with gr.Tab("ControlNet"):
        with gr.Row():
            with gr.Column():
                controlnet_dropdown = gr.Dropdown(choices=controlnet_types, label="Pilih Tipe ControlNet")
                controlnet_image_input = gr.Image(label="Unggah Gambar untuk ControlNet")
                controlnet_button = gr.Button("Proses dengan ControlNet")
            
            with gr.Column():
                controlnet_output_image = gr.Image(label="Hasil ControlNet")
        
        def controlnet_process_func(image, controlnet_type, model):
            # Update fungsi sesuai kebutuhan
            return controlnet_process(image, controlnet_type, model)
        
        controlnet_button.click(fn=controlnet_process_func, inputs=[controlnet_image_input, controlnet_dropdown, model_dropdown, vae_dropdown], outputs=controlnet_output_image)

    
    # Tab untuk Describe
    with gr.Tab("Describe"):
        # Components
        image = gr.Image(type="pil", image_mode="RGBA", label="Input")
        model_repo = gr.Dropdown(dropdown_list, value=SWINV2_MODEL_DSV3_REPO, label="Model")
        general_thresh = gr.Slider(0, 1, step=args.score_slider_step, value=args.score_general_threshold, label="General Tags Threshold", scale=3)
        general_mcut_enabled = gr.Checkbox(value=False, label="Use MCut threshold", scale=1)
        character_thresh = gr.Slider(0, 1, step=args.score_slider_step, value=args.score_character_threshold, label="Character Tags Threshold", scale=3)
        character_mcut_enabled = gr.Checkbox(value=False, label="Use MCut threshold", scale=1)
        clear_button = gr.ClearButton(components=[image, model_repo, general_thresh, general_mcut_enabled, character_thresh, character_mcut_enabled], variant="secondary", size="lg")
        submit_button = gr.Button(value="Submit", variant="primary", size="lg")
        sorted_general_strings = gr.Textbox(label="Output (string)")
        rating = gr.Label(label="Rating")
        character_res = gr.Label(label="Output (characters)")
        general_res = gr.Label(label="Output (tags)")
        # Layout
        with gr.Row():
            with gr.Column(variant="panel"):
                image.render()
                model_repo.render()
                general_thresh.render()
                general_mcut_enabled.render()
                character_thresh.render()
                character_mcut_enabled.render()
                clear_button.render()
                submit_button.render()
            with gr.Column(variant="panel"):
                sorted_general_strings.render()
                rating.render()
                character_res.render()
                general_res.render()
        clear_button.add([sorted_general_strings, rating, character_res, general_res])
        submit_button.click(predictor.predict, inputs=[image, model_repo, general_thresh, general_mcut_enabled, character_thresh, character_mcut_enabled], outputs=[sorted_general_strings, rating, character_res, general_res])

# Jalankan antarmuka
app.launch()