app.py

import gradio as gr
import random
import time

# Define dummy loras list for the gallery
loras = [
    {"repo": "stabilityai/stable-diffusion-xl-base-1.0", "image": "https://huggingface.co/spaces/reach-vb/Blazingly-fast-LoRA/resolve/main/flux_lora.png", "title": "SDXL Base 1.0"},
    {"repo": "stabilityai/sdxl-turbo", "image": "https://huggingface.co/stabilityai/sdxl-turbo/resolve/main/banner.png", "title": "SDXL Turbo"},
    {"repo": "runwayml/stable-diffusion-v1-5", "image": "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/sd-v1-5.png", "title": "SD 1.5"},
    {"repo": "SG161222/Realistic_Vision_V5.1_noVAE", "image": "https://huggingface.co/SG161222/Realistic_Vision_V5.1_noVAE/resolve/main/realistic_vision_v5.1.png", "title": "Realistic Vision V5.1"},
    {"repo": "gsdf/Counterfeit-V3.0", "image": "https://huggingface.co/gsdf/Counterfeit-V3.0/resolve/main/cf3.jpg", "title": "Counterfeit V3.0"},
    {"repo": "digiplay/AbsoluteReality_v1.8.1", "image": "https://huggingface.co/digiplay/AbsoluteReality_v1.8.1/resolve/main/ar.jpg", "title": "Absolute Reality v1.8.1"}
]

# Custom CSS
css = """
#title { text-align: center; margin-bottom: 10px; }
#gallery {min-height: 450px; max-height: 650px; overflow-y: auto;}
#gen_column {display: flex; align-items: flex-end; margin-bottom: 0.5rem;}
#gen_btn {margin-bottom: 0.5rem; max-width: 100%;}
#lora_list {font-size: 0.8em; margin-top: 0.5rem;}
#progress {text-align: center; margin-top: 0.8rem;}
"""

font = "Montserrat"

# Dummy function to update selection
def update_selection(evt: gr.SelectData):
    """Update the UI when a model is selected from the gallery"""
    selected_index = evt.index
    selected_lora = loras[selected_index]
    # Get the model name to display
    model_title = selected_lora.get("title", selected_lora["repo"].split('/')[-1])
    
    # Create an informative text about the selected model
    info_text = f"Selected model: **{model_title}**\n\nModel ID: `{selected_lora['repo']}`"
    
    # Return with appropriate placeholder text for the prompt
    return (
        gr.update(placeholder=f"Enter your prompt for {model_title}..."),
        info_text,
        selected_index,
        1024,  # Default width
        1024   # Default height
    )

# Dummy function to add a custom model
def add_custom_lora(lora_id):
    """Add a custom model from a Hugging Face ID or URL"""
    if not lora_id or lora_id.strip() == "":
        return gr.update(), gr.update(visible=False), gr.update(), gr.update(), None, gr.update()
    
    # Create a dummy entry for the custom model
    custom_entry = {
        "repo": lora_id,
        "image": "https://huggingface.co/spaces/reach-vb/Blazingly-fast-LoRA/resolve/main/flux_lora.png",  # Placeholder image
        "title": f"Custom: {lora_id.split('/')[-1]}"
    }
    
    # Add the custom entry to the gallery list
    updated_loras = loras.copy()
    updated_loras.append(custom_entry)
    
    # Create info HTML for the custom model
    info_html = f"""
    <div style="padding: 10px; border: 1px solid #ddd; border-radius: 5px; margin-top: 10px;">
        <p><b>Custom model added:</b> {lora_id}</p>
    </div>
    """
    
    # Create info text for selection
    info_text = f"Using custom model: **{lora_id}**"
    
    # Return with updates
    return (
        gr.update(value=info_html, visible=True),
        gr.update(visible=True),
        gr.update(value=[(item.get("image"), item.get("title", item["repo"].split('/')[-1])) for item in updated_loras]),
        info_text,
        len(updated_loras) - 1,  # Index of the newly added model
        gr.update(placeholder=f"Enter your prompt for custom model {lora_id.split('/')[-1]}...")
    )

# Dummy function to remove custom model info
def remove_custom_lora():
    """Remove custom model information and reset UI"""
    return (
        gr.update(visible=False),
        gr.update(visible=False),
        gr.update(value=[(item.get("image"), item.get("title", item["repo"].split('/')[-1])) for item in loras]),
        "Select a base model or add a custom one below.",
        None,
        gr.update(value="")
    )

# Dummy function to generate images
def run_lora(prompt, selected_index, seed, width, height):
    """Simulate image generation with the selected model"""
    if selected_index is None:
        return gr.update(value=None), seed, gr.update(value="Please select a model first.", visible=True)
    
    if not prompt or prompt.strip() == "":
        return gr.update(value=None), seed, gr.update(value="Please enter a prompt.", visible=True)
    
    # Show progress bar
    progress = gr.update(value="Generating your image...", visible=True)
    
    # Simulate generation delay
    time.sleep(2)
    
    # If seed is 0, randomize it
    if seed == 0:
        seed = random.randint(1, 2147483647)
    
    # Get the selected model info
    model_info = loras[selected_index] if selected_index < len(loras) else {"repo": "custom_model", "title": "Custom Model"}
    
    # For dummy purposes, we'll just return the model's image or a placeholder
    # In a real implementation, you would call an API to generate the image
    result_image = model_info.get("image", "https://huggingface.co/spaces/reach-vb/Blazingly-fast-LoRA/resolve/main/flux_lora.png")
    
    # Hide progress bar
    progress = gr.update(visible=False)
    
    return result_image, seed, progress

# Now you can run the app with this code:
if __name__ == "__main__":
    with gr.Blocks(theme=gr.themes.Soft(font=font), css=css) as app:
        title = gr.HTML(
            """<h1><a href="https://huggingface.co/docs/inference-providers/en/index">Blazingly Fast LoRA by Fal & HF (this is a dummy app)</a> 🤗</h1>""",
            elem_id="title",
        )
        title = gr.HTML(
            """<h3>This is just a dummy app</h3>""",
            elem_id="subtitle",
        )
        # --- States for parameters previously in Advanced Settings ---
        selected_index = gr.State(None)
        width = gr.State(1024) # Default width
        height = gr.State(1024) # Default height
        seed = gr.State(0) # Default seed (will be randomized by run_lora)

        with gr.Row():
            with gr.Column(scale=3):
                prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Type a prompt after selecting a LoRA/Model")
            with gr.Column(scale=1, elem_id="gen_column"):
                generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn")
        with gr.Row():
            with gr.Column():
                selected_info = gr.Markdown("Select a base model or add a custom one below.") # Updated initial text
                gallery = gr.Gallery(
                    # Ensure items have 'image' and 'title' keys, provide fallbacks if needed
                    [(item.get("image"), item.get("title", item["repo"].split('/')[-1])) for item in loras],
                    label="Model Gallery", # Changed label
                    allow_preview=False,
                    columns=3,
                    elem_id="gallery",
                    show_share_button=False
                )
                with gr.Group():
                    custom_lora = gr.Textbox(label="Custom Model", info="Hugging Face model ID (e.g., user/model-name) or URL", placeholder="stabilityai/stable-diffusion-xl-base-1.0") # Updated label/placeholder
                    gr.Markdown("[Check Hugging Face Models](https://huggingface.co/models?inference_provider=fal-ai&pipeline_tag=text-to-image&sort=trending)", elem_id="lora_list") # Updated link/text
                custom_lora_info = gr.HTML(visible=False)
                custom_lora_button = gr.Button("Clear custom model info", visible=False) # Changed button text
            with gr.Column():
                # Keep progress bar element, but it will only be shown briefly if API is slow, then hidden by run_lora return
                progress_bar = gr.Markdown(elem_id="progress", visible=False, value="Generating...")
                result = gr.Image(label="Generated Image")
                # Display the seed used for the generation
                used_seed_display = gr.Textbox(label="Seed Used", value=0, interactive=False) # Display seed used

        gallery.select(
            update_selection,
            inputs=[], # No direct inputs needed, uses evt
            # Update prompt placeholder, selection text, selected index state, and width/height states
            outputs=[prompt, selected_info, selected_index, width, height],
            api_name=False,
        )
        # Use submit event for Textbox to trigger add_custom_lora
        custom_lora.submit(
            add_custom_lora,
            inputs=[custom_lora],
            # Outputs: info card, remove button, gallery, selection text, selected index state, prompt placeholder
            outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, prompt],
            api_name=False,
        )
        custom_lora_button.click(
            remove_custom_lora,
            outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, custom_lora], # Clear textbox too
            api_name=False,
        )
        gr.on(
            triggers=[generate_button.click, prompt.submit],
            fn=run_lora,
            # Inputs now use state variables for width, height, seed
            inputs=[prompt, selected_index, seed, width, height],
            # Outputs: result image, seed state (updated with used seed), progress bar update
            outputs=[result, seed, progress_bar],
            api_name=False,
        ).then(
            # Update the displayed seed value after run_lora completes
            lambda s: gr.update(value=s),
            inputs=[seed],
            outputs=[used_seed_display],
            api_name=False,
        )

    app.queue()
    app.launch(debug=True, show_api=False)