import gradio as gr

def generate_image(prompt, image_size="Default", num_inference_steps=28, seed="random", guidance_scale=3.5, sync_mode=True, num_images=1):
    # Load the model (assuming it supports these parameters)
    model = gr.load("models/robiai/picasoe", provider="hf-inference")
    # Implement image generation logic here
    # Return the generated image
    pass

with gr.Blocks(title="Picasoe") as demo:
    gr.Markdown("# Picasoe")
    gr.Markdown("Convert your ideas into jaw-dropping visuals.")
    
    with gr.Row():
        with gr.Column():
            # Input components
            prompt = gr.Textbox(label="Prompt", placeholder="Describe the image you want to generate...")
            image_size = gr.Dropdown(
                choices=["Default", "Square", "Square HD", "Portrait 3:4", "Portrait 9:16", "Landscape 4:3", "Landscape 16:9", "Custom"],
                label="Image Size",
                value="Default"
            )
            num_inference_steps = gr.Slider(minimum=1, maximum=50, step=1, label="Num Inference Steps", value=28)
            seed = gr.Textbox(label="Seed", placeholder="random", value="random")
            guidance_scale = gr.Slider(minimum=0.1, maximum=10.0, step=0.1, label="Guidance Scale (CFG)", value=3.5)
            sync_mode = gr.Checkbox(label="Sync Mode", value=True)
            num_images = gr.Slider(minimum=1, maximum=10, step=1, label="Number of Images", value=1)
            generate_btn = gr.Button("Generate Image")
        
        with gr.Column():
            # Output component
            output_image = gr.Image(label="Generated Image")
    
    # Set up the click event for generating the image
    generate_btn.click(
        fn=generate_image,
        inputs=[prompt, image_size, num_inference_steps, seed, guidance_scale, sync_mode, num_images],
        outputs=output_image
    )

demo.launch()