import gradio as gr
from diffusers import DiffusionPipeline

# Load the diffusion pipeline
pipeline = DiffusionPipeline.from_pretrained("sd-community/sdxl-flash-mini")

def generate_image(prompt, num_inference_steps=50, guidance_scale=7.5):
    # Generate image based on prompt
    image = pipeline(prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale).images[0]
    return image

# Create Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# sdxl-flash-mini")
    
    with gr.Row():
        with gr.Column():
            prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here")
            num_inference_steps = gr.Slider(label="Number of Inference Steps", minimum=1, maximum=100, value=50)
            guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, value=7.5)
            generate_button = gr.Button("Generate Image")
        
        with gr.Column():
            output_image = gr.Image(label="Generated Image")

    generate_button.click(
        fn=generate_image,
        inputs=[prompt, num_inference_steps, guidance_scale],
        outputs=output_image
    )

# Launch the Gradio interface
demo.launch()