import gradio as gr
from diffusers import StableDiffusionImg2ImgPipeline
import torch
from PIL import Image

# Load the model
model_id = "nitrosocke/Ghibli-Diffusion"
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id, torch_dtype=torch.float32)

# Move pipeline to GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = pipe.to(device)

# Define the inference function
def ghibli_transform(input_image, prompt="ghibli style", strength=0.75, guidance_scale=7.5, num_steps=50):
    if input_image is None:
        raise gr.Error("No image uploaded! Please upload an image before clicking Transform.")
    
    # Process the input image (keep it as PIL Image)
    try:
        init_image = input_image.convert("RGB").resize((768, 768))
    except Exception as e:
        raise gr.Error(f"Failed to process image: {str(e)}")
    
    # Generate the Ghibli-style image
    try:
        output = pipe(
            prompt=prompt,
            image=init_image,
            strength=strength,
            guidance_scale=guidance_scale,
            num_inference_steps=num_steps  # Use the UI-provided value
        ).images[0]
    except Exception as e:
        raise gr.Error(f"Pipeline error: {str(e)}")
    
    return output

# Create the Gradio interface
with gr.Blocks(title="Ghibli Diffusion Image Transformer") as demo:
    gr.Markdown("# Ghibli Diffusion Image Transformer")
    gr.Markdown("Upload an image and transform it into Studio Ghibli style using nitrosocke/Ghibli-Diffusion!")
    
    with gr.Row():
        with gr.Column():
            input_img = gr.Image(label="Upload Image", type="pil")
            prompt = gr.Textbox(label="Prompt", value="ghibli style")
            strength = gr.Slider(0, 1, value=0.75, step=0.05, label="Strength (How much to transform)")
            guidance = gr.Slider(1, 20, value=7.5, step=0.5, label="Guidance Scale")
            num_steps = gr.Slider(10, 100, value=50, step=5, label="Inference Steps (Higher = Better Quality, Slower)")
            submit_btn = gr.Button("Transform")
        with gr.Column():
            output_img = gr.Image(label="Ghibli-Style Output")
    
    # Connect the button to the function
    submit_btn.click(
        fn=ghibli_transform,
        inputs=[input_img, prompt, strength, guidance, num_steps],
        outputs=output_img
    )

# Launch the Space with share=True for public link
demo.launch(share=True)