app.py

import gradio as gr
import numpy as np
import torch
from diffusers import StableDiffusionInpaintPipeline
from PIL import Image
from segment_anything import SamPredictor, sam_model_registry

device = "cuda"
sam_checkpoint = "/home/jupyter/diffusers/examples/sam_vit_h_4b8939.pth"  # Added missing forward slash at the beginning
model_type = "vit_h"
# Load the model using the function from the registry and pass the checkpoint path
model_fn = sam_model_registry[model_type]
model = model_fn(checkpoint=sam_checkpoint)

# Move the model to the desired device (GPU)
model.to(device)

predictor = SamPredictor(model)

pipe = StableDiffusionInpaintPipeline.from_pretrained(
    "stabilityai/stable-diffusion-2-inpainting",
    torch_dtype=torch.float16,
)  # Removed space

pipe = pipe.to(device)

selected_pixels = []


with gr.Blocks() as demo:
    with gr.Row():
        input_img = gr.Image(label="Input")  # Removed space
        mask_img = gr.Image(label="Mask")  # Corrected "Mas" to "Mask"
        output_img = gr.Image(label="Output")  # Removed space

    with gr.Row():
        prompt_text = gr.Textbox(lines=1, label="Prompt")  # Removed space

    with gr.Row():
        submit = gr.Button("Submit")

    def generate_mask(image, evt: gr.SelectData):
        selected_pixels.append(evt.index)  # Removed space

        predictor.set_image(image)  # Removed space
        input_points = np.array(selected_pixels)
        input_labels = np.ones(input_points.shape[0])
        mask, _, _ = predictor.predict(
            point_coords=input_points,
            point_labels=input_labels,
            multimask_output=False
        )
        # (n, sz, sz)
        mask = Image.fromarray(mask[0, :, :])  # Removed space
        return mask

    def inpaint(image, mask, prompt):
        image = Image.fromarray(image)  # Removed space
        mask = Image.fromarray(mask)  # Removed space

        image = image.resize((512, 512))
        mask = mask.resize((512, 512))

        output = pipe(
            prompt=prompt,
            image=image,
            mask_image=mask,
        ).images[0]

        return output

    input_img.select(generate_mask, [input_img], [mask_img])
    submit.click(inpaint, inputs=[input_img, mask_img, prompt_text], outputs=[output_img])

if __name__ == "__main__":
    demo.launch()