app.py

import gradio as gr
from PIL import Image
import numpy as np
from transformers import SamModel, SamProcessor
from diffusers import AutoPipelineForInpainting
import torch

# Check if GPU is available, otherwise use CPU
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")

# Model and Processor setup
model_name = "facebook/sam-vit-huge"
model = SamModel.from_pretrained(model_name).to(device)
processor = SamProcessor.from_pretrained(model_name)

def mask_to_rgb(mask):
    """ Convert binary mask to RGB with transparency for the background. """
    bg_transparent = np.zeros(mask.shape + (4,), dtype=np.uint8)
    bg_transparent[mask == 1] = [0, 255, 0, 127]  # Green mask with some transparency
    return bg_transparent

def get_processed_inputs(image, points):
    """ Process the input image and points using SAM model and processor. """
    inputs = processor(image, input_points=points, return_tensors="pt").to(device)
    with torch.no_grad():
        outputs = model(**inputs)
    masks = processor.image_processor.post_process_masks(
        outputs.pred_masks.cpu(), 
        inputs["original_sizes"].cpu(), 
        inputs["reshaped_input_sizes"].cpu()
    )
    best_mask = masks[0][0][outputs.iou_scores.argmax()]
    return ~best_mask.cpu().numpy()

def inpaint(raw_image, input_mask, prompt, negative_prompt=None, seed=74294536, cfgs=7):
    """ Inpaint the masked area in the image using a text prompt and an inpainting pipeline. """
    mask_image = Image.fromarray(input_mask)
    rand_gen = torch.manual_seed(seed)
    pipeline = AutoPipelineForInpainting.from_pretrained(
        "diffusers/stable-diffusion-xl-1.0-inpainting-0.1", 
        torch_dtype=torch.float16 if device == "cuda" else torch.float32
    ).to(device)
    if device == "cpu":
        pipeline.enable_model_cpu_offload()
    image = pipeline(
        prompt=prompt,
        image=raw_image,
        mask_image=mask_image,
        guidance_scale=cfgs,
        negative_prompt=negative_prompt,
        generator=rand_gen
    ).images[0]
    return image

def gradio_interface(image, points_json, positive_prompt, negative_prompt):
    """ Gradio interface function to handle image, points for segmentation, and prompts. """
    points = [[(point['x'], point['y']) for point in stroke['points']] for stroke in points_json]
    raw_image = Image.fromarray(image).convert("RGB").resize((512, 512))
    mask = get_processed_inputs(raw_image, points)
    processed_image = inpaint(raw_image, mask, positive_prompt, negative_prompt)
    return processed_image, mask_to_rgb(mask)

iface = gr.Interface(
    fn=gradio_interface,
    inputs=[
        gr.Image(type="numpy", label="Input Image"),
        gr.Image(type="json", label="Click to select points", tool="sketch", brush_radius=1, shape=(512, 512)),
        gr.Textbox(label="Positive Prompt", placeholder="Enter positive prompt here"),
        gr.Textbox(label="Negative Prompt", placeholder="Enter negative prompt here")
    ],
    outputs=[
        gr.Image(label="Inpainted Image"),
        gr.Image(label="Segmentation Mask")
    ],
    title="Interactive Image Inpainting",
    description="Click on the image to select points for segmentation, provide prompts, and see the inpainted result."
)

iface.launch(share=True)