app.py

import gradio as gr
import torch
import numpy as np
from PIL import Image
import cv2
from segment_anything import sam_model_registry, SamPredictor

# Load model
checkpoint = "sam_vit_h_4b8939.pth"
device = "cuda" if torch.cuda.is_available() else "cpu"
model_type = "vit_h"

sam = sam_model_registry[model_type](checkpoint=checkpoint)
sam.to(device)
predictor = SamPredictor(sam)

def segment_image(input_img):
    np_img = np.array(input_img)
    image = cv2.cvtColor(np_img, cv2.COLOR_RGB2BGR)

    predictor.set_image(image)
    h, w, _ = image.shape
    input_point = np.array([[w // 2, h // 2]])
    input_label = np.array([1])
    
    masks, scores, logits = predictor.predict(
        point_coords=input_point,
        point_labels=input_label,
        multimask_output=False
    )

    mask = masks[0].astype(np.uint8) * 255
    return Image.fromarray(mask)

# UI
iface = gr.Interface(fn=segment_image,
                     inputs=gr.Image(type="pil"),
                     outputs=gr.Image(type="pil"),
                     title="Segment Anything Model",
                     description="Upload an image and get a segmentation mask.")

iface.launch()