app.py

import gradio as gr
from gradio_bbox_annotator import BBoxAnnotator
from PIL import Image
import numpy as np

from inference import load_model, get_embedding, run
import torch
import os
import spaces


MODEL = None
DEVICE = torch.device("cpu")
CUDA_READY = False

def load_model_cpu(checkpoint_path: str):
    global MODEL, DEVICE
    MODEL, _ = load_model(checkpoint_path)  
    MODEL = MODEL.to("cpu")
    MODEL.eval()
    DEVICE = torch.device("cpu")

load_model_cpu("medsam_vit_b.pth")

@spaces.GPU
def prepare_cuda():
    global MODEL, DEVICE, CUDA_READY
    if torch.cuda.is_available() and not CUDA_READY:
        print("CUDA is available. Moving model to GPU...")
        MODEL.to("cuda")
        DEVICE = torch.device("cuda")
        CUDA_READY = True
        _ = torch.zeros(1, device=DEVICE)
        print("Model moved to CUDA.")
    else:
        print("CUDA not available or already initialized.")

def parse_first_bbox(bboxes):

    if not bboxes:
        return None
    b = bboxes[0]
    if isinstance(b, dict):
        x, y = float(b["x"]), float(b["y"])
        w, h = float(b["width"]), float(b["height"])
        return x, y, x + w, y + h
    if isinstance(b, (list, tuple)) and len(b) >= 4:
        return float(b[0]), float(b[1]), float(b[2]), float(b[3])
    return None

def segment(annot_value):
    prepare_cuda()

    if annot_value is None or len(annot_value) < 1:
        return None, 

    img_path = annot_value[0]
    bboxes = annot_value[1] if len(annot_value) > 1 else []

    if not bboxes:
        return None, 

    img = Image.open(img_path).convert("RGB")
    img_np = np.array(img)
    H, W, _ = img_np.shape


    box = parse_first_bbox(bboxes)
    if box is None:
        return None, "解析矩形框失败，请重画。"

    xmin, ymin, xmax, ymax = box
    xmin, ymin, xmax, ymax = map(int, [xmin, ymin, xmax, ymax])
    box_np = np.array([[xmin, ymin, xmax, ymax]], dtype=float)
    box_1024 = box_np / np.array([W, H, W, H]) * 1024.0

    embedding = get_embedding(MODEL, img_np, DEVICE)
    mask = run(MODEL, embedding, box_1024, H, W)  


    mask_rgb = np.stack([mask * 255] * 3, axis=-1).astype(np.uint8)
    bbox_text = f"xmin={int(xmin)}, ymin={int(ymin)}, xmax={int(xmax)}, ymax={int(ymax)}"

    return Image.fromarray(mask_rgb), bbox_text


example = ("003_img.png", [(50, 60, 120, 150, "cell")])

demo = gr.Interface(
    fn=segment,  
    inputs=BBoxAnnotator(
        value=example,                 
        categories=["cell", "nucleus"],
        label="upload"
    ),
    outputs=[
        gr.Image(type="pil", label="Mask result"),
        gr.Textbox(label="location")
    ],
    examples=[[example]],
    cache_examples=False
)

if __name__ == "__main__":
    demo.queue().launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,         
        show_error=True,
        ssr_mode=False       
    )