CouchPotato101/prostate_detection_backend

1import torch2import numpy as np3from utils import load_and_preprocess_image, create_tiled4 5 6def predict_image(path: str):7    # Import here to ensure model is loaded8    from model_loader import model, device9 10    if model is None:11        raise RuntimeError("Model not loaded. Call load_model() first!")12 13    image = load_and_preprocess_image(path)14    tiled = create_tiled(image)15 16    inp = tiled.astype(np.float32) / 255.017    inp = inp.transpose(2, 0, 1)18    inp = torch.tensor(inp).unsqueeze(0).to(device)19 20    with torch.no_grad():21        out = model(inp)22        probs = torch.sigmoid(out).cpu().numpy()[0]23        isup = int(np.sum(probs > 0.5))24 25    return {26        "isup_grade": isup,27        "probabilities": probs.tolist()28    }29