app.py

import logging

import torch
from PIL import Image
from torchvision import transforms
import gradio as gr
import numpy as np
import spaces

from pytorch_grad_cam.utils.image import show_cam_on_image
from scripts.trainer import XrayReg

# Model selection dropdown options (extendable)
MODEL_OPTIONS = {
    "XrayReg (912yp4l6) [vit_large_patch16_224_in21k]": {
        "ckpt": "xray_regression_noaug/912yp4l6/checkpoints/epoch=99-step=5900.ckpt",
        "model_name": "vit_large_patch16_224_in21k"
    },
    "XrayReg (ie399gjr) [vit_small_patch16_224_in21k]": {
        "ckpt": "xray_regression_noaug/ie399gjr/checkpoints/epoch=99-step=5900.ckpt",
        "model_name": "vit_small_patch16_224_in21k"
    },
    "XrayReg (kcku20nx) [vit_large_patch16_224_in21k]": {
        "ckpt": "xray_regression_noaug/kcku20nx/checkpoints/epoch=99-step=5900.ckpt",
        "model_name": "vit_large_patch16_224_in21k"
    },
    "XrayReg (ohtmkj0i) [vit_base_patch16_224_in21k]": {
        "ckpt": "xray_regression_noaug/ohtmkj0i/checkpoints/epoch=99-step=5900.ckpt",
        "model_name": "vit_base_patch16_224_in21k"
    },
    "XrayReg (vlk8qrkx) [vit_large_patch16_224_in21k]": {
        "ckpt": "xray_regression_noaug/vlk8qrkx/checkpoints/epoch=99-step=5900.ckpt",
        "model_name": "vit_large_patch16_224_in21k"
    },
}


def preprocess_image(inp):
    """
    Preprocess the input image.

    Returns:
        input_tensor: Tensor to be fed into the model.
        rgb_img: NumPy array normalized to [0, 1] for GradCAM visualization.
    """
    try:
        preprocess = transforms.Compose(
            [
                transforms.Resize((224, 224)),
                transforms.ToTensor(),
            ]
        )
        input_tensor = preprocess(inp).unsqueeze(0)
        rgb_img = np.array(inp.resize((224, 224))).astype(np.float32) / 255.0
        return input_tensor, rgb_img
    except Exception as e:
        logging.error("Error in image preprocessing: %s", e)
        raise





def preprocess_image_custom(inp):
    preprocess = transforms.Compose(
        [
            transforms.Resize((224, 224)),
            transforms.Grayscale(num_output_channels=1),
            transforms.ToTensor(),
        ]
    )
    input_tensor = preprocess(inp).unsqueeze(0)
    rgb_img = np.array(inp.resize((224, 224)).convert("RGB")).astype(np.float32) / 255.0
    return input_tensor, rgb_img


def predict_custom(model, input_tensor):
    with torch.no_grad():
        input_tensor = (
            input_tensor.cuda() if torch.cuda.is_available() else input_tensor
        )
        pred = model(input_tensor)
        pred = pred.cpu().numpy().flatten()[0]
    return float(pred)


def load_custom_model(model_key):
    model_info = MODEL_OPTIONS[model_key]
    # Pass model_name to config for correct model instantiation
    config = {"model": {"name": model_info["model_name"]}}
    model = XrayReg.load_from_checkpoint(model_info["ckpt"], map_location="cpu")
    model = model.model
    model.eval()
    for param in model.parameters():
        param.requires_grad = True
    return model

@spaces.GPU
def predict_and_cam_custom(inp, model):
    input_tensor, rgb_img = preprocess_image_custom(inp)
    model = model.cuda() if torch.cuda.is_available() else model.model
    model.eval()
    for param in model.parameters():
        param.requires_grad = True
    value = predict_custom(model, input_tensor)
    # GradCAM for regression: use last conv layer, target output
    from pytorch_grad_cam import GradCAM
    from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
    # model = model.cuda()
    target_layers = [
        layer
        for name, layer in model.named_modules()
        if isinstance(layer, torch.nn.Conv2d)
    ][-1:]
    gradcam = GradCAM(model=model, target_layers=target_layers)
    targets = [ClassifierOutputTarget(0)]  # For regression, just use output
    grayscale_cam = gradcam(input_tensor=input_tensor, targets=targets)[0]
    cam_image = show_cam_on_image(rgb_img, grayscale_cam, use_rgb=True)
    cam_pil = Image.fromarray(cam_image)
    # Return as tuple (number, image) for Gradio
    return value, cam_pil


def create_interface_custom():
    # Use stateful model cache to avoid reloading on every prediction
    from functools import lru_cache

    @lru_cache(maxsize=5)
    def cached_load_model(model_key):
        return load_custom_model(model_key)

    def predict_wrapper(inp, model_key):
        model = cached_load_model(model_key)
        return predict_and_cam_custom(inp, model)

    interface = gr.Interface(
        fn=predict_wrapper,
        inputs=[
            gr.Image(type="pil"),
            gr.Dropdown(list(MODEL_OPTIONS.keys()), label="Model"),
        ],
        outputs=[
            gr.Number(label="Regression Output"),
            gr.Image(type="pil", label="GradCAM Visualization"),
        ],
        examples=None,
        title="Xray Regression Gradio App",
        description="Upload an X-ray image and select a model to get regression output and GradCAM visualization.",
        allow_flagging="never",
        live=True,  # Ensures model reloads on dropdown change
    )
    return interface


def download_models():
    import huggingface_hub

    repo_name = "SuperSecureHuman/xray-reg-models"
    local_dir = "./"
    huggingface_hub.snapshot_download(
        repo_id=repo_name,
        local_dir=local_dir,
    )

def main():
    # Download models if not already present
    try:
        download_models()
    except Exception as e:
        logging.error("Error downloading models: %s", e)
        exit(1)
    logging.basicConfig(level=logging.INFO)
    interface = create_interface_custom()
    interface.launch()


if __name__ == "__main__":
    main()