import gradio as gr
import pandas as pd
import numpy as np
import pydicom
import os
from skimage import transform
import torch
from segment_anything import sam_model_registry
import matplotlib.pyplot as plt
from PIL import Image
import torch.nn.functional as F
import io
from gradio_image_prompter import ImagePrompter
import nrrd  # Add this import for NRRD file support

def load_image(file_path):
    if file_path.endswith(".dcm"):
        ds = pydicom.dcmread(file_path)
        img = ds.pixel_array
    elif file_path.endswith(".nrrd"):
        img, _ = nrrd.read(file_path)  # Add this condition for NRRD files
    else:
        img = np.array(Image.open(file_path))

    # Convert grayscale to 3-channel RGB by replicating channels
    if len(img.shape) == 2:  # Grayscale image (height, width)
        img = np.stack((img,)*3, axis=-1)  # Replicate grayscale channel to get (height, width, 3)

    H, W = img.shape[:2]
    return img, H, W

@torch.no_grad()
def medsam_inference(medsam_model, img_embed, points_1024, H, W):
    points_torch = torch.as_tensor(points_1024, dtype=torch.float, device=img_embed.device)
    points_torch = points_torch.reshape(1, -1, 2)  # (1, N, 2)

    sparse_embeddings, dense_embeddings = medsam_model.prompt_encoder(
        points=points_torch,
        boxes=None,
        masks=None,
    )
    
    low_res_logits, _ = medsam_model.mask_decoder(
        image_embeddings=img_embed,  # (B, 256, 64, 64)
        image_pe=medsam_model.prompt_encoder.get_dense_pe(),  # (1, 256, 64, 64)
        sparse_prompt_embeddings=sparse_embeddings,  # (B, 2, 256)
        dense_prompt_embeddings=dense_embeddings,  # (B, 256, 64, 64)
        multimask_output=False,
    )

    low_res_pred = torch.sigmoid(low_res_logits)  # (1, 1, 256, 256)

    low_res_pred = F.interpolate(
        low_res_pred,
        size=(H, W),
        mode="bilinear",
        align_corners=False,
    )  # (1, 1, gt.shape)
    low_res_pred = low_res_pred.squeeze().cpu().numpy()  # (H, W)
    medsam_seg = (low_res_pred > 0.5).astype(np.uint8)
    return medsam_seg

# Function for visualizing images with masks
def visualize(image, mask, points):
    fig, ax = plt.subplots(1, 2, figsize=(10, 5))
    ax[0].imshow(image, cmap='gray')
    for point in points:
        ax[0].plot(point[0], point[1], 'ro')  # Mark points on the image
    ax[1].imshow(image, cmap='gray')
    ax[1].imshow(mask, alpha=0.5, cmap="jet")
    plt.tight_layout()

    # Convert matplotlib figure to a PIL Image
    buf = io.BytesIO()
    fig.savefig(buf, format='png')
    plt.close(fig)  # Close the figure to release memory
    buf.seek(0)
    pil_img = Image.open(buf)
    
    return pil_img

# Main function for Gradio app
def process_images(img_dict):
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    
    # Load and preprocess image
    img = img_dict['image']
    points = img_dict['points']
    if len(points) == 0:
        raise ValueError("No points provided.")
    
    image, H, W = img, img.shape[0], img.shape[1]
    if len(image.shape) == 2:
        image = np.repeat(image[:, :, None], 3, axis=-1)
    H, W, _ = image.shape

    image_resized = transform.resize(image, (1024, 1024), order=3, preserve_range=True, anti_aliasing=True).astype(np.uint8)
    image_resized = (image_resized - image_resized.min()) / np.clip(image_resized.max() - image_resized.min(), a_min=1e-8, a_max=None)
    image_tensor = torch.tensor(image_resized).float().permute(2, 0, 1).unsqueeze(0).to(device)

    # Initialize the MedSAM model and set the device
    model_checkpoint_path = "medsam_vit_b.pth"  # Replace with the correct path to your checkpoint
    medsam_model = sam_model_registry['vit_b'](checkpoint=model_checkpoint_path)
    medsam_model = medsam_model.to(device)
    medsam_model.eval()

    # Calculate resized point coordinates
    scale_factors = np.array([1024 / W, 1024 / H])
    points_1024 = np.array(points) * scale_factors

    # Perform inference
    mask = medsam_inference(medsam_model, img_embed, points_1024, H, W)

    # Visualization
    visualization = visualize(image, mask, points)
    return visualization

# Set up Gradio interface
iface = gr.Interface(
    fn=process_images,
    inputs=[
        ImagePrompter(label="Image")
    ],
    outputs=[
        gr.Image(type="pil", label="Processed Image")
    ],
    title="ROI Selection with MEDSAM",
    description="Upload an image (including NRRD files) and select points of interest for processing."
)

# Launch the interface
iface.launch()