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 io

# Function to load bounding boxes from CSV
def load_bounding_boxes(csv_file):
    # Assuming CSV file has columns: 'filename', 'x_min', 'y_min', 'x_max', 'y_max'
    df = pd.read_csv(csv_file)
    return df

# Function to load DICOM images
def load_dicom_images(folder_path):
    images = []
    for filename in sorted(os.listdir(folder_path)):
        if filename.endswith(".dcm"):
            filepath = os.path.join(folder_path, filename)
            ds = pydicom.dcmread(filepath)
            img = ds.pixel_array
            images.append(img)
    return np.array(images)

# MedSAM inference function
def medsam_inference(medsam_model, img, box, H, W, target_size):
    # Resize image and box to target size
    img_resized = transform.resize(img, (target_size, target_size), anti_aliasing=True)
    box_resized = np.array(box) * (target_size / np.array([W, H, W, H]))

    # Convert image to PyTorch tensor
    img_tensor = torch.from_numpy(img_resized).float().unsqueeze(0).unsqueeze(0).to(device)  # Add channel and batch dimension

    # Model expects box in format (x0, y0, x1, y1)
    box_tensor = torch.tensor(box_resized, dtype=torch.float32).unsqueeze(0).to(device)  # Add batch dimension

    # MedSAM inference
    img_embed = medsam_model.image_encoder(img_tensor)
    mask = medsam_model.predict(img_embed, box_tensor)

    # Post-process mask: resize back to original size
    mask_resized = transform.resize(mask[0].cpu().numpy(), (H, W))

    return mask_resized

# Function for visualizing images with masks
def visualize(images, masks, box):
    fig, ax = plt.subplots(len(images), 2, figsize=(10, 5*len(images)))
    for i, (image, mask) in enumerate(zip(images, masks)):
        ax[i, 0].imshow(image, cmap='gray')
        ax[i, 0].add_patch(plt.Rectangle((box[0], box[1]), box[2]-box[0], box[3]-box[1], edgecolor="red", facecolor="none"))
        ax[i, 1].imshow(image, cmap='gray')
        ax[i, 1].imshow(mask, alpha=0.5, cmap="jet")
    plt.tight_layout()
    buf = io.BytesIO()
    plt.savefig(buf, format='png')
    plt.close(fig)
    buf.seek(0)
    return buf

# Main function for Gradio app
def process_images(csv_file, dicom_folder, target_size):
    bounding_boxes = load_bounding_boxes(csv_file)
    dicom_images = load_dicom_images(dicom_folder)

    # Initialize MedSAM model
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    medsam_model = sam_model_registry['your_model_version'](checkpoint='path_to_your_checkpoint')
    medsam_model = medsam_model.to(device)
    medsam_model.eval()

    masks = []
    for index, row in bounding_boxes.iterrows():
        if index >= len(dicom_images):
            continue  # Skip if the index exceeds the number of images

        image = dicom_images[index]
        H, W = image.shape
        box = [row['x_min'], row['y_min'], row['x_max'], row['y_max']]

        mask = medsam_inference(medsam_model, image, box, H, W, target_size)
        masks.append(mask)

    visualizations = visualize(dicom_images, masks, box)

    return visualizations, np.array(masks)

# Set up Gradio interface
iface = gr.Interface(
    fn=process_images,
    inputs=[gr.inputs.File(type="file"), gr.inputs.Directory()],
    outputs=[gr.outputs.Image(type="plot"), gr.outputs.File(type="numpy")]
)

iface.launch()