import pandas as pd
import os
from PIL import Image
import numpy as np
import torch
import matplotlib.pyplot as plt
from IPython import get_ipython
import sys
import gc
import streamlit as st


def show_image(image):
    """
    Display an image in various environments (Jupyter, PyCharm, Hugging Face Spaces).
    Handles different types of image inputs (file path, PIL Image, numpy array, OpenCV, PyTorch tensor).

    Args:
    image (str or PIL.Image or numpy.ndarray or torch.Tensor): The image to display.
    """
    in_jupyter = is_jupyter_notebook()
    in_colab = is_google_colab()

    # Convert image to PIL Image if it's a file path, numpy array, or PyTorch tensor
    if isinstance(image, str):

        if os.path.isfile(image):
            image = Image.open(image)
        else:
            raise ValueError("File path provided does not exist.")
    elif isinstance(image, np.ndarray):

        if image.ndim == 3 and image.shape[2] in [3, 4]:

            image = Image.fromarray(image[..., ::-1] if image.shape[2] == 3 else image)
        else:

            image = Image.fromarray(image)
    elif torch.is_tensor(image):

        image = Image.fromarray(image.permute(1, 2, 0).numpy().astype(np.uint8))

    # Display the image
    if in_jupyter or in_colab:

        from IPython.display import display
        display(image)
    else:

        image.show()



def show_image_with_matplotlib(image):
    if isinstance(image, str):
        image = Image.open(image)
    elif isinstance(image, np.ndarray):
        image = Image.fromarray(image)
    elif torch.is_tensor(image):
        image = Image.fromarray(image.permute(1, 2, 0).numpy().astype(np.uint8))

    plt.imshow(image)
    plt.axis('off')  # Turn off axis numbers
    plt.show()


def is_jupyter_notebook():
    """
    Check if the code is running in a Jupyter notebook.

    Returns:
        bool: True if running in a Jupyter notebook, False otherwise.
    """
    try:
        from IPython import get_ipython
        if 'IPKernelApp' not in get_ipython().config:
            return False
        if 'ipykernel' in str(type(get_ipython())):
            return True  # Running in Jupyter Notebook
    except (NameError, AttributeError):
        return False  # Not running in Jupyter Notebook

    return False  # Default to False if none of the above conditions are met


def is_pycharm():
    return 'PYCHARM_HOSTED' in os.environ


def is_google_colab():
    return 'COLAB_GPU' in os.environ or 'google.colab' in sys.modules


def get_image_path(name, path_type):
    """
    Generates a path for models, images, or data based on the specified type.

    Args:
    name (str): The name of the model, image, or data folder/file.
    path_type (str): The type of path needed ('models', 'images', or 'data').

    Returns:
    str: The full path to the specified resource.
    """
    # Get the current working directory (assumed to be inside 'code' folder)
    current_dir = os.getcwd()

    # Get the directory one level up (the parent directory)
    parent_dir = os.path.dirname(current_dir)

    # Construct the path to the specified folder
    folder_path = os.path.join(parent_dir, path_type)

    # Construct the full path to the specific resource
    full_path = os.path.join(folder_path, name)

    return full_path


def get_model_path(model_name):
    """
    Get the path to the specified model folder.
    
    Args:
        model_name (str): Name of the model folder.

    Returns:
        str: Absolute path to the specified model folder.
    """
    # Directory of the current script
    current_script_dir = os.path.dirname(os.path.abspath(__file__))

    # Directory of the 'app' folder (parent of the 'my_model' folder)
    app_dir = os.path.dirname(os.path.dirname(current_script_dir))

    # Path to the 'models/{model_name}' folder
    model_path = os.path.join(app_dir, "models", model_name)

    return model_path



def free_gpu_resources():
    """
    Clears GPU memory.
    """

    if torch.cuda.is_available():
        torch.cuda.empty_cache()
        torch.cuda.empty_cache()
        gc.collect()
        gc.collect()