import os
import pandas as pd
from huggingface_hub import HfApi, HfFolder
import yaml
import numpy as np
import time

config = yaml.safe_load(open("./config/config.yaml")) 
    
def load_image_and_saliency(class_idx, data_dir):
    path = os.path.join(data_dir, 'images', str(class_idx))
    images = os.listdir(path)
    # pick a random image
    # set random seed usiing time
    np.random.seed(int(time.time()))
    id = np.random.randint(0, len(images))
    image = os.path.join(path, images[id])
    gradcam_image = os.path.join(data_dir, 'saliency', 'gradcam', images[id])
    lime_image = os.path.join(data_dir, 'saliency', 'lime', images[id])
    sidu_image = os.path.join(data_dir, 'saliency', 'sidu', images[id])
    rise_image = os.path.join(data_dir, 'saliency', 'rise', images[id])
    return image, gradcam_image, lime_image, sidu_image, rise_image

def load_example_images(class_idx, data_dir, max_images=16):
    path = os.path.join(data_dir, 'images', str(class_idx))
    images = os.listdir(path)
    # set random seed usiing time
    np.random.seed(int(time.time()))
    ids = np.random.choice(len(images), max_images, replace=False)
    images = [os.path.join(path, images[id]) for id in ids]
    return images

# Function to load words based on global variable
def load_words(idx):
    words = [f"word_{idx}_{i}" for i in range(20)]
    return words


def load_csv_concepts(data_dir):
    # Load data from csv
    data = pd.read_csv(os.path.join(data_dir, 'concepts_by_class.csv'))
    return data