import pandas as pd
import numpy as np
import tensorflow as tf
import torch
import os
import cv2
from transformers import AutoModelForImageClassification

def image_preprocessing(image):
    images=[]
    for i in image:
        binary_image = i
        binary_image = cv2.resize(binary_image, (224, 224))
        binary_image = cv2.merge([binary_image, binary_image, binary_image])
        binary_image = binary_image/255
        binary_image = torch.from_numpy(binary_image)
        images.append(binary_image)
    return images

def predict_image(image_path, model):
    preprocessed_img = image_preprocessing(image_path)
    images = torch.stack(preprocessed_img)
    images = images.permute(0, 3, 1, 2)
    predictions = model(images).logits.detach().numpy()
    return predictions


model = AutoModelForImageClassification.from_pretrained("models/vit-base-beans")

def struck_images(word__image):

    
    predictions = predict_image(word__image, model)

    not_struck =[]
    for i in range(len(predictions)):
        if predictions[i].argmax().item() == 0:
           
            not_struck.append(word__image[i])

    # print(not_struck)
    return not_struck


# struck_images()