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bottom_bar_layout = QHBoxLayout()
self.save_button = QPushButton('Save as file')
self.save_button.clicked.connect(self.save_as_file)        
self.save_button.setFixedWidth(300)
bottom_bar_layout.addWidget(self.save_button)
self.percent_traced_label = QLabel()
bottom_bar_layout.addWidget(self.percent_traced_label)

main_layout.addLayout(top_bar_layout)
main_layout.addLayout(image_bar_layout)

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def choose_source_image(self):
        self.source_filename = QFileDialog.getOpenFileName()[0]
        self.source_image_data = cv2.imread(self.source_filename)
        source_image_resized = resize_image(self.source_image_data, self.max_img_width, self.max_img_height)
        self.source_image.setPixmap(pixmap_from_cv_image(source_image_resized))

def process_image(self):
    if self.source_image_data is None:
        error_dialog = QErrorMessage()
        error_dialog.showMessage('No image selected')

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def resize_image(image_data, max_img_width, max_img_height):
    scale_percent = min(max_img_width / image_data.shape[1], max_img_height / image_data.shape[0])
    width = int(image_data.shape[1] * scale_percent)
    height = int(image_data.shape[0] * scale_percent)
    newSize = (width, height)
    image_resized = cv2.resize(image_data, newSize, None, None, None, cv2.INTER_AREA)
    return image_resized


def pixmap_from_cv_image(cv_image):

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import sys
from PyQt6.QtWidgets import QApplication, QHBoxLayout, QMainWindow, QPushButton, QSpinBox, QVBoxLayout, QWidget, QFileDialog, QLabel, QErrorMessage
from PyQt6.QtGui import QImage, QPixmap
import cv2
import numpy as np


class ImageWidget(QLabel):
    def __init__(self, parent=None):
        super().__init__(parent)

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import sys
from PyQt6.QtWidgets import QApplication, QHBoxLayout, QMainWindow, QPushButton, QSpinBox, QVBoxLayout, QWidget, QFileDialog, QLabel, QErrorMessage
from PyQt6.QtGui import QImage, QPixmap
import cv2
import numpy as np

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import pandas as pd
import plotly.express as px
from sklearn.feature_extraction.text import TfidfVectorizer
import wikipedia
import numpy as np
from tqdm.notebook import tqdm
import re
import os
from sklearn.metrics.pairwise import linear_kernel
from surprise import Dataset

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movies_df = pd.read_csv(f'{BASE_FOLDER}/MovieLens-1M/movies.dat',
                        delimiter='::', engine= 'python', header=None,
                        names=['movie_name', 'genre'])
movies_df.head()

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movies_df['genre'] = movies_df['genre'].apply(lambda x: x.split('|'))
movies_df_exploded = movies_df.explode('genre')
px.histogram(movies_df_exploded, x='genre', height=400, title='Movie count by genre').update_xaxes(categoryorder="total descending")

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movies_df['year'] = movies_df['movie_name'].apply(lambda movie_name: re.search('\((\d*)\)', movie_name).groups(1)[0])
movie_count_by_year = px.histogram(movies_df, x='year', height=400, title='Movie count by year').update_xaxes(categoryorder="total descending")
movie_count_by_year