News : The geoai-py - part 001.

A powerful Python package for integrating Artificial Intelligence with geospatial data analysis and visualization

GeoAI bridges the gap between AI and geospatial analysis, providing tools for processing, analyzing, and visualizing geospatial data using advanced machine learning techniques. Whether you're working with satellite imagery, LiDAR point clouds, or vector data, GeoAI offers intuitive interfaces to apply cutting-edge AI models.

Today , I tested this python package named geoai-py. I used the pip tool:

pip install geoai-py
Successfully installed Flask-Caching-2.3.1 MarkupSafe-3.0.2 PySocks-1.7.1 PyYAML-6.0.2 absl-py-2.3.1 aenum-3.1.16 affine-2.4.0 aiohappyeyeballs-2.6.1 aiohttp-3.12.14 aiosignal-1.4.0 albucore-0.0.24 albumentations-2.0.8 aniso8601-10.0.1 annotated-types-0.7.0 antlr4-python3-runtime-4.9.3 anyio-4.9.0 anywidget-0.9.18 argon2-cffi-25.1.0 argon2-cffi-bindings-21.2.0 arrow-1.3.0 asttokens-3.0.0 beautifulsoup4-4.13.4 bitsandbytes-0.46.1 bleach-6.2.0 blinker-1.9.0 bqplot-0.12.45 branca-0.8.1 buildingregulariser-0.2.2 cachelib-0.13.0 cachetools-6.1.0 cffi-1.17.1 click-8.2.1 click-plugins-1.1.1.2 cligj-0.7.2 color-operations-0.2.0 comm-0.2.2 contextily-1.6.2 contourpy-1.3.2 cycler-0.12.1 datasets-4.0.0 decorator-5.2.1 defusedxml-0.7.1 dill-0.3.8 docstring-parser-0.17.0 duckdb-1.3.2 einops-0.8.1 eval-type-backport-0.2.2 ever-beta-0.5.1 executing-2.2.0 fastjsonschema-2.21.1 filelock-3.18.0 fiona-1.10.1 flask-3.1.1 flask-cors-6.0.1 flask-restx-1.3.0 folium-0.20.0 fonttools-4.59.0 fqdn-1.5.1 frozenlist-1.7.0 fsspec-2025.3.0 gdown-5.2.0 geoai-py-0.9.0 geographiclib-2.0 geojson-3.2.0 geopandas-1.1.1 geopy-2.4.1 gitdb-4.0.12 gitpython-3.1.44 grpcio-1.73.1 h11-0.16.0 httpcore-1.0.9 httpx-0.28.1 huggingface_hub-0.33.4 hydra-core-1.3.2 importlib-resources-6.5.2 ipyevents-2.0.2 ipyfilechooser-0.6.0 ipyleaflet-0.20.0 ipython-9.4.0 ipython-pygments-lexers-1.1.1 ipytree-0.2.2 ipyvue-1.11.2 ipyvuetify-1.11.3 ipywidgets-8.1.7 isoduration-20.11.0 itsdangerous-2.2.0 jedi-0.19.2 jinja2-3.1.6 joblib-1.5.1 jsonargparse-4.40.0 jsonnet-0.21.0 jsonpointer-3.0.0 jupyter-client-8.6.3 jupyter-core-5.8.1 jupyter-events-0.12.0 jupyter-leaflet-0.20.0 jupyter-server-2.16.0 jupyter-server-proxy-4.4.0 jupyter-server-terminals-0.5.3 jupyterlab-pygments-0.3.0 jupyterlab_widgets-3.0.15 kiwisolver-1.4.8 kornia-0.8.1 kornia_rs-0.1.9 leafmap-0.48.6 lightly-1.5.21 lightly_utils-0.0.2 lightning-2.5.2 lightning-utilities-0.14.3 localtileserver-0.10.6 mapclassify-2.10.0 maplibre-0.3.4 markdown-3.8.2 markdown-it-py-3.0.0 matplotlib-3.10.3 matplotlib-inline-0.1.7 mdurl-0.1.2 mercantile-1.2.1 mistune-3.1.3 morecantile-6.2.0 multidict-6.6.3 multiprocess-0.70.16 narwhals-1.48.0 nbclient-0.10.2 nbconvert-7.16.6 nbformat-5.10.4 numexpr-2.11.0 omegaconf-2.3.0 opencv-python-headless-4.12.0.88 overrides-7.7.0 overturemaps-0.15.0 pandas-2.3.1 pandocfilters-1.5.1 parso-0.8.4 planetary-computer-1.0.0 plotly-6.2.0 prettytable-3.16.0 prometheus-client-0.22.1 prompt_toolkit-3.0.51 propcache-0.3.2 psygnal-0.14.0 pure-eval-0.2.3 pyarrow-21.0.0 pycparser-2.22 pydantic-2.11.7 pydantic-core-2.33.2 pygments-2.19.2 pyogrio-0.11.0 pyparsing-3.2.3 pyproj-3.7.1 pystac-1.13.0 pystac-client-0.9.0 python-box-7.3.2 python-dateutil-2.9.0.post0 python-dotenv-1.1.1 python-json-logger-3.3.0 pytorch_lightning-2.5.2 pytz-2025.2 pywin32-311 pywinpty-2.0.15 pyzmq-27.0.0 rasterio-1.4.3 regex-2024.11.6 rfc3339-validator-0.1.4 rfc3986-validator-0.1.1 rich-14.0.0 rio-cogeo-5.4.2 rio-tiler-7.8.1 rioxarray-0.19.0 rtree-1.4.0 safetensors-0.5.3 scikit-learn-1.7.1 scooby-0.10.1 segmentation-models-pytorch-0.5.0 send2trash-1.8.3 sentry-sdk-2.33.0 server-thread-0.3.0 shapely-2.1.1 simpervisor-1.0.0 simsimd-6.5.0 six-1.17.0 smmap-5.0.2 sniffio-1.3.1 soupsieve-2.7 stack_data-0.6.3 stringzilla-3.12.5 tensorboard-2.20.0 tensorboard-data-server-0.7.2 tensorboardX-2.6.4 terminado-0.18.1 threadpoolctl-3.6.0 timm-1.0.17 tinycss2-1.4.0 tokenizers-0.21.2 torch-2.7.1 torchange-0.0.1 torchgeo-0.7.1 torchinfo-1.8.0 torchmetrics-1.7.4 torchvision-0.22.1 tornado-6.5.1 traitlets-5.14.3 traittypes-0.2.1 transformers-4.53.2 types-python-dateutil-2.9.0.20250708 typeshed-client-2.8.2 typing-inspection-0.4.1 tzdata-2025.2 uri-template-1.3.0 uvicorn-0.35.0 wandb-0.21.0 wcwidth-0.2.13 webcolors-24.11.1 webencodings-0.5.1 websocket-client-1.8.0 werkzeug-3.1.3 whitebox-2.3.6 whiteboxgui-2.3.0 widgetsnbextension-4.0.14 xarray-2025.7.1 xxhash-3.5.0 xyzservices-2025.4.0 yarl-1.20.1

You can find some example on the Open Geospatial Solutions - the official youtube channel

Let's see my testing python example:

>>> import geoai
>>> dir(geoai)
['AgricultureFieldDelineator', 'Any', 'AutoConfig', 'AutoModelForMaskGeneration', 

'AutoModelForMaskedImageModeling', 'AutoProcessor', 'BoundingBox', 'BuildingFootprintExtractor',

 'CLIPSegForImageSegmentation', 'CLIPSegProcessor', 'CLIPSegmentation', 'CarDetector', 

'ChangeDetection', 'CustomDataset', 'DetectionResult', 'Dict', 'ET', 'GroundedSAM', 'Image', 

'Iterable', 'List', 'Map', 'MapLibre', 'MultiPolygon', 'NonGeoDataset', 'ObjectDetector', 

'Optional', 'OrderedDict', 'ParkingSplotDetector', 'Path', 'Polygon', 'RandomRotation', 

'ShipDetector', 'SolarPanelDetector', 'Tuple', 'Union', 'Window', '__author__', 

'__builtins__', '__cached__', '__doc__', '__email__', '__file__', '__loader__', '__name__', 

'__package__', '__path__', '__spec__', '__version__', 'adaptive_regularization', 

'add_geometric_properties', 'analyze_vector_attributes', 'batch_vector_to_raster', 'bbox_to_xy',

 'box', 'boxes_to_vector', 'calc_stats', 'change_detection', 'classify', 'classify_image', 

'classify_images', 'clip_raster_by_bbox', 'coords_to_xy', 'create_overview_image', 

'create_split_map', 'create_vector_data', 'csv', 'cv2', 'dataclass', 'deeplabv3_resnet50', 

'dict_to_image', 'dict_to_rioxarray', 'download', 'download_file', 'download_model_from_hf', 

'download_naip', 'download_overture_buildings', 'download_pc_stac_item', 'edit_vector_data', 

'export_geotiff_tiles', 'export_geotiff_tiles_batch', 'export_tiles_to_geojson', 

'export_training_data', 'extract', 'extract_building_stats', 'fasterrcnn_resnet50_fpn_v2', 

'fcn_resnet50', 'features', 'geoai', 'geojson_to_coords', 'geojson_to_xy', 'get_device', 

'get_instance_segmentation_model', 'get_model_config', 'get_model_input_channels', 

'get_overture_data', 'get_raster_info', 'get_raster_info_gdal', 'get_raster_resolution', 

'get_raster_stats', 'get_vector_info', 'get_vector_info_ogr', 'glob', 'gpd', 'hf', 

'hf_hub_download', 'hybrid_regularization', 'image_segmentation', 'inspect_pth_file', 

'install_package', 'instance_segmentation', 'instance_segmentation_batch', 

'instance_segmentation_inference_on_geotiff', 'json', 'leafmap', 'logging', 'maplibregl', 

'mapping', 'mask_generation', 'maskrcnn_resnet50_fpn', 'masks_to_vector', 'math', 

'mosaic_geotiffs', 'ndimage', 'np', 'object_detection', 'object_detection_batch', 'orthogonalize',

 'os', 'pc_collection_list', 'pc_item_asset_list', 'pc_stac_download', 'pc_stac_search', 'pd', 

'pipeline', 'plot_batch', 'plot_images', 'plot_masks', 'plot_performance_metrics', 

'plot_prediction_comparison', 'plt', 'print_raster_info', 'print_vector_info', 'raster_to_vector',

 'raster_to_vector_batch', 'rasterio', 'read_pc_item_asset', 'read_raster', 'read_vector', 

'region_groups', 'regularization', 'regularize', 'requests', 'rotate', 'rowcol_to_xy', 'rxr', 

'segment', 'semantic_segmentation', 'semantic_segmentation_batch', 'set_proj_lib_path', 'shape', 

'show', 'stack_bands', 'subprocess', 'sys', 'temp_file_path', 'time', 'torch', 'torchgeo', 'tqdm',

 'train', 'train_MaskRCNN_model', 'train_classifier', 'train_instance_segmentation_model', 

'train_segmentation_model', 'transform_bounds', 'try_common_architectures', 'utils', 

'vector_to_geojson', 'vector_to_raster', 'view_image', 'view_pc_item', 'view_pc_items', 

'view_raster', 'view_vector', 'view_vector_interactive', 'visualize_vector_by_attribute', 

'warnings', 'write_colormap', 'xr']

Python Qt6 : simple celtic knots tool with SVG file format.

Today, I try to create SVG file with an celtic knot design tool.

I used random values from -360 up to 360 for for Twist 1, Twist 2, and Twist 3 sliders.

The basic function is this, is created by artificial intelligence and not works very well.

        # Generate star polygon vertices
        points_cw = []
        points_ccw = []
        for i in range(steps):
            t = 2 * math.pi * i / steps
            r = outer_radius if i % 2 == 0 else inner_radius
            x_cw = center[0] + r * math.cos(t)
            y_cw = center[1] + r * math.sin(t)
            x_ccw = center[0] + r * math.cos(-t + math.pi / max(lobes, 1))
            y_ccw = center[1] + r * math.sin(-t + math.pi / max(lobes, 1))
            points_cw.append((x_cw, y_cw))
            points_ccw.append((x_ccw, y_ccw))

See one random example with this tool:

Python Qt6 : simple merge sprites images with unittest feature.

Today, I created one python tool script with the artificial intelligence to merge sprites images.

I used the artificial intelligence to add unittest to create default images with PIL to test the result.

You can select your folder , select the align of merge features or test with unittest button.

This is the result and works well:

import os
import unittest
from PIL import Image, ImageDraw, ImageFont, ImageQt
import shutil
from PyQt6.QtWidgets import QApplication, QMainWindow, QPushButton, QFileDialog, QLabel, QVBoxLayout, QWidget, QComboBox
from PyQt6.QtGui import QPixmap
from PyQt6.QtCore import Qt
import sys

def create_test_image(path, size, number):
    img = Image.new('RGBA', size, (255, 255, 255, 255))
    draw = ImageDraw.Draw(img)
    # Simplified to just draw the number with a basic color background
    draw.rectangle((0, 0, size[0], size[1]), fill=(0, 100 * number % 255, 0, 255))
    try:
        font = ImageFont.load_default()
    except:
        font = None
    draw.text((size[0]//2-5, size[1]//2-5), str(number), fill=(255, 255, 255, 255), font=font)
    img.save(path, 'PNG')

def merge_sprites(folder_path, output_horizontal, output_vertical):
    images = [Image.open(os.path.join(folder_path, f)) for f in os.listdir(folder_path) if f.endswith(('.png', '.jpg', '.jpeg'))]
    
    if not images:
        return None, None
    
    width, height = images[0].size
    
    # Horizontal merge
    total_width = width * len(images)
    horizontal_image = Image.new('RGBA', (total_width, height))
    for i, img in enumerate(images):
        horizontal_image.paste(img, (i * width, 0))
    horizontal_image.save(output_horizontal, 'PNG')
    
    # Vertical merge
    total_height = height * len(images)
    vertical_image = Image.new('RGBA', (width, total_height))
    for i, img in enumerate(images):
        vertical_image.paste(img, (0, i * height))
    vertical_image.save(output_vertical, 'PNG')
    
    return horizontal_image, vertical_image

class TestSpriteMerger(unittest.TestCase):
    def setUp(self):
        self.test_folder = 'test_images'
        self.size = (50, 20)
        os.makedirs(self.test_folder, exist_ok=True)
        for i in range(3):
            create_test_image(os.path.join(self.test_folder, f'test_{i+1}.png'), self.size, i+1)
    
    def test_merge_horizontal(self):
        output_h = 'test_merged_horizontal.png'
        output_v = 'test_merged_vertical.png'
        h_img, _ = merge_sprites(self.test_folder, output_h, output_v)
        
        self.assertIsNotNone(h_img, "Horizontal merge failed")
        self.assertEqual(h_img.size, (self.size[0] * 3, self.size[1]))
    
    def test_merge_vertical(self):
        output_h = 'test_merged_horizontal.png'
        output_v = 'test_merged_vertical.png'
        _, v_img = merge_sprites(self.test_folder, output_h, output_v)
        
        self.assertIsNotNone(v_img, "Vertical merge failed")
        self.assertEqual(v_img.size, (self.size[0], self.size[1] * 3))
    
    def tearDown(self):
        if os.path.exists(self.test_folder):
            shutil.rmtree(self.test_folder)
        for f in ['test_merged_horizontal.png', 'test_merged_vertical.png']:
            if os.path.exists(f):
                os.remove(f)

class SpriteMergerApp(QMainWindow):
    def __init__(self):
        super().__init__()
        self.setWindowTitle("Sprite Merger")
        self.setGeometry(100, 100, 800, 600)
        
        self.folder_path = ""
        
        layout = QVBoxLayout()
        
        self.select_button = QPushButton("Select Folder")
        self.select_button.clicked.connect(self.select_folder)
        layout.addWidget(self.select_button)
        
        self.merge_type = QComboBox()
        self.merge_type.addItems(["Horizontal", "Vertical"])
        layout.addWidget(self.merge_type)
        
        self.process_button = QPushButton("Process Selected Folder")
        self.process_button.clicked.connect(self.process_folder)
        layout.addWidget(self.process_button)
        
        self.test_button = QPushButton("Run Unit Test")
        self.test_button.clicked.connect(self.run_unit_test)
        layout.addWidget(self.test_button)
        
        self.result_label = QLabel("No image processed")
        layout.addWidget(self.result_label)
        
        self.image_label = QLabel()
        layout.addWidget(self.image_label)
        
        container = QWidget()
        container.setLayout(layout)
        self.setCentralWidget(container)
    
    def select_folder(self):
        self.folder_path = QFileDialog.getExistingDirectory(self, "Select Sprite Folder")
        self.result_label.setText(f"Selected: {self.folder_path}")
    
    def process_folder(self):
        if not self.folder_path:
            self.result_label.setText("Please select a folder first")
            return
        h_img, v_img = merge_sprites(self.folder_path, 'merged_horizontal.png', 'merged_vertical.png')
        selected_type = self.merge_type.currentText()
        
        img = h_img if selected_type == "Horizontal" else v_img
        if img:
            pixmap = QPixmap.fromImage(ImageQt.ImageQt(img))
            self.image_label.setPixmap(pixmap.scaled(700, 500, aspectRatioMode=Qt.AspectRatioMode.KeepAspectRatio))
            self.result_label.setText(f"{selected_type} merge completed")
        else:
            self.result_label.setText(f"{selected_type} merge failed")
    
    def run_unit_test(self):
        suite = unittest.TestLoader().loadTestsFromTestCase(TestSpriteMerger)
        result = unittest.TextTestRunner().run(suite)
        
        test_folder = 'test_images'
        os.makedirs(test_folder, exist_ok=True)
        for i in range(3):
            create_test_image(os.path.join(test_folder, f'test_{i+1}.png'), (50, 20), i+1)
        
        h_img, v_img = merge_sprites(test_folder, 'test_merged_horizontal.png', 'test_merged_vertical.png')
        selected_type = self.merge_type.currentText()
        
        img = h_img if selected_type == "Horizontal" else v_img
        if img:
            pixmap = QPixmap.fromImage(ImageQt.ImageQt(img))
            self.image_label.setPixmap(pixmap.scaled(700, 500, aspectRatioMode=Qt.AspectRatioMode.KeepAspectRatio))
            self.result_label.setText(f"Unit tests: {result.testsRun} run, {len(result.failures)} failed, showing {selected_type.lower()} merge")
        else:
            self.result_label.setText(f"Unit tests: {result.testsRun} run, {len(result.failures)} failed, merge failed")

if __name__ == '__main__':
    app = QApplication(sys.argv)
    window = SpriteMergerApp()
    window.show()
    sys.exit(app.exec())

Python 3.13.5 : keras and torch and tensorflow on python version 3.13.5 !

I am not very satisfied with the security of the system in this area, I have noticed intrusions when I was at the Vodafone provider and now I am on mobile and they persist. I should improve my software and hardware protection and restart the system, but I have the impression that it won't help much considering the hacking capabilities that exist.

For now, I'm sticking with this solution, because it's quite good for simple tests with the Python language.

Today I will show you that the involvement of developers is somewhat lagging behind, and this can be seen from the analysis of the development progression across versions of the Python language, as it is not easy to implement in newer versions. Some packages, although important, are not available in newer versions.Today I will show you that the involvement of developers is somewhat lagging behind, and this can be seen from the analysis of the development progression across versions of the Python language, as it is not easy to implement in newer versions. Some packages, although important, are not available in newer versions. Let's see these python packages:

Keras is a deep learning API designed for human beings, not machines. Keras focuses on debugging speed, code elegance & conciseness, maintainability, and deployability. When you choose Keras, your codebase is smaller, more readable, easier to iterate on.

... read more on the official website.

pip install --upgrade keras
Collecting keras
  Downloading keras-3.10.0-py3-none-any.whl.metadata (6.0 kB)
...
Successfully installed absl-py-2.3.0 h5py-3.14.0 keras-3.10.0 ml-dtypes-0.5.1 namex-0.1.0 optree-0.16.0

TensorFlow does not support Python 3.13 as of July 2025; it supports Python 3.9–3.11. To use Keras with TensorFlow, downgrade to Python 3.11, and this install will comes with:

pip install tensorflow
ERROR: Could not find a version that satisfies the requirement tensorflow (from versions: none)
ERROR: No matching distribution found for tensorflow

You can use the torch python package:

pip install torch
Collecting torch
...
Successfully installed filelock-3.18.0 fsspec-2025.5.1 mpmath-1.3.0 sympy-1.14.0 torch-2.7.1

... during the test with these packages in running it seems that I have errors. I believe that those who develop Python didn't really have a strategy regarding the protection of installed packages because I've found all sorts of junk packages that can be installed with pip or from GitHub.

Python 3.13.5 : the groq tested with basic example !

About groq you can find on the official Grok website .

Today I tested the groq A.P.I. with the basic example :

import os

from groq import Groq

client = Groq(
    api_key="gsk_..."
)

chat_completion = client.chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "explain what groq is",
        }
    ],
    model="llama-3.3-70b-versatile",
)

print(chat_completion.choices[0].message.content)

The result of : "explain what groq is" is that :

Groq is a relatively new and innovative technology company that focuses on developing high-performance, artificial intelligence (AI) computing hardware and software solutions. Founded in 2016 by Jonathan Ross, a former Google engineer, Groq aims to provide faster, more efficient, and scalable AI computing capabilities to various industries. Groq's primary product is a tensor processing unit (TPU) chip designed specifically for machine learning (ML) and deep learning (DL) workloads. This TPU is optimized for high-performance, low-latency, and low-power consumption, making it suitable for applications that require rapid processing of vast amounts of data, such as: 1. **Computer Vision**: Image and video recognition, object detection, and tracking. 2. **Natural Language Processing (NLP)**: Text analysis, sentiment analysis, and language translation. 3. **Speech Recognition**: Voice command processing and speech-to-text applications. Groq's innovative architecture and design philosophy are centered around the following key aspects: 1. **High-Bandwidth Memory**: Groq's TPU features high-bandwidth memory, which enables fast data transfer and processing. 2. **Scalable Architecture**: The TPU is designed to scale horizontally, allowing for easy expansion to meet growing computational demands. 3. **Low-Latency**: Groq's architecture minimizes latency, ensuring fast response times and real-time processing capabilities. 4. **Software-Defined**: The TPU is software-defined, allowing for flexibility and customization to support a wide range of AI applications and frameworks. Groq's technology has the potential to accelerate AI adoption in various industries, including: 1. **Autonomous Vehicles**: Enhanced computer vision and sensor processing for safer and more efficient autonomous driving. 2. **Healthcare**: Faster medical image analysis and diagnosis, as well as improved personalized medicine and treatment planning. 3. **Financial Services**: Enhanced risk analysis, portfolio optimization, and fraud detection using AI-powered systems. While Groq is still a relatively new company, its innovative approach to AI computing has garnered significant attention and interest from the tech industry, investors, and potential customers. As AI continues to transform various aspects of our lives, Groq's technology is poised to play a significant role in shaping the future of artificial intelligence and machine learning.

Python Qt6 : ... simple resize image files.

I like the combination of Python development and the inclusion of the PyQt6 module. It is very fast and stable and allows me to create all sorts of tools to use.

Today I will show you another handy script that allows you to read all the image files from a folder and, depending on the selections: height, length, and/or aspect ratio, resize them and then place them in a folder created specifically for the resulting images.

Here is how the script looks, I clearly used artificial intelligence and it didn't take more than a few minutes, my evaluation, testing, and rearranging the interface took longer ...

import sys
import os
from datetime import datetime
from PyQt6.QtWidgets import QApplication, QMainWindow, QWidget, QVBoxLayout, QPushButton, QFileDialog, QLineEdit, QCheckBox, QLabel, QMessageBox
from PyQt6.QtCore import Qt
from PIL import Image

class ResizeApp(QMainWindow):
    def __init__(self):
        super().__init__()
        self.setWindowTitle("Image Resizer")
        self.setGeometry(100, 100, 400, 200)

        layout = QVBoxLayout()
        central_widget = QWidget()
        central_widget.setLayout(layout)
        self.setCentralWidget(central_widget)

        self.folder_button = QPushButton("Select Folder")
        self.folder_button.clicked.connect(self.select_folder)
        layout.addWidget(self.folder_button)

        self.width_edit = QLineEdit("800")
        self.width_edit.setPlaceholderText("Width (px)")
        layout.addWidget(QLabel("Width:"))
        layout.addWidget(self.width_edit)

        self.height_edit = QLineEdit("600")
        self.height_edit.setPlaceholderText("Height (px)")
        layout.addWidget(QLabel("Height:"))
        layout.addWidget(self.height_edit)

        self.aspect_ratio = QCheckBox("Maintain Aspect Ratio")
        self.aspect_ratio.setChecked(True)
        layout.addWidget(self.aspect_ratio)

        self.resize_button = QPushButton("Resize Images")
        self.resize_button.clicked.connect(self.resize_images)
        layout.addWidget(self.resize_button)

        self.folder_path = ""

    def select_folder(self):
        self.folder_path = QFileDialog.getExistingDirectory(self, "Select Image Folder")
        if self.folder_path:
            self.folder_button.setText(f"Selected: {os.path.basename(self.folder_path)}")

    def resize_images(self):
        if not self.folder_path:
            QMessageBox.warning(self, "Error", "Please select a folder.")
            return

        try:
            width = int(self.width_edit.text())
            height = int(self.height_edit.text())
        except ValueError:
            QMessageBox.warning(self, "Error", "Please enter valid width and height.")
            return

        if width <= 0 or height <= 0:
            QMessageBox.warning(self, "Error", "Width and height must be positive.")
            return

        date_str = datetime.now().strftime("%d%m%y_%H%M")
        aspect_str = "asp_on" if self.aspect_ratio.isChecked() else "asp_off"
        output_folder = os.path.join(self.folder_path, f"resized_{date_str}_{height}_{aspect_str}")
        os.makedirs(output_folder, exist_ok=True)

        for file_name in os.listdir(self.folder_path):
            if file_name.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.gif')):
                image_path = os.path.join(self.folder_path, file_name)
                try:
                    with Image.open(image_path) as img:
                        if self.aspect_ratio.isChecked():
                            img.thumbnail((width, height), Image.Resampling.LANCZOS)
                        else:
                            img = img.resize((width, height), Image.Resampling.LANCZOS)
                        output_path = os.path.join(output_folder, f"resized_{date_str}_{height}_{aspect_str}_{file_name}")
                        img.save(output_path)
                except Exception as e:
                    QMessageBox.warning(self, "Error", f"Failed to process {file_name}: {str(e)}")

        QMessageBox.information(self, "Success", f"Images resized and saved to {output_folder}!")

if __name__ == '__main__':
    app = QApplication(sys.argv)
    window = ResizeApp()
    window.show()
    sys.exit(app.exec())

Python 3.13.5 : use the jupyterlab, notebook and voila - part 001.

JupyterLab is the latest web-based interactive development environment for notebooks, code, and data. Its flexible interface allows users to configure and arrange workflows in data science, scientific computing, computational journalism, and machine learning. A modular design invites extensions to expand and enrich functionality.

Let's test with these python modules: jupyterlab, notebook and voila.

First, I install with pip tool the jupyterlab python module:

pip install jupyterlab
Collecting jupyterlab
...
Successfully installed anyio-4.9.0 argon2-cffi-25.1.0 argon2-cffi-bindings-21.2.0 arrow-1.3.0 asttokens-3.0.0 async-lru-2.0.5
attrs-25.3.0 babel-2.17.0 bleach-6.2.0 cffi-1.17.1 comm-0.2.2 debugpy-1.8.14 defusedxml-0.7.1 executing-2.2.0 
fastjsonschema-2.21.1 fqdn-1.5.1 h11-0.16.0 httpcore-1.0.9 httpx-0.28.1 ipykernel-6.29.5 ipython-9.4.0 
ipython-pygments-lexers-1.1.1 isoduration-20.11.0 jedi-0.19.2 json5-0.12.0 jsonpointer-3.0.0 jsonschema-4.24.0 
jsonschema-specifications-2025.4.1 jupyter-client-8.6.3 jupyter-core-5.8.1 jupyter-events-0.12.0 jupyter-lsp-2.2.5 
jupyter-server-2.16.0 jupyter-server-terminals-0.5.3 jupyterlab-4.4.4 jupyterlab-pygments-0.3.0 jupyterlab-server-2.27.3 
matplotlib-inline-0.1.7 mistune-3.1.3 nbclient-0.10.2 nbconvert-7.16.6 nbformat-5.10.4 nest-asyncio-1.6.0 notebook-shim-0.2.4
overrides-7.7.0 pandocfilters-1.5.1 parso-0.8.4 platformdirs-4.3.8 prometheus-client-0.22.1 prompt_toolkit-3.0.51 
psutil-7.0.0 pure-eval-0.2.3 pycparser-2.22 python-json-logger-3.3.0 pywin32-310 pywinpty-2.0.15 pyyaml-6.0.2 pyzmq-27.0.0
referencing-0.36.2 rfc3339-validator-0.1.4 rfc3986-validator-0.1.1 rpds-py-0.26.0 send2trash-1.8.3 sniffio-1.3.1 
stack_data-0.6.3 terminado-0.18.1 tinycss2-1.4.0 tornado-6.5.1 traitlets-5.14.3 types-python-dateutil-2.9.0.20250516 
uri-template-1.3.0 wcwidth-0.2.13 webcolors-24.11.1 webencodings-0.5.1 websocket-client-1.8.0

Next, the install of the notebook python module

pip install notebook
Collecting notebook
...
Installing collected packages: notebook
Successfully installed notebook-7.4.4

This python package named voila will help us to use online graphic user interfaces:

pip install voila
Collecting voila
...
Successfully installed voila-0.5.8 websockets-15.0.1

The voila package need to work with ipywidgets python package:

pip install ipywidgets
Collecting ipywidgets
...
Successfully installed ipywidgets-8.1.7 jupyterlab_widgets-3.0.15 widgetsnbextension-4.0.14

Let's start the jupiter tool with this command:

jupyter notebook

I used an default python example with a slider:

import ipywidgets as widgets
from IPython.display import display

slider = widgets.IntSlider(value=5, min=0, max=10)
display(slider)

This command will start the web with the slider using the voila command:

voila test.ipynb

The result is this: