The 2025 election for the PSF Board :
- Abigail Dogbe
- Jannis Leidel
- Sheena O’Connell
- Simon Willison
Is a blog about python programming language. You can see my work with python programming language, tutorials and news.
import subprocess
import sys
import os
import shutil
import importlib.util
import re
import concurrent.futures
from typing import List, Tuple, Set
class ModuleManager:
def __init__(self):
self.modules: Set[str] = set()
self.pip_path = self._get_pip_path()
def _get_pip_path(self) -> str:
possible_path = os.path.join(sys.exec_prefix, "Scripts", "pip.exe")
return shutil.which("pip") or (possible_path if os.path.exists(possible_path) else None)
def extract_imports_from_file(self, file_path: str) -> List[Tuple[str, str]]:
imports = []
try:
with open(file_path, 'r', encoding='utf-8') as file:
for line in file:
# Detect 'import module'
import_match = re.match(r'^\s*import\s+([a-zA-Z0-9_]+)(\s+as\s+.*)?$', line)
if import_match:
module = import_match.group(1)
imports.append((module, line.strip()))
continue
# Detect 'from module import ...'
from_match = re.match(r'^\s*from\s+([a-zA-Z0-9_]+)\s+import\s+.*$', line)
if from_match:
module = from_match.group(1)
imports.append((module, line.strip()))
except FileNotFoundError:
print(f"❌ Fișierul {file_path} nu a fost găsit.")
except Exception as e:
print(f"❌ Eroare la citirea fișierului {file_path}: {e}")
return imports
def scan_directory_for_py_files(self, directory: str = '.') -> List[str]:
py_files = []
for root, _, files in os.walk(directory):
for file in files:
if file.endswith('.py'):
py_files.append(os.path.join(root, file))
return py_files
def collect_unique_modules(self, directory: str = '.') -> None:
py_files = self.scan_directory_for_py_files(directory)
all_imports = []
with concurrent.futures.ThreadPoolExecutor() as executor:
future_to_file = {executor.submit(self.extract_imports_from_file, file_path): file_path for file_path in py_files}
for future in concurrent.futures.as_completed(future_to_file):
imports = future.result()
all_imports.extend(imports)
for module, _ in all_imports:
self.modules.add(module)
def is_module_installed(self, module: str) -> bool:
return importlib.util.find_spec(module) is not None
def run_pip_install(self, module: str) -> bool:
if not self.pip_path:
print(f"❌ Nu am găsit pip pentru {module}.")
return False
try:
subprocess.check_call([self.pip_path, "install", module])
print(f"✅ Pachetul {module} a fost instalat cu succes.")
return True
except subprocess.CalledProcessError as e:
print(f"❌ Eroare la instalarea pachetului {module}: {e}")
return False
def check_and_install_modules(self) -> None:
def process_module(module):
print(f"\n🔎 Verific dacă {module} este instalat...")
if self.is_module_installed(module):
print(f"✅ {module} este deja instalat.")
else:
print(f"📦 Instalez {module}...")
self.run_pip_install(module)
# Re-verifică după instalare
if self.is_module_installed(module):
print(f"✅ {module} funcționează acum.")
else:
print(f"❌ {module} nu funcționează după instalare.")
with concurrent.futures.ThreadPoolExecutor() as executor:
executor.map(process_module, self.modules)
def main():
print("🔍 Verific pip...")
manager = ModuleManager()
if manager.pip_path:
print(f"✅ Pip este disponibil la: {manager.pip_path}")
else:
print("⚠️ Pip nu este disponibil.")
return
directory = sys.argv[1] if len(sys.argv) > 1 else '.'
print(f"\n📜 Scanez directorul {directory} pentru fișiere .py...")
manager.collect_unique_modules(directory)
if not manager.modules:
print("⚠️ Nu s-au găsit module în importuri.")
return
print(f"\nModule unice detectate: {', '.join(manager.modules)}")
manager.check_and_install_modules()
if __name__ == "__main__":
main()
import torch
import torch.nn as nn
import numpy as np
data = np.array([
[1800.5, 1810.0, 1795.0, 1000, 1805.2],
[1805.2, 1815.0, 1800.0, 1200, 1812.8],
[1812.8, 1820.0, 1808.0, 1100, 1810.5],
[1810.5, 1818.0, 1805.0, 1300, 1825.0],
[1825.0, 1830.0, 1815.0, 1400, 1820.3],
[1820.3, 1828.0, 1810.0, 1250, 1835.7]
])
X, y = torch.tensor(data[:, :4], dtype=torch.float32), torch.tensor(data[:, 4], dtype=torch.float32)
model = nn.Sequential(nn.Linear(4, 6), nn.ReLU(), nn.Linear(6, 4), nn.ReLU(), nn.Linear(4, 1))
optimizer = torch.optim.Adam(model.parameters())
loss_fn = nn.MSELoss()
for _ in range(3000):
optimizer.zero_grad()
y_pred = model(X).squeeze()
loss = loss_fn(y_pred, y)
loss.backward()
optimizer.step()
prediction = model(torch.tensor([[1830.0, 1840.0, 1825.0, 1150]], dtype=torch.float32))
print("Predicted XAU/USD closing price:", round(prediction.item(), 2))
python torch_001.py
Predicted XAU/USD closing price: 1819.57
import tensorflow as tf
import numpy as np
data = np.array([
[1800.5, 1810.0, 1795.0, 1000, 1805.2],
[1805.2, 1815.0, 1800.0, 1200, 1812.8],
[1812.8, 1820.0, 1808.0, 1100, 1810.5],
[1810.5, 1818.0, 1805.0, 1300, 1825.0],
[1825.0, 1830.0, 1815.0, 1400, 1820.3],
[1820.3, 1828.0, 1810.0, 1250, 1835.7]
])
X, y = data[:, :4], data[:, 4]
model = tf.keras.Sequential([
tf.keras.layers.Dense(6, activation='relu', input_shape=(4,)),
tf.keras.layers.Dense(4, activation='relu'),
tf.keras.layers.Dense(1)
])
model.compile(optimizer='adam', loss='mse')
model.fit(X, y, epochs=3000, verbose=0)
prediction = model.predict(np.array([[1830.0, 1840.0, 1825.0, 1150]]))
print("Predicted XAU/USD closing price:", round(prediction[0][0], 2))
python tf_001.py
2025-08-30 21:11:13.966066: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
C:\Python313\Lib\site-packages\google\protobuf\runtime_version.py:98: UserWarning: Protobuf gencode version 5.28.3 is exactly one major version older than the runtime version 6.31.1 at tensorflow/core/framework/attr_value.proto. Please update the gencode to avoid compatibility violations in the next runtime release.
...
Predicted XAU/USD closing price: 2.9
from sklearn.neural_network import MLPRegressor
import numpy as np
data = np.array([
[1800.5, 1810.0, 1795.0, 1000, 1805.2],
[1805.2, 1815.0, 1800.0, 1200, 1812.8],
[1812.8, 1820.0, 1808.0, 1100, 1810.5],
[1810.5, 1818.0, 1805.0, 1300, 1825.0],
[1825.0, 1830.0, 1815.0, 1400, 1820.3],
[1820.3, 1828.0, 1810.0, 1250, 1835.7]
])
X, y = data[:, :4], data[:, 4]
model = MLPRegressor(hidden_layer_sizes=(6, 4), max_iter=3000)
model.fit(X, y)
prediction = model.predict([[1830.0, 1840.0, 1825.0, 1150]])
print("Predicted XAU/USD closing price:", round(prediction[0], 2))
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
>>> 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']
import json
from pathlib import Path
bookmark_path = Path.home() / "AppData/Local/Microsoft/Edge/User Data/Default/Bookmarks"
with open(bookmark_path, "r", encoding="utf-8") as f:
data = json.load(f)
# Exemplu: listăm toate titlurile bookmark-urilor
def extract_bookmarks(bookmark_node):
bookmarks = []
if "children" in bookmark_node:
for child in bookmark_node["children"]:
bookmarks.extend(extract_bookmarks(child))
elif bookmark_node.get("type") == "url":
bookmarks.append((bookmark_node["name"], bookmark_node["url"]))
return bookmarks
all_bookmarks = extract_bookmarks(data["roots"]["bookmark_bar"])
for name, url in all_bookmarks:
print(f"{name}: {url}")
pip install flask
Collecting flask
Downloading flask-3.1.1-py3-none-any.whl.metadata (3.0 kB)
...
Installing collected packages: markupsafe, itsdangerous, blinker, werkzeug, jinja2, flask
Successfully installed blinker-1.9.0 flask-3.1.1 itsdangerous-2.2.0 jinja2-3.1.6 markupsafe-3.0.2 werkzeug-3.1.3
pip install requests
...
Installing collected packages: urllib3, idna, charset_normalizer, certifi, requests
Successfully installed certifi-2025.6.15 charset_normalizer-3.4.2 idna-3.10 requests-2.32.4 urllib3-2.5.0
pip install playwright
Collecting playwright
...
Installing collected packages: pyee, greenlet, playwright
Successfully installed greenlet-3.2.3 playwright-1.52.0 pyee-13.0.0
...
playwright install
Downloading Chromium 136.0.7103.25 ...
from flask import Flask, request
app = Flask(__name__)
@app.route("/")
def detecteaza_ipuri():
ip_client = request.headers.get('CF-Connecting-IP', 'Necunoscut')
ip_cloudflare = request.remote_addr
return (
f"IP real vizitator: {ip_client}
"
f"IP Cloudflare (vizibil de server): {ip_cloudflare}"
)
if __name__ == "__main__":
app.run(debug=True)
from flask import Flask, request
import requests
import ipaddress
app = Flask(__name__)
def este_ip_cloudflare(ip):
try:
raspuns = requests.get("https://www.cloudflare.com/ips-v4")
raspuns.raise_for_status()
subneturi = raspuns.text.splitlines()
for subnet in subneturi:
if ipaddress.ip_address(ip) in ipaddress.ip_network(subnet):
return True
return False
except Exception as e:
return f"Eroare la verificarea IP-ului Cloudflare: {e}"
@app.route("/")
def detecteaza_ipuri():
ip_client = request.headers.get('CF-Connecting-IP', 'Necunoscut')
ip_cloudflare = request.remote_addr
rezultat = este_ip_cloudflare(ip_cloudflare)
return (
f"IP real vizitator: {ip_client}
"
f"IP Cloudflare (forwarder): {ip_cloudflare}
"
f"Este IP-ul din rețeaua Cloudflare? {'DA' if rezultat == True else 'NU' if rezultat == False else rezultat}"
)
if __name__ == "__main__":
app.run(host="0.0.0.0", port=5000)
import requests
url = "https://cobalt.tools"
headers = {
"User-Agent": "Mozilla/5.0", # Simulează un browser real
}
try:
r = requests.get(url, headers=headers, timeout=10)
content = r.text.lower()
print(f"Cod răspuns HTTP: {r.status_code}")
if "cloudflare" in content or "cf-ray" in content or "attention required" in content:
print("Cloudflare a intermediat cererea sau a blocat-o cu o pagină specială.")
else:
print("Cererea a fost servită normal.")
except Exception as e:
print(f"Eroare la conexiune: {e}")
import sys
import re
from urllib.parse import urlparse
from pathlib import Path
from playwright.sync_api import sync_playwright
def converteste_url_in_nume_fisier(url):
parsed = urlparse(url)
host = parsed.netloc.replace('.', '_')
path = parsed.path.strip('/').replace('/', '_')
if not path:
path = 'index'
return f"{host}_{path}.txt"
if len(sys.argv) != 2:
print("Utilizare: python script.py https://exemplu.com")
sys.exit(1)
url = sys.argv[1]
fisier_output = converteste_url_in_nume_fisier(url)
with sync_playwright() as p:
browser = p.chromium.launch(headless=True)
pagina = browser.new_page()
pagina.goto(url, wait_until='networkidle')
continut = pagina.content()
Path(fisier_output).write_text(continut, encoding='utf-8')
browser.close()
print(f"Conținutul a fost salvat în: {fisier_output}")
cd uv_projects
uv_projects>uv init hello-world
Initialized project `hello-world` at `D:\PythonProjects\uv_projects\hello-world`
uv_projects>cd hello-world
uv_projects\hello-world>uv run main.py
Using CPython 3.13.5 interpreter at: C:\Python3135\python.exe
Creating virtual environment at: .venv
Hello from hello-world!
Installing collected packages: tqdm, tifffile, scipy, rpds-py, platformdirs, opencv-python-headless, networkx, llvmlite, lazy-loader, imageio, attrs, scikit-image, referencing, pooch, numba, pymatting, jsonschema-specifications, jsonschema, rembg
---------------------------------------- 0/19 [tqdm] WARNING: The script tqdm.exe is installed in 'C:\Users\nicol\AppData\Roaming\Python\Python313\Scripts' which is not on PATH.
Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
-- ------------------------------------- 1/19 [tifffile] WARNING: The scripts lsm2bin.exe, tiff2fsspec.exe, tiffcomment.exe and tifffile.exe are installed in 'C:\Users\nicol\AppData\Roaming\Python\Python313\Scripts' which is not on PATH.
Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
WARNING: The scripts imageio_download_bin.exe and imageio_remove_bin.exe are installed in 'C:\Users\nicol\AppData\Roaming\Python\Python313\Scripts' which is not on PATH.
Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. ----------
----------------------------------- ---- 17/19 [jsonschema] WARNING: The script jsonschema.exe is installed in 'C:\Users\nicol\AppData\Roaming\Python\Python313\Scripts' which is not on PATH.
Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
----------------------------------- ---- 17/19 [jsonschema] WARNING: The script rembg.exe is installed in 'C:\Users\nicol\AppData\Roaming\Python\Python313\Scripts' which is not on PATH.
Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
Successfully installed attrs-25.3.0 imageio-2.37.0 jsonschema-4.23.0 jsonschema-specifications-2025.4.1 lazy-loader-0.4 llvmlite-0.44.0 networkx-3.4.2 numba-0.61.2 opencv-python-headless-4.11.0.86 platformdirs-4.3.8 pooch-1.8.2 pymatting-1.1.14 referencing-0.36.2 rembg-2.0.66 rpds-py-0.25.0 scikit-image-0.25.2 scipy-1.15.3 tifffile-2025.5.10 tqdm-4.67.1
D:\PythonProjects\PyQt6\catafest_images_viewer>python main_001_removebk.py
Traceback (most recent call last):
File "D:\PythonProjects\PyQt6\catafest_images_viewer\main_001_removebk.py", line 4, in
from image_viewer import ImageViewer
File "D:\PythonProjects\PyQt6\catafest_images_viewer\image_viewer.py", line 7, in
from qt_setup import Image, ContextMenu, ProcessDialog, ProcessingManager, ImageProcessor
File "D:\PythonProjects\PyQt6\catafest_images_viewer\qt_setup.py", line 5, in
from processing import ProcessDialog, ProcessingManager, ImageProcessor
File "D:\PythonProjects\PyQt6\catafest_images_viewer\processing.py", line 7, in
from rembg import remove
File "C:\Users\nicol\AppData\Roaming\Python\Python313\site-packages\rembg\__init__.py", line 5, in
from .bg import remove
File "C:\Users\nicol\AppData\Roaming\Python\Python313\site-packages\rembg\bg.py", line 7, in
import onnxruntime as ort
ModuleNotFoundError: No module named 'onnxruntime'
pip install gTTS
Collecting gTTS
Downloading gTTS-2.5.4-py3-none-any.whl.metadata (4.1 kB)
...
Installing collected packages: gTTS
Successfully installed gTTS-2.5.4
from gtts import gTTS
tts = gTTS('Azi este 25 aprilie 2025', lang='ro', tld='ro')
tts.save('azi25.mp3')