Jean-Michel Daignan jeanmidevacc

Prompt

Help me build a Jekyll website with the following requirements:

Personal blog / portfolio for a Data Scientist / Machine Learning Engineer
Articles support categories and tags
Each article has a cover image visible in article listing pages
A search feature allowing search by article title and tags

	# to run all the tests in the test folder
	pytest

	> tests/integration/test_agent_play.py ........ [ 3%]
	tests/integration/test_game_flow.py ...................... [ 12%]
	tests/integration/test_logging.py ....... [ 15%]
	tests/unit/agents/test_base.py ....... [ 18%]
	tests/unit/dungeon/test_card_pool.py ......................... [ 28

	### Part around unit tests

	import pytest

	from pyscoundrel.models.card import Card, CardType

	pytestmark = pytest.mark.unit

	class TestCardFromDungeonCard:
	def test_creates_card_with_correct_fields(self):

	# Check dist are ok
	twine check dist/*

	# Upload to pypi (when local setup is done , advice use api way)
	twine upload dist/*

	# upload to test pypi
	twine upload --repository testpypi dist/*

	"""Game engine for PyScoundrel."""

	from typing import TYPE_CHECKING, Optional

	from ..dungeon import Dungeon
	from ..models import Card, CardType, Deck, Player, Room, Weapon
	from .actions import ActionResult
	from .state import GamePhase, GameState

	if TYPE_CHECKING:

	# basic cmd
	python -m build

	# Select the outputs
	python -m build --wheel # wheel only (faster, skips sdist)
	python -m build --sdist # sdist only


	## A lot of logs diverse to have finiw with that in a dist/ folder
	Successfully built pyscoundrel-0.1.7.tar.gz and pyscoundrel-0.1.7-py3-none-any.whl

	from voyager import Index, Space

	# sharing also the chunking function
	def chunk_dfp(df, chunk_size):
	"""Yield successive n-sized chunks from df."""
	for i in range(0, len(df), chunk_size):
	yield df.iloc[i:i + chunk_size]

	index = Index(
	space=Space.Cosine,

	# How to colect table versions
	query = f"""
	SELECT *
	FROM (DESCRIBE HISTORY name_of_the_table)
	WHERE timestamp >= '{start_date.strftime('%Y-%m-%d %H:%M:%S')}';
	"""

	# How to get a specific version
	dfs_recommendations = spark.read.format("delta").option("versionAsOf", version_id).table(f"name_of_the_table")