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	import skops
	import sklearn
	import matplotlib.pyplot as plt
	from sklearn.preprocessing import OneHotEncoder
	from sklearn.impute import SimpleImputer
	from sklearn.compose import ColumnTransformer
	from sklearn.tree import DecisionTreeClassifier
	from sklearn.pipeline import Pipeline

	# preprocess the dataset

	df = pd.read_csv("../input/tabular-playground-series-aug-2022/train.csv")


	column_transformer_pipeline = ColumnTransformer([
	("loading_missing_value_imputer", SimpleImputer(strategy="mean"), ["loading"]),
	("numerical_missing_value_imputer", SimpleImputer(strategy="mean"), list(df.columns[df.dtypes == 'float64'])),
	("attribute_0_encoder", OneHotEncoder(categories = "auto"), ["attribute_0"]),
	("attribute_1_encoder", OneHotEncoder(categories = "auto"), ["attribute_1"]),
	("product_code_encoder", OneHotEncoder(categories = "auto"), ["product_code"])])

	df = df.drop(["id"], axis=1)


	pipeline = Pipeline([
	('transformation', column_transformer_pipeline),
	('model', DecisionTreeClassifier(max_depth=4))
	])

	X = df.drop(["failure"], axis = 1)
	y = df.failure

	# split the data and train the model

	from sklearn.model_selection import train_test_split
	X_train, X_test, y_train, y_test = train_test_split(X, y)
	pipeline.fit(X_train, y_train)

	# we will now use skops to initialize a repository
	# create a model card, and push the model to the
	# Hugging Face Hub
	from skops import card, hub_utils
	import pickle

	model_path = "model.pkl"
	local_repo = "decision-tree-playground-kaggle"

	# save the model
	with open(model_path, mode="bw") as f:
	pickle.dump(pipeline, file=f)

	# initialize the repository
	hub_utils.init(
	model=model_path,
	requirements=[f"scikit-learn={sklearn.__version__}"],
	dst=local_repo,
	task="tabular-classification",
	data=X_test,
	)

	# initialize the model card
	from pathlib import Path
	model_card = card.Card(pipeline, metadata=card.metadata_from_config(Path(local_repo)))

	## let's fill some information about the model
	limitations = "This model is not ready to be used in production."
	model_description = "This is a DecisionTreeClassifier model built for Kaggle Tabular Playground Series August 2022, trained on supersoaker production failures dataset."
	model_card_authors = "huggingface"
	get_started_code = f"import pickle \nwith open({local_repo}/{model_path}, 'rb') as file: \n clf = pickle.load(file)"

	# pass this information to the card
	model_card.add(
	get_started_code=get_started_code,
	model_card_authors=model_card_authors,
	limitations=limitations,
	model_description=model_description,
	)

	# we will now evaluate the model and write eval results to the card
	from sklearn.metrics import accuracy_score, f1_score, ConfusionMatrixDisplay, confusion_matrix
	model_card.add(eval_method="The model is evaluated using test split, on accuracy and F1 score with micro average.")
	model_card.add_metrics(accuracy=accuracy_score(y_test, y_pred))
	model_card.add_metrics(**{"f1 score": f1_score(y_test, y_pred, average="micro")})

	model = pipeline.steps[-1][1]

	# we will plot the tree and add the plot to our card
	from sklearn.tree import plot_tree
	plt.figure()
	plot_tree(model,filled=True)
	plt.savefig(f'{local_repo}/tree.png',format='png',bbox_inches = "tight")

	# let's make a prediction and evaluate the model
	y_pred = pipeline.predict(X_test)
	cm = confusion_matrix(y_test, y_pred, labels=model.classes_)
	disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model.classes_)
	disp.plot()

	# save the plot
	plt.savefig(Path(local_repo) / "confusion_matrix.png")

	# add figures to model card with their new sections as keys to the dictionary
	model_card.add_plot(**{"Tree Plot": f'{local_repo}/tree.png', "Confusion Matrix": f"{local_repo}/confusion_matrix.png"})

	#save the card
	model_card.save(f"{local_repo}/README.md")

	# we can now push the model!
	# if the repository doesn't exist remotely on the Hugging Face Hub, it will be created when we set create_remote to True
	repo_id = "scikit-learn/tabular-playground"
	hub_utils.push(
	repo_id=repo_id,
	source=local_repo,
	token=token,
	commit_message="pushing files to the repo from the example!",
	create_remote=True,
	)