# let's import the libraries first
import sklearn
from sklearn.datasets import load_breast_cancer
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from skops import card, hub_utils
import pickle
from sklearn.metrics import (ConfusionMatrixDisplay, confusion_matrix,
                            accuracy_score, f1_score)
import matplotlib.pyplot as plt
from pathlib import Path

# Load the data and split
X, y = load_breast_cancer(as_frame=True, return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42
)

# Train the model
model = DecisionTreeClassifier().fit(X_train, y_train)

# let's save the model
model_path = "example.pkl"
local_repo = "my-awesome-model"
with open(model_path, mode="bw") as f:
    pickle.dump(model, file=f)

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


# create the card 
model_card = card.Card(model, metadata=card.metadata_from_config(Path(destination_folder)))

limitations = "This model is not ready to be used in production."
model_description = "This is a DecisionTreeClassifier model trained on breast cancer dataset."
model_card_authors = "skops_user"
get_started_code = "import pickle \nwith open(dtc_pkl_filename, 'rb') as file: \n    clf = pickle.load(file)"
citation_bibtex = "bibtex\n@inproceedings{...,year={2020}}"

# we can add the information using add
model_card.add(
    citation_bibtex=citation_bibtex,
    get_started_code=get_started_code,
    model_card_authors=model_card_authors,
    limitations=limitations,
    model_description=model_description,
)

# we can set the metadata part directly
model_card.metadata.license = "mit"

# let's make a prediction and evaluate the model
y_pred = model.predict(X_test)

# we can pass metrics using add_metrics and pass details with add
model_card.add(eval_method="The model is evaluated using test split, on accuracy and F1 score with macro 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")})

# we will create a confusion matrix
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")

# the plot will be written to the model card under the name confusion_matrix
# we pass the path of the plot itself
model_card.add_plot(confusion_matrix="confusion_matrix.png")

# save the card
model_card.save(Path(local_repo) / "README.md")

# 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 = "skops-user/my-awesome-model"
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,
)