Delete init_repo_MLstructureMining.py

init_repo_MLstructureMining.py

@@ -1,86 +0,0 @@
-import json
-import os
-import pickle
-from pathlib import Path
-from tempfile import mkdtemp, mkstemp
-from uuid import uuid4
-
-import numpy as np
-import xgboost
-from xgboost import XGBClassifier
-
-import sklearn
-from huggingface_hub import HfApi
-from sklearn.datasets import load_breast_cancer
-from sklearn.ensemble import HistGradientBoostingClassifier
-from sklearn.experimental import enable_halving_search_cv  # noqa
-from sklearn.model_selection import HalvingGridSearchCV, train_test_split
-import shutil
-from skops import card, hub_utils
-from data_loader import get_data_splits_from_clean_data
-# Paths
-model_path = "MLstructureMining_model.bin"
-label_path = "labels.csv"
-data_path = "./cifs_test_s_trained_model"
-
-train_tuple = get_data_splits_from_clean_data(
-    data_path, label_path, simple_load=True, n_data=-1
-)
-print(train_tuple)
-X_test = train_tuple[0]
-
-booster = xgboost.Booster({'nthread': 8})
-booster.load_model(model_path)
-
-model = XGBClassifier()
-
-# Set the booster
-model._Booster = booster
-
-local_repo = mkdtemp(prefix="skops-")
-hub_utils.init(
-    model=model_path,
-    requirements=[f"xgboost={xgboost.__version__}"],
-    dst=local_repo,
-    task="tabular-classification",
-    data=X_test,
-)
-
-shutil.copy(label_path, os.path.join(local_repo, label_path))
-if "__file__" in locals():  # __file__ not defined during docs built
-    # Add this script itself to the files to be uploaded for reproducibility
-    hub_utils.add_files(__file__, dst=local_repo)
-
-print(os.listdir(local_repo))
-print(type(model))
-
-card.metadata_from_config(Path(local_repo))["model_type"] = "xgboost"
-model_card = card.Card(model, metadata=card.metadata_from_config(Path(local_repo)))
-model_card.add(**{"model_type": "xgboost"})
-model_card.save(Path(local_repo) / "README.md")
-model_card.save("README.md")
-
-with open(os.path.join(local_repo, "config.json"), "r") as file:
-    data = json.load(file)
-data["model_type"] = "xgboost"
-with open(os.path.join(local_repo, "config.json"), "w") as file:
-    json.dump(data, file, indent=4)
-
-# you can put your own token here, or set it as an environment variable before
-# running this script.
-token = os.environ["HF_HUB_TOKEN"]
-
-repo_name = f"MLstructureMining"
-user_name = HfApi().whoami(token=token)["name"]
-repo_id = f"{user_name}/{repo_name}"
-print(f"Creating and pushing to repo: {repo_id}")
-
-
-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,
-    private=True,
-)