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inference_prototype_test

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inference_prototype_test / handler.py

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Create handler.py

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	from typing import Dict, Any
	from sklearn.model_selection import train_test_split

	from sklearn.datasets import make_classification
	from sklearn.linear_model import LogisticRegression
	from sklearn.model_selection import train_test_split
	from sklearn.metrics import classification_report



	class EndpointHandler:
	def __init__(self, path: str):
	# model_dir = os.getenv("HF_MODEL_DIR", ".")
	#
	# with open(os.path.join(model_dir, "model.pkl"), "rb") as f:
	# self.model = pickle.load(f)
	#
	# # optional: you could also load a vocabulary or vectorizer
	# with open(os.path.join(model_dir, "tokenizer.pkl"), "rb") as f:
	# self.vectorizer = pickle.load(f)

	# 1. Generate synthetic binary classification data
	X, y = make_classification(n_samples=100, n_features=4, n_classes=2, random_state=42)

	# 2. Split into train/test sets
	X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

	# 3. Create and train the Logistic Regression model
	self.model = LogisticRegression()
	self.model.fit(X_train, y_train)



	def __call__(self, inputs: Dict[str, Any]) -> Dict[str, str]:
	# Expecting input like: {"inputs": "<html>...</html>"}
	html = inputs["inputs"]
	return {"label": str(1)}