# example_model.pkl

# This is a placeholder file for a pickled machine learning model.
# In a real-world scenario, this file would contain the serialized representation
# of a trained machine learning model using the `pickle` library.

# This model is used by the ai-ethics-validator plugin to demonstrate
# how to load and use a model for fairness validation.

# INSTRUCTIONS:
# 1. Replace this placeholder with your actual trained model.
# 2. Ensure the model is compatible with the `validate-ethics` command
#    in the plugin.  The command expects the model to have a `predict` method
#    that takes input data and returns predictions.
# 3. Update the `validate_ethics` function in the plugin's main script
#    to correctly load and use your model.
# 4. Consider using a model that can be easily validated for bias, such as
#    a logistic regression or decision tree.

# Example of how to create a dummy model (FOR TESTING ONLY):
# import pickle
# from sklearn.linear_model import LogisticRegression
# from sklearn.datasets import make_classification
#
# # Generate a synthetic dataset
# X, y = make_classification(n_samples=100, n_features=2, random_state=42)
#
# # Train a logistic regression model
# model = LogisticRegression(random_state=42)
# model.fit(X, y)
#
# # Save the model to a file
# with open("example_model.pkl", "wb") as f:
#     pickle.dump(model, f)

# Placeholder content to prevent errors if the file is not replaced.
# In a real application, this would be replaced with the pickled model.
# Replace this with the actual pickled model data.
class PlaceholderModel:
    def predict(self, data):
        # Placeholder prediction logic
        return [0] * len(data)

import pickle
model = PlaceholderModel()
with open("example_model.pkl", "wb") as f:
    pickle.dump(model, f)


# END OF FILE