About the Agent

The Code Documentation Generator Agent automates the process of generating detailed and accurate documentation for software codebases. Using GenAI, this agent analyzes the source code and generates documentation, including descriptions of functions, classes, and modules. It ensures that all code is well-documented, making it easier for developers to understand and maintain the software. By automating documentation generation, this agent reduces the manual effort required by developers and ensures that documentation remains up-to-date with code changes. This improves code maintainability and reduces the time spent on manual documentation tasks, ultimately enhancing the overall quality and efficiency of the software development lifecycle. The agent seamlessly integrates with popular development tools and platforms, ensuring that it works within the existing workflows of development teams. This integration ensures that documentation generation occurs automatically during code updates, reducing manual intervention and maintaining consistency. Additionally, the agent incorporates a human feedback loop, allowing developers to review and refine the generated documentation. This feature ensures that the documentation remains highly relevant and tailored to the specific needs of the project, enabling continuous improvement in documentation quality and accuracy.

module: data_processing.py

class DataProcessor:
    """
    Class for processing data for machine learning models.
    """

    def __init__(self, data):
        """
        Initialize the DataProcessor with the dataset.

        Args:
            data (list): A list of data points.
        """
        self.data = data
        self.cleaned_data = None
        self.transformed_data = None

    def clean_data(self):
        """
        Cleans the dataset by removing invalid entries.

        Returns:
            list: Cleaned data.
        """
        self.cleaned_data = [d for d in self.data if self.is_valid(d)]
        return self.cleaned_data

    def is_valid(self, data_point):
        """
        Check if the data point is valid.

        Args:
            data_point (dict): A single data point.

        Returns:
            bool: True if valid, False otherwise.
        """
        return 'value' in data_point

    def normalize_data(self):
        """
        Normalize the cleaned data to have values between 0 and 1.

        Returns:
            list: Normalized data.
        """
        if self.cleaned_data is None:
            self.cleaned_data = self.clean_data()
        max_value = max(d['value'] for d in self.cleaned_data)
        self.transformed_data = [{'value': d['value'] / max_value} for d in self.cleaned_data]
        return self.transformed_data

    def save_data(self, filename):
        """
        Save the processed data to a CSV file.

        Args:
            filename (str): The file name to save the data to.
        """
        with open(filename, 'w') as f:
            for item in self.transformed_data:
                f.write(f"{item['value']}
")

module: model.py

class MachineLearningModel:
    """
    A machine learning model for classification.
    """

    def __init__(self, model_name):
        """
        Initialize the machine learning model.

        Args:
            model_name (str): The name of the model.
        """
        self.model_name = model_name
        self.parameters = {}
        self.is_trained = False

    def train(self, data):
        """
        Train the model using the provided dataset.

        Args:
            data (list): Training data.
        """
        if not data:
            raise ValueError("Training data cannot be empty.")
        print(f"Training {self.model_name} model with {len(data)} data points.")
        self.is_trained = True
        self.parameters = {"accuracy": 0.9, "loss": 0.2}

    def predict(self, new_data):
        """
        Make predictions on new data.

        Args:
            new_data (list): New data for predictions.

        Returns:
            list: Predicted values.
        """
        if not self.is_trained:
            raise RuntimeError("Model must be trained before making predictions.")
        return [1 if d['value'] > 0.5 else 0 for d in new_data]

    def evaluate(self, test_data):
        """
        Evaluate the model's performance on test data.

        Args:
            test_data (list): Test dataset.

        Returns:
            dict: Evaluation metrics including accuracy and loss.
        """
        predictions = self.predict(test_data)
        correct_predictions = sum([1 for i, d in enumerate(test_data) if d['value'] > 0.5 == predictions[i]])
        accuracy = correct_predictions / len(test_data)
        loss = 0.3  # This would be calculated dynamically in a real scenario
        return {"accuracy": accuracy, "loss": loss}

    def save_model(self, file_path):
        """
        Save the model parameters to a file.

        Args:
            file_path (str): Path to save the model.
        """
        if not self.is_trained:
            raise RuntimeError("Cannot save an untrained model.")
        with open(file_path, 'w') as f:
            f.write(str(self.parameters))

module: evaluation.py

def evaluate_model_performance(model, test_data):
    """
    Evaluate the performance of the given model on the test data.

    Args:
        model (MachineLearningModel): The machine learning model to evaluate.
        test_data (list): The test dataset.

    Returns:
        dict: Evaluation results including accuracy and loss.
    """
    return model.evaluate(test_data)


def generate_evaluation_report(evaluation_results, report_file):
    """
    Generate a markdown report of the evaluation results.

    Args:
        evaluation_results (dict): The evaluation metrics (accuracy, loss, etc.).
        report_file (str): The filename for the generated report.
    """
    with open(report_file, 'w') as f:
        f.write("# Model Evaluation Report
")
        f.write(f"Accuracy: {evaluation_results['accuracy']:.2f}
")
        f.write(f"Loss: {evaluation_results['loss']:.2f}
")

Code Documentation