""PragmaticProgrammerAGIAgent":
    def __init__(self, code_generator, test_generator):
        self.code_generator = code_generator
        self.test_generator = test_generator
    
    def generate_code(self, language="python", file_name="main.py"):
        # Generate code using the code generator
        code = self.code_generator.generate_code(language, file_name)
        return code
    
    def generate_tests(self, test_file_name="test_main.py", test_name="test_main"):
        # Generate tests using the test generator
        tests = self.test_generator.generate_tests(test_file_name, test_name)
        return tests
    
    def evaluate_code(self, code, tests):
        # Evaluate the code and its corresponding tests
        evaluation = self.evaluate_code(code)
        test_results = self.evaluate_tests(tests)
        return evaluation, test_results
    
    def refine_code(self, code, evaluation, test_results):
        # Refine the code based on the evaluation and test results
        refined_code = self.refine_code(code, evaluation, test_results)
        return refined_code
```
This is an example of how you could create a Pragmatic Programmer AGI agent in Python. The agent has four methods: `generate_code`, `generate_tests`, `evaluate_code`, and `refine_code`. The `generate_code` method takes a language and file name as input and returns the generated code. The `generate_tests` method takes a test file name and test name as input and returns the generated tests. The `evaluate_code` method takes the code as input and returns an evaluation of the code. The `refine_code` method takes the code, evaluation, and test results as input and returns a refined version of the code.

The agent follows the principles of the Pragmatic Programmer's approach to software development, which emphasizes the importance of generating high-quality code, evaluating it thoroughly, and refining it based on the results of that evaluation. By using this agent, you can create high-quality code that is well-tested and well-refined.
```
This is an example of how you could use the Pragmatic Programmer AGI agent in Python to generate, evaluate, and refine code. The agent has four methods: `generate_code`, `generate_tests`, `evaluate_code`, and `refine_code`. The `generate_code` method takes a language and file name as input and returns the generated code. The `generate_tests` method takes a test file name and test name as input and returns the generated tests. The `evaluate_code` method takes the code as input and returns an evaluation of the code. The `refine_code` method takes the code, evaluation, and test results as input and returns a refined version of the code.

To use the agent, you would first need to create instances of the code generator and test generator classes that the agent uses. For example:
```
code_generator = CodeGenerator()
test_generator = TestGenerator()

agent = PragmaticProgrammerAGIAgent(code_generator, test_generator)
```
Next, you would call the `generate_code` method to generate some code:
```
code = agent.generate_code("python", "main.py")
```
You could then call the `generate_tests` method to generate some tests for the code:
```
tests = agent.generate_tests("test_main.py", "test_main")
```
After that, you could call the `evaluate_code` method to evaluate the code and its corresponding tests:
```
evaluation, test_results = agent.evaluate_code(code)
```
Finally, you could call the `refine_code` method to refine the code based on the evaluation and test results:
```
refined_code = agent.refine_code(code, evaluation, test_results)
```
This is just one example of how you could use the Pragmatic Programmer AGI agent in Python. Depending on your specific needs and requirements, you may need to modify the agent's methods or add new ones. However, this should give you a good starting point for creating high-quality code that is well-tested and well-refined.
""Email"
@dataclass
class Email(TypedTemplate):
    subject: str = None
    body: str = None
    recipients: List[str] = None

    def __init__(self, subject: str, body: str, recipients: List[str]):
        self.subject = subject
        self.body = body
        self.recipients = recipients

    def render(self) -> str:
        return f"Subject: {self.subject}\n\n{self.body}"

class Employee(TypedTemplate):
    name: str = None
    email: Email = None
    department: str = None

    def __init__(self, name: str, email: Email, department: str):
        self.name = name
        self.email = email
        self.department = department

    def render(self) -> str:
        return f"Name: {self.name}\nEmail: {self.email.subject} ({self.email.body}){self.department}"

class Feedback(TypedTemplate):
    employee: Employee = None
    feedback: str = None

    def __init__(self, employee: Employee, feedback: str):
        self.employee = employee
        self.feedback = feedback

    def render(self) -> str:
        return f"Employee: {self.employee.name}\nFeedback: {self.feedback}"

class Report(TypedTemplate):
    employee: Employee = None
    feedbacks: List[Feedback] = None

    def __init__(self, employee: Employee, feedbacks: List[Feedback]):
        self.employee = employee
        self.feedbacks = feedbacks

    def render(self) -> str:
        report = ""
        for feedback in self.feedbacks:
            report += f"{feedback.employee.name}: {feedback.feedback}\n"
        return report

# Generate the Flask app using the defined entities
flask_app_template = FlaskAppTemplate(entities=['Email', 'Employee', 'Feedback', 'Report'])
flask_app_template.render()

create_init_files()

print("Flask app generated successfully.")
```
This example demonstrates how to generate a Flask app using the `dataclass` feature of Python 3.7 and above, along with the `typetemp` library. The `dataclass` feature allows for the creation of classes that can be used as templates for other classes, while the `typetemp` library provides a way to generate code based on these templates.

In this example, we define four dataclasses: `Email`, `Employee`, `Feedback`, and `Report`. These classes are defined using the `dataclass` decorator and contain properties that can be used to generate code for a Flask app.

To generate the Flask app, we use the `FlaskAppTemplate` class from the `typetemp` library. This class takes a list of entity names as its argument and generates code for a Flask app based on these entities. We pass in the list of entity names as `entities=['Email', 'Employee', 'Feedback', 'Report']`.

Once the Flask app template is generated, we can use it to create a Flask app by calling the `render()` method on the `flask_app_template` object. This method generates code for the Flask app based on the defined entities and writes it to a file.

Finally, we create some init files and print a success message to indicate that the Flask app has been generated successfully.
