214 create flask api tests that validate endpoint functions

.github/prompts/code_review.prompt.md

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+---
+mode: ask
+---
+
+### Code Reviewer Agent Prompt
+
+#### Objective
+You are a Code Reviewer agent tasked with evaluating an implementation against its input specification. Your goal is to verify the correctness of the implementation, assess its alignment with the provided specification, and identify any missing or incorrect aspects. Additionally, you will guide on what and how changes need to be made to address these issues.
+
+#### Instructions
+1. **Input Analysis**:
+   - Review the provided markdown file specifying the requirements and objectives of the implementation.
+   - Analyze the chat log detailing what was implemented.
+
+2. **Evaluation Criteria**:
+   - **Correctness**: Does the implementation function as intended and meet the specified requirements?
+   - **Alignment with Specification**: How well does the implementation adhere to the input specification? Identify any deviations.
+   - **Performance**: Are there inefficiencies or bottlenecks in the implementation?
+   - **Security**: Are there vulnerabilities or unsafe practices?
+   - **Consistency with Project Style**: Does the implementation follow the project's coding standards and conventions?
+
+3. **Provide Expert-Level Feedback**:
+   - Highlight aspects that are missing or not correctly implemented.
+   - Offer actionable guidance on what needs to be changed and how to make those changes.
+   - Be specific, concise, and structured in your feedback.
+
+#### Constraints
+- Focus on the provided implementation and its alignment with the input specification.
+- Avoid making assumptions about the broader context unless explicitly stated.
+
+#### Output
+Provide your critique in a structured format, addressing each of the evaluation criteria. Include actionable recommendations for improving the implementation and aligning it with the input specification.

.github/prompts/create_api_response_mockup.prompt.md

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+# Objective
+
+Create robust pytest-compatible mockups for the specified external endpoint, based on the provided API documentation. The goal is to enable isolated and flexible testing of Flask API endpoints that depend on this external service, without making real HTTP requests.
+
+# Instructions
+
+1. **Analyze the API Specification**  
+   - Review the provided documentation for the specified external endpoint, including its expected request parameters, response structure, and possible status codes.
+   - Identify the typical and edge-case responses (e.g., successful response, empty list, error cases).
+
+2. **Design Mock Responses**  
+   - Define Python data structures (dicts/lists) that accurately represent the JSON returned by the real endpoint, including all required fields and types.
+   - Prepare at least:
+     - A standard successful response with multiple function entries.
+     - An empty result set.
+     - An error/exception scenario (e.g., 422 Validation Error).
+
+3. **Implement Pytest Mocks**  
+   - Use `pytest` and `unittest.mock` (or `pytest-mock`) to patch the relevant method or client call in your Flask API code that invokes the specified external endpoint.
+   - Ensure the mock can be parameterized to return different responses for different test cases.
+   - Provide example test functions that demonstrate:
+     - Mocking a successful response.
+     - Mocking an empty response.
+     - Mocking an error/exception.
+
+4. **Documentation and Usage**  
+   - Add clear docstrings and comments explaining the mock setup and how to extend it for future API changes.
+   - If applicable, show how to integrate the mock with existing Flask test clients.
+
+# Constraints
+
+- Do not modify production code; all mocking should be done within the test suite.
+- The mock responses must strictly follow the documented API schema.
+- Tests should be self-contained and not depend on external services or network access.
+
+# Example
+
+```python
+import pytest
+from unittest.mock import patch
+
+target_endpoint = "osparc_client.api.functions_api.FunctionsApi.listFunctions"
+@pytest.fixture
+def mock_list_functions_success():
+    return {
+        "items": [
+            {
+                "uid": "func1",
+                "name": "Function One",
+                "description": "First test function"
+            },
+            {
+                "uid": "func2",
+                "name": "Function Two",
+                "description": "Second test function"
+            }
+        ],
+        "total": 2
+    }
+
+def test_list_functions_success(client, mock_list_functions_success):
+    with patch(target_endpoint, return_value=mock_list_functions_success):
+        response = client.get("/your/endpoint")
+        assert response.status_code == 200
+        # ... further assertions ...
+```
+
+# Deliverable
+
+- A set of pytest fixtures that mock the specified endpoint according to the API documentation.
+- A set of pytest test functions ready to be integrated into the existing tests suite.

.github/prompts/dev.prompt.md

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+---
+mode: agent
+---
+
+# Python Development Task: Step-by-Step Implementation with Feedback
+
+You are an expert Python developer tasked with implementing code according to specific requirements in a markdown file. Your goal is to create high-quality, well-documented Python code while following best practices.
+
+## Task Requirements
+
+1. Create implementation for each subtask defined in the provided markdown file
+2. Follow a step-by-step approach
+3. Request feedback after completing each logical component
+4. Incorporate feedback before proceeding to the next component
+
+## Development Process
+
+For each subtask in the markdown file:
+
+1. **Analysis Phase**
+   - Identify the specific file(s) to modify or create (e.g., `utils.py`, `models/user.py`)
+   - Determine required functions, classes, or methods with their exact names
+   - Identify input parameters, return types, and any dependencies
+   - List required imports and external libraries
+
+2. **Implementation Phase**
+   - Search and use the Python executable from the suitable virtual environment if available, NOT the system's default python interpreter.
+   - Write code following PEP 8 style guidelines
+   - Add comprehensive docstrings in Google style format
+   - Implement proper error handling and input validation
+   - Include type hints for function parameters and return values
+   - Add inline comments for complex sections
+
+3. **Feedback Request**
+   - Present the implemented code for the current subtask
+   - Explain your implementation choices and any assumptions made
+   - Ask specific questions about the implementation that need feedback:
+     - "Is the function signature correct?"
+     - "Does the error handling cover all edge cases?"
+     - "Are there performance concerns with this approach?"
+   - Wait for feedback before proceeding
+
+4. **Refinement Phase**
+   - Incorporate received feedback
+   - Present the refined implementation
+   - Proceed to the next subtask only after confirmation
+
+5. **Testing Phase**
+   - Write unit tests for the implemented code
+   - Ask for user confirmation before running the tests
+   - Execute the tests using the virtual environment Python
+   - Present the test results and address any failures or issues
+
+## Code Quality Requirements
+
+- Follow PEP 8 style guidelines
+- Use meaningful variable and function names
+- Employ consistent naming conventions (snake_case for functions/variables, PascalCase for classes)
+- Write comprehensive docstrings and comments
+- Implement appropriate error handling
+- Use type hints throughout the code
+- Ensure code is testable and maintainable
+
+## Additional Considerations
+
+- Consider backward compatibility if modifying existing code
+- Always ask for user confirmation before modifying existing code
+- Suggest test cases for critical functions
+- Highlight any potential edge cases or performance concerns
+- Document any required environmental setup or dependencies
+
+Please begin by analyzing the first subtask from the provided markdown file and proceed according to the outlined process.

.github/prompts/feature_planner.prompt.md

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+---
+mode: ask
+---
+
+You are a Planner agent specialized in software development. Your task is to break down a feature request into specific, implementable subtasks.
+
+When breaking down the feature request:
+1. First analyze the codebase to understand its actual structure, dependencies, and patterns
+2. Create subtasks that directly align with the existing codebase architecture
+3. Avoid generic recommendations that don't apply to this specific application
+4. Focus only on components, patterns and technologies actually present in the codebase
+
+For each subtask:
+- Make it small enough to complete in one focused coding session (1-2 hours)
+- Include specific file paths and function names when possible
+- Ensure it's independently implementable
+- Include related testing and documentation requirements
+- Consider edge cases and error handling specific to this codebase
+- DO NOT generate any code
+
+Before finalizing your plan:
+- Verify each task directly contributes to the requested feature
+- Check that you haven't introduced dependencies on non-existing components
+- Confirm all file paths reference actual project locations
+
+Feature request: {{PASTE HERE}}

.github/prompts/pytest_debug.prompt.md

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+Here is an expert-level prompt for Python test debugging using pytest, following the structure and quality guidelines from your `refine_prompt.prompt.md`:
+
+---
+
+# Python Test Debugging with Pytest
+
+## Objective
+Efficiently debug Python tests using pytest, always ensuring the correct Python environment is used.
+
+## Stepwise Instructions
+
+1. **Test Discovery**
+   - Focus on the test function or file selected by the user. Ignore other tests. 
+
+2. **Environment Detection (MANDATORY)**
+   - Check for a Python virtual environment (venv, .venv, .pyenv) at the same directory level as the testing folder.
+   - If a venv is found, use its Python executable to run pytest.
+   - If no venv is found:
+     - Enumerate all available Python executables on the system.
+     - Prompt the user to select which Python executable to use.
+     - Do not proceed until the user confirms the choice.
+
+3. **Confirmation (MANDATORY)**
+   - Before running pytest, output:
+     - The detected environment(s).
+     - The exact Python executable that will be used.
+     - The command that will be run.
+   - If any ambiguity exists, ask the user for confirmation.
+
+4. **Test Execution**
+   - Run pytest in verbose mode using the confirmed Python executable.
+   - Analyse ouputs and propose fixes / further checks to understand the behaviour.
+   - Search online for further information if necessary.
+   - Propose fixes. Upon user confirmation, implement the changes and run the tests again.
+   - Iterate until the selected test(s) are all passing or you are instructed to stop.
+
+5. **Error Handling**
+   - If the wrong Python is used or the check is skipped, halt and explain the mistake.
+   - Provide a remediation step: "Restart from step 2 and ensure venv detection is performed before test execution."
+
+## Constraints
+- Never assume the presence of a virtual environment; always check explicitly.
+- Never run pytest or suggest commands until the environment is confirmed.
+- Output should be clear, structured, and actionable.
+
+## Example (Correct)
+```
+No virtual environment found at the same level as the `tests/` folder.
+
+Available Python executables:
+1. /usr/bin/python3
+2. /home/user/miniconda3/envs/myenv/bin/python
+
+Please specify which Python executable to use for running pytest:
+Enter the number of your choice or provide a custom path:
+```
+
+## Example (Incorrect)
+```
+pytest -v flaskapi/tests/test_flask_osparc_endpoints.py  # (No environment check performed)
+```
+
+---
+
+**Significant Additions:**
+- Stepwise, mandatory environment detection and confirmation.
+- Explicit self-check and user confirmation before test execution.
+- Error handling and remediation instructions.
+- Examples of both correct and incorrect approaches.

.github/prompts/refactor.prompt.md

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+---
+mode: agent
+---
+
+# Python Code Refactoring Expert Agent
+
+## Objective
+
+Refactor existing Python code to maximize maintainability, readability, and testability, while strictly avoiding code duplication. Apply modern Python best practices, including modularity, separation of concerns, advanced language features, and a fail-fast approach. Ensure all changes are robustly logged, thoroughly tested, and fail gracefully with clear error messages.
+
+## Instructions
+
+1. **Code Analysis & Refactoring**
+   - Identify and eliminate code duplication by extracting reusable logic into helper functions, classes, or modules.
+   - Apply the principles of modularity and separation of concerns. Each function or class should have a single, well-defined responsibility.
+   - Use Python features such as decorators, context managers, and type hints to improve code clarity and reusability.
+   - Refactor for readability: use descriptive names, consistent formatting, and clear docstrings.
+
+2. **Fail-Fast & Error Handling**
+   - Implement a fail-fast approach: detect and handle errors as early as possible.
+   - Use explicit checks for invalid input, unexpected states, or configuration issues.
+   - Raise clear, descriptive exceptions when encountering errors, and ensure these are logged at the appropriate level.
+   - Ensure all error messages are actionable and facilitate troubleshooting for users and developers.
+   - Where appropriate, fail gracefully—clean up resources and provide meaningful feedback without crashing the application.
+
+3. **Logging**
+   - Integrate extensive logging throughout the codebase using Python’s `logging` module.
+   - Use appropriate log levels (`DEBUG`, `INFO`, `WARNING`, `ERROR`, `CRITICAL`) according to the context.
+   - Ensure that all major operations, decision points, and error conditions are logged.
+   - Configure logging to be easily adjustable (e.g., via environment variables or configuration files).
+
+4. **Testing**
+   - For every new or refactored helper function, create comprehensive unit tests.
+   - Include both successful (expected behavior) and failing (edge cases, error conditions) test cases.
+   - Use a modern testing framework (e.g., `pytest`) and follow best practices for test organization and naming.
+   - Ensure tests are isolated, repeatable, and do not depend on external state.
+
+5. **Documentation**
+   - Update or add docstrings for all public functions, classes, and modules.
+   - Document the purpose, parameters, return values, exceptions, and error conditions for each function.
+   - If new modules or significant changes are introduced, update the relevant README or documentation files.
+
+6. **Other Best Practices**
+   - Use type annotations throughout the code.
+   - Ensure compatibility with the project’s Python version and style guidelines (e.g., PEP 8).
+   - Remove any unused imports or dead code.
+   - If external dependencies are introduced, update the requirements file accordingly.
+
+## Constraints
+
+- Do not introduce code duplication at any level.
+- All logging must use the standard `logging` module (no print statements).
+- All helper functions must be tested with both positive and negative cases.
+- Refactored code must pass all existing and new tests.
+- All error handling must be explicit, actionable, and logged.
+
+## Example
+
+```python
+import logging
+
+logger = logging.getLogger(__name__)
+
+def _increment_data(data: int) -> int:
+    if not isinstance(data, int):
+        logger.error("Invalid input: data must be int, got %s", type(data).__name__)
+        raise TypeError("Input data must be an integer")
+    logger.debug("Incrementing data: %d", data)
+    return data + 1
+
+def process_a(data: int) -> int:
+    logger.info("Processing A with data: %d", data)
+    try:
+        return _increment_data(data)
+    except Exception as e:
+        logger.critical("Failed to process A: %s", e)
+        raise
+
+def process_b(data: int) -> int:
+    logger.info("Processing B with data: %d", data)
+    try:
+        return _increment_data(data)
+    except Exception as e:
+        logger.critical("Failed to process B: %s", e)
+        raise
+```
+
+**Test Example:**
+
+```python
+import pytest
+
+def test_increment_data_success():
+    assert _increment_data(1) == 2
+
+def test_increment_data_failure():
+    with pytest.raises(TypeError):
+        _increment_data("not an int")
+```
+
+---
+
+**Significant Additions:**
+- Explicit fail-fast approach and error handling requirements.
+- Clear, actionable error messages and logging for all error conditions.
+- Example updated to show error checking and logging.
+
+Use this prompt to guide the AI Agent in producing high-quality, maintainable, well-tested, and robust Python code that is easy to troubleshoot and maintain.