Unit Testing with Pytest

The unit testing framework in GentelmeN-CorE is designed to validate the core logic of the bot, including database interactions, third-party API integrations, and command handling. By utilizing pytest alongside the standard unittest library and extensive mocking, the project ensures that individual components function correctly in isolation without requiring active connections to external services like Telegram, KuCoin, or Hugging Face.

The test suite serves as a critical quality assurance tool, covering edge cases such as network failures, missing API tokens, and unauthorized access attempts. Developers can execute the tests using the command python -m pytest tests/ -v to get a verbose output of the validation process.

Sources: tests/test_commands.py:1-6, requirements.txt:6

Test Architecture and Components

The testing infrastructure relies on mocking external dependencies to simulate real-world scenarios. This is primarily achieved through the unittest.mock module, which allows the replacement of complex objects like the Telegram bot instance or API clients with MagicMock objects.

Core Testing Modules

The test suite is organized into sections mirroring the project's internal structure:

• Helpers: Validates message delivery logic and parameter forwarding.
• Database: Ensures table initialization, data caching, and graceful failure handling when the database is unavailable.
• Integrations: Tests the logic for interacting with Brave Search and Hugging Face image generation.
• Commands: Validates bot commands, authorization checks, and voice message processing.

Sources: tests/test_commands.py:14-386

Mocking Strategy

The project uses decorators like @patch to intercept calls to external libraries such as requests, psycopg2, and tempfile.

Testing Database Operations

The TestInitDb, TestGetUserData, and TestUpdateUserDb classes verify that the bot interacts correctly with the PostgreSQL backend. The tests ensure that the system defaults to sensible values (e.g., English language and specific AI models) if the database connection fails or if a user is not found.

Sources: tests/test_commands.py:46-121, core/database.py:34-71

Integration and API Testing

Testing for integrations.py focuses on handling diverse API responses, including successful data retrieval, HTTP errors, and timeouts.

Image Generation and Search

The TestGenerateImageHf class simulates Hugging Face API interactions. It tests successful PNG byte returns and various failure modes like a missing HF_TOKEN or a 503 Service Unavailable status.

Command and Authorization Testing

The TestCheckBalance and TestHandleVoice classes are critical for verifying security and complex workflows.

Authorization Logic

Tests verify that sensitive commands like /balance are restricted based on the OWNER_USER_ID environment variable or a fallback username check.

• Authorized: If the user_id matches OWNER_USER_ID, access is granted.
• Unauthorized: If credentials do not match, the bot replies with "Brak dostępu" (Access Denied).

Sources: tests/test_commands.py:205-257, core/commands.py:82-99

Voice Processing Workflow

The handle_voice test validates the temporary file lifecycle and routing. It ensures that voice transcriptions starting with "rysuj" are correctly routed to the image generation logic.

Summary of Testing Utilities

The project defines a utility function _make_message within the test suite to quickly generate mock Telegram message objects with configurable parameters like user_id, chat_id, and text. This streamlines the setup phase for command tests.

Sources: tests/test_commands.py:198-206