Implementing a Spam Detection Subnet with Bittensor for the Datura Challenge

[mdominguez56]

Pull Request Description

In this pull request, I've introduced a subnet using Bittensor, tailored for spam detection tasks. This update encompasses various enhancements and new features detailed below:

• DevContainers Setup: I configured a DevContainers setup to streamline the development process. This provides a pre-configured Ubuntu 22 environment with Python, Rust, and tmux, ensuring immediate access to all necessary development tools and reducing setup time.
• spamdetection/ Directory Creation: I created a spamdetection/ directory, evolving from the template/ directory. This houses the updated protocol definitions for miner and validator interactions within the spam detection system.
• Spam Detection Utility Function: I introduced a function in spamdetection/utils/is_spam.py to identify potential spam messages. It's important to emphasize that this function is a not meant to be used in a real case scenario. It is not an indicative of a final implementation for spam detection. Comprehensive comments within the file suggest how a more advanced approach could be developed.
• Miner and Validator Updates: I updated both the miner and validator to adhere to the new protocol. These modifications ensure our system processes and responds to spam detection queries.
• Test Suite Updates: I revised the existing test suite to work with the new interface introduced by the spamdetection/ directory.