Enhanced Course Table Script: Topic Correspondence enhancement using AI, Handling of Errors and Improvement of Code #44
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This commit introduces advanced AI features into the course table generation script. Key updates include: 1. AI-Driven Related Topics Identification: - Implemented TF-IDF vectorization and cosine similarity to identify and display related topics for each course. This enhancement helps users find similar courses based on the course description. - Added a new `DESCRIPTION_COLUMN_NAME` to handle course descriptions for the AI model. 2. Updated Table Formatting: - Modified the `format_entry` function to include a new column in the markdown table that lists related topics for each course. This provides more context and helps in discovering relevant courses. 3. Error Handling Improvement: - Replaced generic exceptions with more specific ones to ensure better error reporting and debugging. 4. File Handling and Code Clean-up: - Renamed variables to avoid shadowing built-in names, ensuring better code readability and adherence to best practices. - Enhanced the `search_lines_with_token` function for efficiency and accuracy in locating the auto-generated table markers. 5. Documentation and Usage: - Updated README and comments to reflect the new AI features and usage instructions. - Added warnings to the README file to prevent manual editing of the auto-generated table. These improvements significantly enhance the functionality and usability of the script, leveraging AI to provide a more informative and user-friendly course table. .
Enhance Course Table with AI-Driven Related Topics and Improved Formatting
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Description
The Course Table Generation Script has also seen immense evolution especially in the use of Artificial intelligence as well as better handling of errors and organization of the code. A new AI engine has been incorporated to compare course descriptions and to generate topics of interest using TF-IDF vectorization and cosine similarity. This improvement helps the users to search for the courses that relate with their interest in a better manner. The table format of README file was expanded to include another column for these related topics to make it easier for users to learn more about the courses.
Besides these AI based features, the error handling of the script has also been enhanced by replacing a general exception with more specific one which makes the script more reliable and informative when errors occur. The error messages have been improved as well to give the user more useful information on what to do. Also, the code was modified to solve the problem of variable shadowing and enhance code quality in terms of clarity and reusability. Some changes were made to the names of variables with an aim of making the script as clear as possible and reduce possibilities of making mistakes due to use of similar names. Altogether, the changes described in this paper enhance the script’s functionality, usability, and readiness for future extension.
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