[WIP] feat: Add class and prompts for issue label generator

[MashyBasker]

Enhance Issue Analysis and Improve Output Formatting

Overview

This pull request introduces a new module for issue analysis, enhancing the functionality for generating issue labels and descriptions based on provided prompts. It also includes minor formatting improvements across existing code for better readability.

Changes

• Key Changes:

  • Added a new file issue_analysis.py that contains the IssueAnalysisGenerator class for generating issue labels and descriptions using a language model.
  • Introduced two new data classes, IssueLabelOutput and IssueDescOutput, to encapsulate the outputs of the label and description generation processes.
  • Implemented methods generate_issue_labels and generate_issue_desc to handle the respective functionalities, including error handling for missing inputs.

• New Features:

  • The IssueAnalysisGenerator class provides functionality to generate labels and descriptions for GitHub issues, utilizing prompts defined in issue_analysis_prompts.py.
  • New prompts for issue analysis have been added, allowing for structured feedback generation based on issue titles and descriptions.

• Refactoring:

  • Improved string formatting in existing print statements across multiple files for consistency and readability.
  • Added a placeholder function create_issue_description in output.py for future implementation, ensuring the structure is in place for generating issue descriptions.

Additional Notes

• Error Handling: The new methods include checks for missing issue labels and descriptions, raising exceptions as necessary. This is a good practice to ensure that the methods fail gracefully when provided with invalid input.

• Logging: The use of logging within the processing methods enhances traceability and debugging capabilities, which is beneficial for maintaining the code.

• Maintainability: The introduction of data classes improves the organization of output data, making it easier to manage and extend in the future.

• Performance: The methods for processing issues leverage the language model efficiently, but consider implementing caching mechanisms if the same issues are processed frequently to reduce API calls.

• Testing: Ensure that unit tests are created for the new functionalities in issue_analysis.py to validate the behavior and output of the new methods.

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Original Description

[sauravpanda]

Is this task ready for review or are we waiting on anything specific?

[MashyBasker]

The issue analysis generation is done. I'm still working on adding it to the github app.

[sauravpanda]

Maybe we should merge this and create a separate task for github app?

[MashyBasker]

That sounds good. I'll put this PR up for review.

[MashyBasker]

@sauravpanda Do I need to make anymore changes to this PR? I'm still working on the GitHub app integration for issue analysis and will make a PR soon.

[kaizen-bot[bot]]

🔍 Code Review Summary

✅ All Clear: This commit looks good! 👍

📊 Stats

• Total Feedbacks: 0
• Suggested Refinements: 0
• Files Affected: 0

🏆 Code Quality

[██████████████████░░] 90% (Excellent)

🧪 Test Cases

Test Cases need updates: Run !unittest to generate

Tests Not Found

The following files are missing corresponding test files:

1. kaizen/generator/issue_analysis.py
2. kaizen/generator/unit_test.py
3. kaizen/reviewer/code_scan.py
4. kaizen/generator/e2e_tests.py
5. kaizen/llms/prompts/issue_analysis_prompts.py
6. kaizen/helpers/output.py

Tests Found But May Need Update

The following test files may need to be updated to reflect recent changes:

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