ajitesh123 / auto-review-ai

๐Ÿš€ AI-Powered Performance Review Generator
https://perfor-ai.streamlit.app/
3 stars 1 forks source link

``` #79

Closed ajitesh123 closed 4 months ago

ajitesh123 commented 4 months ago

This pull request adds the ability for users to provide audio input for their performance reviews and self-reviews. Previously, users had to type their input, but now they can record their audio. The audio is converted from speech to text using the Whisper model on the Groq library, and the resulting text is used as input to the review generation process.

Main Changes:

archie-ai-code-generation[bot] commented 4 months ago

PR Review Summary ๐Ÿ”

This PR adds the ability for users to provide audio input for their performance reviews and self-reviews. The key changes include:

  1. Integration of the streamlit-audiorec library to allow users to record audio input.
  2. Implementation of the convert_speech_to_text function using the Whisper model in the Groq library to convert the recorded audio to text.
  3. Updates to the ReviewRequest and SelfReviewRequest models to include an optional audio_review field.
  4. Modifications to the api_generate_review and api_generate_self_review endpoints to handle the case where an audio file is provided, converting the audio to text and using it as input to the review generation process.
๐Ÿ”’ Security analysis
- The use of the Groq library and the Whisper model for audio-to-text conversion appears to be a secure approach, as these are reputable and widely-used tools. - However, it's important to ensure that the API keys used for the Groq library are properly secured and not exposed in the codebase or client-side. - The handling of temporary files created during the audio-to-text conversion process should be carefully managed to avoid potential security vulnerabilities.
๐Ÿงช Test coverage analysis
- The PR introduces new functionality for handling audio input, which should be thoroughly tested to ensure the reliability and robustness of the audio-to-text conversion process. - Test cases should be added to cover edge cases, such as handling invalid or corrupted audio files, large audio files, and various audio quality scenarios. - The integration of the new functionality with the existing review generation process should also be tested to ensure that the overall system continues to work as expected.
โšก Logical error analysis
- The logic for handling the case where an audio file is provided appears to be sound, with the `convert_speech_to_text` function being called and the resulting text being used as input to the review generation process. - However, it's important to ensure that the error handling and edge case management in the `convert_speech_to_text` function is robust, as any issues in this function could potentially impact the overall review generation process. - The integration of the audio input feature with the existing review generation logic should be carefully reviewed to ensure that there are no unintended consequences or logical errors introduced.
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