ajitesh123 / auto-review-ai

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

``` #25

Closed ajitesh123 closed 4 months ago

ajitesh123 commented 4 months ago

User description

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:


PR Type

Enhancement, Other


Description


Changes walkthrough πŸ“

Relevant files
Enhancement
app_fastapi.py
Add audio input handling in FastAPI endpoints                       

app_fastapi.py
  • Added convert_speech_to_text function call to handle audio input.
  • Updated api_generate_review and api_generate_self_review endpoints to
    process audio input.
  • +11/-2   
    audio_utils.py
    Implement audio to text conversion utility                             

    audio_utils.py
  • Created convert_speech_to_text function using Groq's Whisper model.
  • Added temporary file handling for audio processing.
  • +22/-0   
    review.py
    Add audio review support in review generation                       

    review.py
  • Added audio_review field to ReviewRequest model.
  • Integrated st_audiorec for audio recording in the main function.
  • +34/-5   
    self_review.py
    Add audio review support in self-review generation             

    self_review.py
  • Added audio_review field to SelfReviewRequest model.
  • Integrated st_audiorec for audio recording in the main function.
  • +31/-2   
    Dependencies
    requirements.txt
    Add streamlit-audiorec dependency                                               

    requirements.txt - Added `streamlit-audiorec` library.
    +1/-0     

    πŸ’‘ PR-Agent usage: Comment /help on the PR to get a list of all available PR-Agent tools and their descriptions

    coderabbitai[bot] commented 4 months ago

    Walkthrough

    The changes incorporate speech-to-text functionality for generating both review and self-review. Audio inputs are converted to text using the convert_speech_to_text function. The updates span multiple files, ensuring audio inputs are handled, converted, and processed appropriately.

    Changes

    File Change Summary
    app_fastapi.py Integrated convert_speech_to_text for handling audio reviews in both review and self-review processes.
    audio_utils.py Introduced convert_speech_to_text function leveraging Groq API for audio transcription.
    requirements.txt Added streamlit-audiorec==0.1.3 as a new dependency.
    review.py Incorporated st_audiorec, modified ReviewRequest to include audio_review field, added main function.
    self_review.py Similar to review.py, added audio_review field and a main function for handling self-reviews.

    Poem

    In lines of code, a rabbit's gift,
    From speech to text, we now uplift.
    πŸ‡πŸŽ€
    Reviews and more, now understand,
    Audio to words, how grand a plan!
    🌟
    With clever bytes, the task is done,
    Our journey's ripe, new dawn begun.
    β˜€οΈβœ¨

    [!TIP]

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    codiumai-pr-agent-pro[bot] commented 4 months ago

    PR Reviewer Guide πŸ”

    ⏱️ Estimated effort to review: 4 πŸ”΅πŸ”΅πŸ”΅πŸ”΅βšͺ
    πŸ§ͺ No relevant tests
    πŸ”’ No security concerns identified
    ⚑ Key issues to review

    **Possible Bug:** The `convert_speech_to_text` function in `audio_utils.py` does not handle exceptions that may occur during the transcription process. It's recommended to add error handling to manage potential failures from the Groq client or file operations. **Performance Concern:** The audio file handling in `convert_speech_to_text` involves writing to a temporary file and then reading it back, which could be optimized. Consider processing the audio data directly from memory if the Groq API supports it. **Code Duplication:** Similar code blocks are used in both `api_generate_review` and `api_generate_self_review` for handling audio data. Consider refactoring this into a separate function to reduce duplication and improve maintainability.
    codiumai-pr-agent-pro[bot] commented 4 months ago

    PR Code Suggestions ✨

    CategorySuggestion                                                                                                                                    Score
    Possible issue
    Add error handling for the speech-to-text conversion to improve reliability and error reporting ___ **Consider handling the case where convert_speech_to_text might raise an exception due to
    API errors or invalid audio data. This will prevent the server from crashing and provide a
    way to return a meaningful error response to the client.** [app_fastapi.py [21]](https://github.com/ajitesh123/Perf-Review-AI/pull/25/files#diff-12ae1733c1fe81510692dadf3ad3328d8801f864812689ff2dc412fe14fd04f0R21-R21) ```diff -your_review = convert_speech_to_text(request.audio_review, request.user_api_key) +try: + your_review = convert_speech_to_text(request.audio_review, request.user_api_key) +except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) ``` - [ ] **Apply this suggestion**
    Suggestion importance[1-10]: 9 Why: This suggestion is crucial as it adds error handling for potential exceptions that may arise during the speech-to-text conversion, thereby preventing the server from crashing and providing meaningful error responses to the client.
    9
    Enhancement
    Add validation for audio_review to ensure it contains valid data before processing ___ **Validate the audio_review field in the request to ensure it contains valid audio data
    before attempting to convert it to text. This prevents unnecessary processing and
    potential errors in the conversion function.** [app_fastapi.py [20-21]](https://github.com/ajitesh123/Perf-Review-AI/pull/25/files#diff-12ae1733c1fe81510692dadf3ad3328d8801f864812689ff2dc412fe14fd04f0R20-R21) ```diff -if request.audio_review: +if request.audio_review and isinstance(request.audio_review, bytes) and len(request.audio_review) > 0: your_review = convert_speech_to_text(request.audio_review, request.user_api_key) +else: + raise HTTPException(status_code=400, detail="Invalid audio data provided.") ``` - [ ] **Apply this suggestion**
    Suggestion importance[1-10]: 8 Why: This suggestion improves the robustness of the code by validating the `audio_review` field before processing, which helps prevent unnecessary processing and potential errors.
    8
    Best practice
    Use the tempfile module's context manager for better temporary file management ___ **Instead of manually managing temporary files, use the tempfile module's context manager to
    automatically handle the creation and deletion of temporary files, improving code
    cleanliness and reliability.** [audio_utils.py [8-10]](https://github.com/ajitesh123/Perf-Review-AI/pull/25/files#diff-6de8964d478f54a617e0f99e6a7704be46d3bdcb7f51a426e98aba766114d080R8-R10) ```diff -with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file: +with tempfile.NamedTemporaryFile(suffix=".wav") as temp_file: temp_file.write(audio_data) - temp_file_path = temp_file.name + temp_file.flush() + with open(temp_file.name, "rb") as audio_file: + transcription = client.audio.transcriptions.create( + file=(temp_file.name, audio_file.read()), + model="whisper-large-v3", + response_format="text" + ) ``` - [ ] **Apply this suggestion**
    Suggestion importance[1-10]: 7 Why: This suggestion enhances code cleanliness and reliability by using the `tempfile` module's context manager to automatically handle the creation and deletion of temporary files.
    7
    ajitesh123 commented 4 months ago

    /describe

    codiumai-pr-agent-pro[bot] commented 4 months ago

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    PR Description updated to latest commit (https://github.com/ajitesh123/Perf-Review-AI/commit/b5d042770688095755352e14589252e27a64fa2a)

    ajitesh123 commented 4 months ago

    πŸšΆβ€β™‚οΈ Interactive PR Walkthrough

    Table of Contents

    1. Audio Input Feature Addition
    2. Speech-to-Text Conversion
    3. UI Integration with Streamlit
    4. API and Model Updates

    Step 1: Audio Input Feature Addition πŸ”

    Progress: 1/4 | Difficulty: πŸ”΄ Hard

    This PR introduces a new feature that allows users to provide audio input for reviews and self-reviews. The system now supports converting speech to text, enabling users to record their reviews verbally.

    from audio_utils import convert_speech_to_text
    
    if request.audio_review:
        your_review = convert_speech_to_text(request.audio_review, request.user_api_key)

    Question: What is the primary benefit of adding audio input support?

    Option 1Improved accessibility for users with visual impairments
    Option 2Faster input method for some users
    Option 3Enhanced accuracy in review content
    πŸ’‘ Reveal Explanation While all options could be potential benefits, the primary advantage is likely faster input for some users. This feature allows users to speak their reviews, which can be quicker and more convenient than typing, especially for longer reviews.

    Step 2: Speech-to-Text Conversion πŸ”

    Progress: 2/4 | Difficulty: πŸ”΄ Hard

    A new file, audio_utils.py, has been added to handle the conversion of speech to text. It uses the Groq API to transcribe audio files.

    def convert_speech_to_text(audio_data: bytes, api_key: str) -> str:
        client = Groq(api_key=api_key)
        # ... (file handling and API call)
        return transcription.text

    Question: What potential issue should be considered when implementing speech-to-text conversion?

    Option 1Increased API costs
    Option 2Longer processing time
    Option 3Accuracy of transcription
    πŸ’‘ Reveal Explanation While all these are valid concerns, the accuracy of transcription is crucial. Inaccurate transcriptions could lead to misinterpretations of reviews. Developers should consider implementing a way for users to verify and edit the transcribed text before submission.

    Step 3: UI Integration with Streamlit πŸ”

    Progress: 3/4 | Difficulty: πŸ”΄ Hard

    The PR updates both review.py and self_review.py to include Streamlit components for audio recording. This allows for a seamless integration of the new audio feature in the user interface.

    from st_audiorec import st_audiorec
    
    def main():
        # ... (other UI elements)
        st.subheader("Audio Review (Optional)")
        audio_data = st_audiorec()
        if audio_data is not None:
            st.audio(audio_data, format='audio/wav')

    Question: What additional feature might improve the user experience with audio reviews?

    Option 1A progress bar during transcription
    Option 2The ability to pause and resume recording
    Option 3Automatic language detection for multi-language support
    πŸ’‘ Reveal Explanation While all these features could enhance the user experience, the ability to pause and resume recording would be particularly useful for longer reviews. It allows users to gather their thoughts or refer to notes without having to start over.

    Step 4: API and Model Updates πŸ”

    Progress: 4/4 | Difficulty: 🟒 Easy

    The PR modifies the existing API endpoints and data models to accommodate the new audio input feature. This includes updates to ReviewRequest and SelfReviewRequest classes.

    class ReviewRequest(BaseModel):
        # ... (existing fields)
        audio_review: Optional[bytes] = None
    
    class SelfReviewRequest(BaseModel):
        # ... (existing fields)
        audio_review: Optional[bytes] = None

    Question: What potential backward compatibility issue should be considered with these changes?

    Option 1Existing clients might break if they don't expect the new field
    Option 2Database schema changes might be required
    Option 3Performance might degrade for non-audio reviews
    πŸ’‘ Reveal Explanation The most immediate concern is that existing clients might break if they don't expect the new audio_review field. To ensure backward compatibility, the API should be versioned or designed to handle requests both with and without the new field gracefully.
    ajitesh123 commented 4 months ago

    Interactive PR Walkthrough πŸšΆβ€β™‚οΈ

    Step 1: Audio Input Feature Addition

    This PR introduces a new feature that allows users to provide audio input for reviews and self-reviews. The system now supports converting speech to text, enabling users to record their reviews verbally.

    from audio_utils import convert_speech_to_text
    
    if request.audio_review:
        your_review = convert_speech_to_text(request.audio_review, request.user_api_key)

    Question: What is the primary benefit of adding audio input support?

    1. Improved accessibility for users with visual impairments
    2. Faster input method for some users
    3. Enhanced accuracy in review content
    Explanation While all options could be potential benefits, the primary advantage is likely faster input for some users. This feature allows users to speak their reviews, which can be quicker and more convenient than typing, especially for longer reviews.

    Step 2: Speech-to-Text Conversion

    A new file, audio_utils.py, has been added to handle the conversion of speech to text. It uses the Groq API to transcribe audio files.

    def convert_speech_to_text(audio_data: bytes, api_key: str) -> str:
        client = Groq(api_key=api_key)
        # ... (file handling and API call)
        return transcription.text

    Question: What potential issue should be considered when implementing speech-to-text conversion?

    1. Increased API costs
    2. Longer processing time
    3. Accuracy of transcription
    Explanation While all these are valid concerns, the accuracy of transcription is crucial. Inaccurate transcriptions could lead to misinterpretations of reviews. Developers should consider implementing a way for users to verify and edit the transcribed text before submission.

    Step 3: UI Integration with Streamlit

    The PR updates both review.py and self_review.py to include Streamlit components for audio recording. This allows for a seamless integration of the new audio feature in the user interface.

    from st_audiorec import st_audiorec
    
    def main():
        # ... (other UI elements)
        st.subheader("Audio Review (Optional)")
        audio_data = st_audiorec()
        if audio_data is not None:
            st.audio(audio_data, format='audio/wav')

    Question: What additional feature might improve the user experience with audio reviews?

    1. A progress bar during transcription
    2. The ability to pause and resume recording
    3. Automatic language detection for multi-language support
    Explanation While all these features could enhance the user experience, the ability to pause and resume recording would be particularly useful for longer reviews. It allows users to gather their thoughts or refer to notes without having to start over.

    Step 4: API and Model Updates

    The PR modifies the existing API endpoints and data models to accommodate the new audio input feature. This includes updates to ReviewRequest and SelfReviewRequest classes.

    class ReviewRequest(BaseModel):
        # ... (existing fields)
        audio_review: Optional[bytes] = None
    
    class SelfReviewRequest(BaseModel):
        # ... (existing fields)
        audio_review: Optional[bytes] = None

    Question: What potential backward compatibility issue should be considered with these changes?

    1. Existing clients might break if they don't expect the new field
    2. Database schema changes might be required
    3. Performance might degrade for non-audio reviews
    Explanation The most immediate concern is that existing clients might break if they don't expect the new audio_review field. To ensure backward compatibility, the API should be versioned or designed to handle requests both with and without the new field gracefully.