Add Audio Input for Performance Reviews

[archie-ai-code-explain-pr-review[bot]]

• Enable users to provide audio input for their performance reviews and self-reviews, in addition to the existing text-based input.
• Use the streamlit-audio-recorder library to allow audio recording in the app.
• Implement the convert_speech_to_text function using the Whisper model on the Groq library to convert the audio to text.
• Update the ReviewRequest and SelfReviewRequest models to include an optional audio_review field, and use this input for generating the review and self-review.
• Update the review generation logic in app.py to handle the new audio_review field.

[greptile-apps[bot]]

Implementation Steps

1. Update ReviewRequest and SelfReviewRequest Models:

# review.py
class ReviewRequest(BaseModel):
    your_role: str
    candidate_role: str
    perf_question: Optional[str] = None
    your_review: str
    audio_review: Optional[str] = None  # New field
    llm_type: str
    user_api_key: str
    model_size: str = "small"

# self_review.py
class SelfReviewRequest(BaseModel):
    text_dump: str
    questions: List[str]
    instructions: Optional[str] = None
    audio_review: Optional[str] = None  # New field
    llm_type: str
    user_api_key: str
    model_size: str = "medium"

2. Implement convert_speech_to_text Function:

# llm.py
from groq import Whisper

def convert_speech_to_text(audio_path: str) -> str:
    whisper = Whisper(api_key=GROQ_API_KEY)
    result = whisper.transcribe(audio_path)
    return result['text']

3. Update Streamlit App to Include Audio Recording:

# app.py
import streamlit as st
from streamlit_audio_recorder import audio_recorder

# Inside the Performance Review section
if review_type == "Performance Review":
    audio_review = audio_recorder()
    if audio_review:
        audio_text = convert_speech_to_text(audio_review)
        your_review = st.text_area('Your Review (Text or Audio)', value=audio_text, height=200)

# Inside the Self-Review section
if review_type == "Self-Review":
    audio_review = audio_recorder()
    if audio_review:
        audio_text = convert_speech_to_text(audio_review)
        text_dump = st.text_area('Text Dump (Text or Audio)', value=audio_text, height=200)

4. Update Review Generation Logic:

# app.py
if st.button('Generate Performance Review'):
    if not user_api_key:
        st.error("Please enter your API key in the sidebar.")
    elif not your_role or not candidate_role or not your_review:
        st.error("Please fill in all required fields.")
    else:
        try:
            questions = perf_question.split('\n') if perf_question else DEFAULT_QUESTIONS.split('\n')
            review_request = ReviewRequest(
                your_role=your_role,
                candidate_role=candidate_role,
                perf_question="\n".join(questions),
                your_review=your_review,
                audio_review=audio_review,  # Include audio_review
                llm_type=llm_type,
                user_api_key=user_api_key,
                model_size=model_size
            )
            review = generate_review(**review_request.model_dump())
            for qa in review:
                st.markdown(f"**{qa['question']}**")
                st.markdown(qa['answer'])
                st.markdown("---")
        except Exception as e:
            st.error(f"An error occurred: {str(e)}")

References

/app.py /review.py /self_review.py /llm.py

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