Add audio input and transcription to performance review generation

[ajitesh123]

Purpose

This PR adds the ability to generate performance reviews based on both text input and audio input. Users can now record audio and have it transcribed using the Groq API, and the transcription will be included in the context provided to the language model when generating the performance review.

Critical Changes

• Added audio_bytes parameter to generate_review() and generate_self_review() functions, which allows passing in audio data to be transcribed and included in the review context.
• Implemented convert_speech_to_text() function to use the Groq API to transcribe the audio input and return the text.
• Updated the generate_prompt() function to include the audio review transcript in the context, if available.
• Added unit tests to verify the behavior of the audio input and transcription functionality.
• Updated the requirements.txt file to include the streamlit-audiorec and groq dependencies.

===== Original PR title and description ============

Original Title: ## Summary

Original 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

1. Added the streamlit-audio-record library to the project, which allows users to record audio input within the Streamlit application.
2. Implemented the convert_speech_to_text function using the Whisper model in the Groq library to convert the recorded audio to text.
3. Updated the ReviewRequest and SelfReviewRequest models to include an optional audio_review field, and updated the generate_prompt and generate_review functions to use this field if it is provided.
4. Updated the app.py and app_fastapi.py files to handle the audio input, convert it to text, and pass it to the review generation process.
5. Added unit tests to ensure the audio input functionality works as expected.

Impact

With these changes, users can now provide audio input for their performance reviews and self-reviews, which can make the process more convenient and natural for them. The audio input is automatically converted to text and used in the review generation process, ensuring a seamless experience for the users.

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PR Review Summary

Overall Review:

This PR introduces audio input functionality for performance reviews and self-reviews, enhancing user experience. The implementation includes speech-to-text conversion using the Whisper model via Groq, updates to the review generation process to incorporate audio input, and corresponding UI changes. While the overall implementation appears solid, there are a few areas that require attention, particularly regarding error handling and potential security implications of handling audio data.

---

⚡ Logical Error Analysis

1. [Blocker] Duplicate function call in app.py: There's a redundant call to `generate_review()` on lines 106 and 107. The second call overwrites the result of the first, potentially ignoring the audio input.

🔒 Security Analysis

2. [Blocker] Exposure of API keys: The user's API key is directly passed to various functions and stored in the Streamlit session state, which could lead to accidental exposure.

🌟 Code Quality And Design

3. [Consider] Refactor the `generate_review` function to use a strategy pattern for different input types. The current implementation of `generate_review` in app.py (line 79) and review.py (line 92) has multiple parameters and handles both text and audio input.

🧪 Test Coverage Analysis

4. [Consider] The new `test_audio_input.py` file provides basic unit tests for the audio functionality, which is a good start. However, it lacks coverage for error cases and edge scenarios.

Recommendations

Recommendation #1

Remove the duplicate call on line 107 in app.py. The corrected code should look like this: ```python if st.button('Write Review'): audio_review = None if audio_bytes is not None: audio_review = convert_speech_to_text(audio_bytes, user_api_key) if audio_review: st.write("Transcribed Audio:", audio_review) review = generate_review(your_role, candidate_role, perf_question, your_review, llm_type, user_api_key, model_size, audio_review) st.markdown(review) ``` This ensures that the audio input is properly considered in the review generation process.

Recommendation #2

Implement a secure key management system or use environment variables for API keys instead of passing them directly through the application. Here's an example of how to use environment variables: 1. Add a `.env` file to your project root (make sure to add it to .gitignore): ``` GROQ_API_KEY=your_api_key_here ``` 2. Install the python-dotenv package: ``` pip install python-dotenv ``` 3. In your app.py, load the environment variables: ```python import os from dotenv import load_dotenv load_dotenv() # Use the API key groq_api_key = os.getenv('GROQ_API_KEY') ``` 4. Update the `convert_speech_to_text` function to use the environment variable: ```python def convert_speech_to_text(audio_bytes): client = Groq(api_key=os.getenv('GROQ_API_KEY')) # ... rest of the function ``` This approach keeps sensitive information out of your codebase and reduces the risk of accidental exposure.

Recommendation #3

Implement a strategy pattern to separate the logic for different input types. This will improve the function's maintainability and make it easier to add new input types in the future. Here's an example implementation: 1. Create a new file `review_strategies.py`: ```python from abc import ABC, abstractmethod class ReviewInputStrategy(ABC): @abstractmethod def process_input(self, input_data): pass class TextReviewStrategy(ReviewInputStrategy): def process_input(self, text_input): return text_input class AudioReviewStrategy(ReviewInputStrategy): def process_input(self, audio_input): return convert_speech_to_text(audio_input) class CombinedReviewStrategy(ReviewInputStrategy): def process_input(self, text_input, audio_input): audio_text = convert_speech_to_text(audio_input) if audio_input else "" return f"{text_input}\n\nAudio Review Transcript: {audio_text}" ``` 2. Update the `generate_review` function in `review.py`: ```python from review_strategies import CombinedReviewStrategy def generate_review(your_role, candidate_role, perf_question, your_review, llm_type, user_api_key, model_size, audio_review=None): perf_question = perf_question or DEFAULT_QUESTIONS input_strategy = CombinedReviewStrategy() processed_input = input_strategy.process_input(your_review, audio_review) prompt = generate_prompt(your_role, candidate_role, perf_question, processed_input) llm = create_llm_instance(llm_type, user_api_key) response = get_completion(prompt, llm, model_size) return parse_llm_response(response) ``` This approach makes the code more modular and easier to extend with new input types in the future.

Recommendation #4

Enhance the test coverage by adding more test cases, including error scenarios and edge cases. Here are some additional tests to consider adding to `test_audio_input.py`: ```python import unittest from unittest.mock import patch, MagicMock from app import convert_speech_to_text from review import generate_review from self_review import generate_self_review class TestAudioInput(unittest.TestCase): # ... existing tests ... @patch('app.Groq') def test_convert_speech_to_text_error(self, mock_groq): mock_client = MagicMock() mock_groq.return_value = mock_client mock_client.audio.transcriptions.create.side_effect = Exception("API Error") audio_bytes = b"dummy audio data" with self.assertRaises(Exception): convert_speech_to_text(audio_bytes, "fake_api_key") def test_generate_review_no_audio(self): result = generate_review("Manager", "Employee", "How did they perform?", "Good work", "openai", "fake_api_key", "small") self.assertIsNotNone(result) self.assertTrue(len(result) > 0) def test_generate_self_review_empty_audio(self): result = generate_self_review("Text dump", ["Q1"], None, "openai", "fake_api_key", "small", "") self.assertIsNotNone(result) self.assertTrue(len(result) > 0) @patch('review.create_llm_instance') def test_generate_review_long_audio(self, mock_create_llm): mock_llm = MagicMock() mock_create_llm.return_value = mock_llm mock_llm.generate_text.return_value = "

Q1A1

" long_audio = "A" * 10000 # Simulate a long audio transcript result = generate_review("Manager", "Employee", "How did they perform?", "Good work", "openai", "fake_api_key", "small", long_audio) self.assertEqual(len(result), 1) self.assertEqual(result[0]["question"], "Q1") self.assertEqual(result[0]["answer"], "A1") ``` These additional tests cover error handling, cases without audio input, empty audio input, and long audio transcripts. This will help ensure the robustness of the audio input functionality across various scenarios.

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