During the development of HealthMate, we encountered several challenges that tested our problem-solving skills and technical expertise.
1.Audio Processing & Model Integration
One of the major hurdles was processing audio files and passing them correctly to our deep learning model. Handling different audio formats, ensuring proper preprocessing, and optimizing input compatibility required extensive debugging. We overcame this by implementing Librosa for audio feature extraction and standardizing input formats.
2.FastAPI & Frontend Communication
Initially, our FastAPI backend was not correctly receiving audio files from the frontend, leading to 422 Unprocessable Entity errors. The issue stemmed from incorrect form-data handling and MIME type mismatches. We fixed it by refining our request format, debugging with Postman, and adjusting CORS policies.
3.Real-Time Performance Optimization
Processing large audio files in real-time caused latency issues. To improve efficiency, we optimized model inference, used NumPy & TensorFlow Lite, and enabled asynchronous requests in FastAPI. This significantly reduced response times.
Despite these challenges, our team’s persistence and structured debugging approach ensured a robust and efficient solution.