README.md

---
title: GLAM Web App
emoji: 📈
colorFrom: green
colorTo: purple
sdk: gradio
sdk_version: 6.18.0
python_version: '3.13'
app_file: app.py
pinned: false
license: mit
short_description: Respiratory monitoring model
---


# GLAM Pipeline

This repository includes a respiratory audio separation pipeline built around a local SepFormer model and a clinical reasoning/GNN inference path.

## Key Files

### model.py

Loads the SepFormer separation model using local pretrained files in `pretrained_sepformer`.

Wraps encoder/masknet/decoder into `UnifiedSepFormer`.

### interface.py

Provides shared audio loading, separation, and saving helpers.

Includes:

* `separate_audio_file(...)`
* `predict_sources(...)`
* `save_separated_sources(...)`

for pipeline reuse.

Still supports the existing Gradio UI workflow via:

* `separate_audio(...)`

### pipeline.py

Runs separation over a directory of mixture audio files.

Writes separated WAVs and waveform PNGs to an output folder.

Produces:

* `pipeline_summary.json`
* `pipeline_summary.csv`

### gnn.py

Defines the GNN model architecture used for respiratory audio reasoning.

Provides utilities for:

* loading a GNN checkpoint
* estimating breathing rate
* patient state tracking
* clinical alert generation

### reasoning_pipeline.py

Processes GNN run outputs:

* `window_report.json`
* `window_report.csv`

Generates reasoning summary CSV/JSON files.

Includes visualization helpers for per-patient summary plots.

### full_pipeline.py

Runs the complete flow:

1. Separate mixture audio files
2. Run Wav2Vec2 + GNN inference
3. Save per-run artifacts
4. Aggregate reasoning summaries

## Running a Simulation
To test the system using the provided sample data:

1. Open the Patient Registration page.
2. Navigate to the Sample_Audio folder.
3. Upload S1, S2, and S3 as individual patient recordings.
4. Click Register to register each patient.

Next:

1. Open the Separation page.
2. Upload Sample_0_mix.wav from the Sample_Audio folder as the mixed audio recording.
3. Select the registered patients (S1, S2, and S3).
4. Click Run Separation.


Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference