SETUP.md

Setup Guide — Burnout Risk Tracker

Prerequisites

• Python 3.9+
• pip or conda

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1. Clone the Repository

git clone https://github.com/sashpol111/burnout-tracker.git
cd burnout-tracker

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2. Install Dependencies

pip install -r requirements.txt

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3. API Keys

This project requires two external API keys.

Groq API (required for LLM coaching and chat):

1. Go to https://console.groq.com and create a free account
2. Generate an API key from the dashboard

Kaggle API (required only if rebuilding the dataset from scratch — not needed to run the app):

1. Go to https://www.kaggle.com/settings/account
2. Under the API section, click "Generate New Token"
3. Copy the username and key values from that file

Create a .env file in the root directory:

GROQ_API_KEY=your_groq_api_key_here
KAGGLE_USERNAME=your_kaggle_username
KAGGLE_KEY=your_kaggle_api_key

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4. Run the App

streamlit run app.py

Open your browser to http://localhost:8501

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5. Rebuilding the Dataset (optional)

The app ships with data/unified_dataset.csv already built. If you want to regenerate it from all three API sources, run:

python data/data_pipeline.py

This will call the Kaggle API, Groq API, and HuggingFace Datasets API and may take several minutes.

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Notes for Graders

The app runs fully offline after setup — the only live API call at runtime is to Groq for the coaching advice and chat responses. If you do not have a Groq API key, the risk prediction and sliders will still work, but the AI coach section will show an error message. All ML experiments/evidence can be run independently without launching the app:

python src/preprocessing_experiment.py
python src/hyperparameter_tuning.py
python src/error_analysis.py
python src/ablation.py