Workout Tracker

A Streamlit web application for tracking gym workouts and progress. The app allows you to log exercises, weights, and track your progress over time using Google BigQuery as the backend database.

Features

• 🏋️ Track exercises and weights for two different workout days
• 💾 Automatic data persistence in BigQuery
• 🔄 Pre-loads your last workout's weights
• 📊 View your workout history
• 📱 Mobile-friendly interface

Tech Stack

• Frontend: Streamlit
• Backend: Google BigQuery
• Language: Python 3.12

Exercise Configuration

All exercises are configured in config.py as a JSON structure. The configuration includes:

• Exercise name
• Number of sets
• Repetitions for each set
• Rest time between sets

Example structure:

WORKOUT_DATA = {
    "Giorno 1": {
        "Gambe": [
            {
                "nome": "Hack Squat",
                "serie": 4,
                "ripetizioni": [12, 10, 8, 8],
                "recupero": 90
            },
            # ... other exercises
        ]
    }
}

To modify or add exercises, simply update the WORKOUT_DATA dictionary in config.py.

Setup

1. Clone the repository

git clone https://github.com/yourusername/workout-tracker.git
cd workout-tracker

2. Install dependencies

pip install -r requirements.txt

3. Set up Google Cloud

• Create a new GCP project
• Enable BigQuery API
• Create a service account with BigQuery permissions
• Download the service account key
• Create a BigQuery dataset and table using the provided schema

4. Configure Streamlit secrets Create .streamlit/secrets.toml with your GCP credentials and BigQuery table information:

[gcp_service_account]
type = "service_account"
project_id = "your-project-id"
private_key_id = "..."
private_key = "..."
client_email = "..."
# ... other service account details

[bigquery]
table_id = "project-id.dataset.table"

5. Run the app

streamlit run app.py

BigQuery Schema

CREATE TABLE `project.dataset.table`
(
  date DATE,
  workout_type STRING,
  exercise_group STRING,
  exercise_name STRING,
  series1 FLOAT64,
  series2 FLOAT64,
  series3 FLOAT64,
  series4 FLOAT64,
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
)
PARTITION BY date;

Project Structure

workout_tracker/
│
├── app.py                    # Main application file
├── config.py                 # Workout configuration and exercises
├── requirements.txt          # Project dependencies
├── README.md                # Documentation
├── LICENSE                  # MIT License
│
└── .streamlit/
    └── secrets.toml         # Configuration secrets (not in repo)

Required Python Packages

These are the main dependencies required to run the app:

streamlit
pandas
google-cloud-bigquery
google-cloud-bigquery-storage
db-dtypes
pyarrow

All dependencies are listed in requirements.txt.

Deployment

The app can be deployed on Streamlit Cloud:

1. Push your code to GitHub
2. Connect your repository to Streamlit Cloud
3. Add your secrets in the Streamlit Cloud dashboard
4. Deploy!

Local Development

To run the app locally:

1. Install dependencies: pip install -r requirements.txt
2. Set up your .streamlit/secrets.toml with your GCP credentials
3. Run: streamlit run app.py

Contributing

Feel free to open issues or submit pull requests if you have suggestions for improvements!

License

This project is licensed under the MIT License - see the LICENSE file for details.