Sam Sulek Lifting Tracking

This project tracks the lifting progress of Sam Sulek by scraping his YouTube channel, analyzing the data, and presenting it in an interactive Streamlit web application.

Features

• Automated YouTube Scraping: Automatically fetches new video data from Sam Sulek's YouTube channel.
• Data Cleaning and Enrichment: Cleans and enriches the scraped data by extracting relevant information like the lifting event, day number, and specific lifts performed.
• Interactive Data Visualization: Presents the data in an interactive Streamlit application with features like:
  • Lifting Pie Charts: Visualizes the frequency of different lifts and muscle groups.
  • Event Durations: Shows the duration of each of Sam Sulek's lifting phases (e.g., "Winter Bulk," "Fall Cut").
  • Lifting Calendars: Displays the lifting data on interactive calendar heatmaps.
• Up-to-Date Data: Allows users to fetch the most recent data with the click of a button.

How to Use

1. Visit the Streamlit App: The easiest way to use the application is to visit the Streamlit App.
2. Get Most Recent Data: Click the "Get Most Recent Data" button to check for and fetch new videos.
3. Analyze Data: Click the "Analyse" button to view the latest data analysis, including pie charts, event durations, and lifting calendars.

Data Scraping and Analysis

The project uses the following process to scrape and analyze the data:

1. Scraping: The scrape.py script uses yt-dlp to scrape video titles and URLs from Sam Sulek's YouTube channel.
2. Data Cleaning: The analyse.py script cleans and enriches the data by:
   • Parsing Titles: Using regular expressions to extract the lifting event, day number, and specific lifts from the video titles.
   • Standardizing Lifts: Using the lift_types.json file to standardize the names of different lifts.
3. Data Analysis: The analyse.py script also performs the data analysis, including:
   • Calculating Lift Frequencies: Counting the occurrences of each lift and muscle group.
   • Determining Event Durations: Calculating the duration of each lifting phase.
4. Data Visualization: The main.py script uses Streamlit to create the interactive web application and heat_maps.py to generate the calendar plots.

Running Locally

To run the project locally, follow these steps:

1. Clone the Repository:

   git clone https://github.com/your-username/sam_sulek_lifting_tracking.git
   cd sam_sulek_lifting_tracking

2. Install Dependencies:

   pip install -r requirements.txt

3. Run the Streamlit App:

   streamlit run main.py

To-Do

• Add more advanced data analysis features, such as tracking progress on specific lifts over time.
• Improve the UI/UX of the Streamlit application.
• Add a database to store the data instead of a JSON file.
• Create more detailed documentation for each script.

Contributing

Contributions are welcome! If you have any ideas for new features or improvements, please open an issue or submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for more details.