Fitness-tracker

Personal Fitness Tracking System Using PYTHON & STREAMLIT

Python-based designed to help users monitor and analyze their nutrition and workout routines. Utilizing streamlit through Nutrition APIs, it simplifies the logging of meals and exercises. This app makes health tracking accessible to everyone. This is the iteration of a health dashboard I have built. The focus on this one was to fully automate the back-end and put less emphasis on the front-end. For that reason, I choose Streamlit since it allows for rapid deployment of simple applications. For a detailed explanation of the back-end data is Provided the Organization.

Go Live Demo Of Project :

https://harishjagdale0/Fitness-tracker-streamlit-app/ (No Longer Live Project in Web Application.)

Table of Contents

1. Features
2. Installation
3. Usage
4. Contributions
5. Future Improvements
6. License

Showcase Of Project :

Features

• Easy to Use UI Meal and Exercise Logging: Log meals and exercises using data, processed through the Nutrition API for detailed nutritional info and calories burned.
• Habit Tracking and Visualization: Track and visualize health and fitness habits using data, enhanced with custom Python visualizations for in-depth data analysis.
• Speech Recognition: Audio input capabilities for hands-free data logging.
• User Authentication: Securely manage user data and personal health records.
• Data Management: Organized and efficient storage of user, nutrition, and workout data.
• Use For Daily Life: Get reminders and alerts to maintain consistent health tracking.

Installation

• Clone the repository: git clone https://github.com/HarishJagdale0/Fitness-tracker.git
• Navigate to the project directory: cd Fitness-tracker
• Install dependencies: conda create --name fitness --file requirements.txt
• Activate the environment: conda activate fitness
• Create a .env file in the project root directory based on the .env.example template.
• Run the local tests in the application: python main.py

Usage

After launching the application, Begin by logging your meals and exercises using simple, data Processing. Explore the various visualizations and insights generated based on your data.

Contributions

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch:

    git checkout -b feature/BestFeature
   

3. Commit your Changes:

    git commit -m 'Add some BestFeature'
   

4. Push to the Branch:

    git push origin feature/BestFeature
   

5. Open a Pull Request

Future Improvements

• Tkinter GUI: Interactive and straightforward graphical interface.
• Meal Planner: Integrate a meal planning feature with recipes and grocery lists based on nutritional goals - refer & use a 3rd party API.
• Mobile Application: Develop a mobile version for convenient on-the-go access.
• Advanced Analytics: Use machine learning to provide personalized health insights and recommendations.
• Integration with Fitness Devices: Sync data from fitness trackers for automated exercise logging.
• Community Features: Implement social sharing, challenges, and leaderboards to encourage user engagement.

License

Distributed under the MIT License. See LICENSE for more information.

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