shubh-vedi / fitness_chatbot_LLM

A personalized AI fitness companion providing exercise descriptions, tailored workout recommendations, and answers to your fitness-related questions.

Fitness Chatbot

A personalized AI fitness companion providing exercise descriptions, tailored workout recommendations, and answers to your fitness-related questions.

Introduction

The Fitness Chatbot leverages a comprehensive exercise database and the power of large language models (LLMs) to facilitate your fitness journey. Whether you're a beginner or a seasoned athlete, this chatbot has something for you.

Key Features

• Exercise Descriptions: Get clear instructions and form tips for a wide range of exercises.
• Workout Recommendations: Receive personalized workout plans based on your goals (weight loss, muscle building, endurance), experience level, and equipment availability.
• Fitness Q&A: Ask questions about training principles, nutrition, or anything fitness-related and get informative responses.

Technologies Used

• Python: Core programming language.
• Cohere: Large language model for workout generation and understanding fitness queries.
• Streamlit: Web framework for building the user interface.
• Pandas: Data manipulation and analysis (for managing the exercise database).
• AWS Lightsail: Deployment platform.

Project Demo Video Link :

Video.mp4

Streamlit Deployment Link :

Streamlit link : (http://34.200.246.244:8503/)

Screenshot of UI

How to Run Locally

1. Clone the Repository:

   git clone (https://github.com/shubh-vedi/fitness_chatbot_LLM.git)
   

2. **cd Fitness-Chatbot

   pip install -r requirements.txt
   

3. **Set Environment Variables:

*Obtain your Cohere API Key and create a .env file in the project's root directory with the following content:

**COHERE_API_KEY=YOUR_API_KEY **Load the environment variables using a library like dotenv.

4. **Run the Streamlit App:

   streamlit run app.py
   

Deployment on AWS Lightsail

Prerequisites:

• An AWS account with Lightsail access.
• Knowledge of basic Linux commands.

Steps

1. Create a Lightsail Instance:

   • Choose an appropriate Linux distribution (e.g., Ubuntu).
   • Select an instance size with sufficient resources for your app.

2. SSH into the Instance:

   • Connect to your instance using its public IP address.
   • Install any necessary updates (e.g., sudo apt update && sudo apt upgrade).

3. Set up and Install Dependencies:

   • Follow the same setup steps as in the "How to Run Locally" section, including:
     • Cloning your repository (git clone [your_repo_link]).
     • Installing dependencies (pip install -r requirements.txt).
     • Configuring environment variables for your Cohere API key.

4. Run the Streamlit App:

   • Start the app using streamlit run app.py.
   • Important: To keep the app running after you close the SSH session, use tools like tmux or screen.

5. Configure Firewall (Optional):

   • If needed, adjust your Lightsail firewall settings to allow incoming traffic on the port Streamlit uses (typically port 8501). Instructions for this step will depend slightly on your chosen Linux distribution.

Example: Opening Port 8501 on Ubuntu

• Run sudo ufw allow 8501

License

[State your chosen license - MIT, Apache 2.0, etc.]

Get Involved!

Contributions, suggestions, and feedback are welcome! Feel free to open issues or submit pull requests.