FitFusion

(Diet and Workout Planner)

Introduction

FitFusion is an AI-powered application that generates personalized 7-day diet and workout plans based on user inputs such as age, gender, fitness goals, dietary preferences, and lifestyle habits. The system uses Phidata, LangChain, OpenAI embeddings, and a Weaviate vector store to retrieve relevant information and provide detailed, step-by-step plans.

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Features

• Personalized Plans: Tailors diet and workout schedules to user-specific goals and preferences using Sqlite memory.
• Knowledge Base Integration: Utilizes a vector store (Weaviate) to fetch and store knowledge for accurate responses.
• Multi-agent System: Combines multiple AI agents for data retrieval and web searching using Phidata.
• Interactive UI: A Streamlit-based user interface for seamless interaction and customization.
• Dynamic Adjustments: Allows users to modify inputs and receive updated plans instantly.

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Prerequisites

1. Docker: To run the Weaviate vector store.
2. Python 3.8+: For the FitFusion codebase.
3. Virtual Environment (Optional): Recommended to manage dependencies.

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Installation

1. Clone the repository:

   git clone https://github.com/your-repo/fitfusion.git
   cd fitfusion

2. Create a virtual environment and activate it (optional):

   python -m venv venv
   source venv/bin/activate  # Linux/Mac
   venv\Scripts\activate  # Windows

3. Install the required dependencies:

   pip install -r requirements.txt

4. Set up environment variables for API keys in a .env file or export them directly:

   OPENAI_API_KEY=your_openai_api_key
   COHERE_API_KEY=your_cohere_api_key
   LANGCHAIN_API_KEY=your_langchain_api_key
   LANGSMITH_API=your_langsmith_api_key
   LANGCHAIN_ENDPOINT=https://api.smith.langchain.com

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Initializing Weaviate

1. Run Weaviate with Docker:
   • Create a docker-compose.yml file with the following content:

     version: '3.4'
     services:
       weaviate:
         image: semitechnologies/weaviate:latest
         ports:
           - "8080:8080"
         environment:
           - QUERY_DEFAULTS_LIMIT=100
           - AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED=true
           - PERSISTENCE_DATA_PATH=/data
           - DEFAULT_VECTORIZER_MODULE=text2vec-openai
           - OPENAI_APIKEY=your_openai_api_key
         volumes:
           - ./weaviate_data:/data

   • Start Weaviate:

     docker-compose up -d

   • Verify it is running at http://localhost:8080.

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Running the Application

1Start the Streamlit UI:

• Launch the Streamlit interface:

  streamlit run app.py

• Interact with the app at http://localhost:8501.

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How It Works

1. Knowledge Base Creation:

   • FitFusion uses a knowledge base built on Weaviate with OpenAI embeddings to store and retrieve data.
   • Relevant data files (e.g., .txt) are loaded from the ./diet_data directory.

2. Multi-agent Framework:

   • A team of agents (e.g., diet planner and web searcher) collaborates to fetch, retrieve, and generate responses.

3. Interactive Customization:

   • Users provide personal details, preferences, and goals via the Streamlit UI.
   • The AI dynamically generates a detailed diet and workout plan tailored to the user's input.

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Example Usage

• Update personal details in the sidebar form.
• Click the "Generate Diet & Workout Plan" button to create a 7-day plan.
• Ask follow-up questions or refine the plan using the chat interface.

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Development and Debugging

• Restart the Streamlit session using the "New Chat Session" button if needed.
• Modify or add knowledge files in the ./diet_data directory and restart the app to reload data.

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Enjoy building healthier habits with FitFusion! 🚀