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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.

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.

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.

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

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.

Running the Application

1Start the Streamlit UI:

  • Launch the Streamlit interface: 
    streamlit run app.py
  • Interact with the app at http://localhost:8501.

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.

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.

FitFusion_ui

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.

Enjoy building healthier habits with FitFusion! 🚀