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.
- 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.
- Docker: To run the Weaviate vector store.
- Python 3.8+: For the FitFusion codebase.
- Virtual Environment (Optional): Recommended to manage dependencies.
- Clone the repository:
git clone https://github.com/your-repo/fitfusion.git cd fitfusion - Create a virtual environment and activate it (optional):
python -m venv venv source venv/bin/activate # Linux/Mac venv\Scripts\activate # Windows
- Install the required dependencies:
pip install -r requirements.txt
- Set up environment variables for API keys in a
.envfile 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
- Run Weaviate with Docker:
- Create a
docker-compose.ymlfile 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.
- Create a
1Start the Streamlit UI:
- Launch the Streamlit interface:
streamlit run app.py
- Interact with the app at
http://localhost:8501.
-
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_datadirectory.
-
Multi-agent Framework:
- A team of agents (e.g., diet planner and web searcher) collaborates to fetch, retrieve, and generate responses.
-
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.
- 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.
- Restart the Streamlit session using the "New Chat Session" button if needed.
- Modify or add knowledge files in the
./diet_datadirectory and restart the app to reload data.
Enjoy building healthier habits with FitFusion! 🚀