Python and React AI Assistant Application

This application is an AI-powered assistant that integrates Python execution capabilities with React component rendering on the fly, offering a comprehensive environment for data analysis, visualization, and interactive web development.

vision_support.mp4

streamlit_langgraphv2.mp4

New Feature	Feature Description	Notes
03.07.2024	Multimodal Support (Vision Capability)	- Enables the application to process and analyze images alongside text and code. - AI to generate from referanced images for a wider range of tasks. - Integration: Seamlessly added to existing framework.

Key Features and Functionalities

1. Intelligent Chat Interface:

   • Powered by Claude 3.5 Sonnet, an advanced AI model from Anthropic.
   • Enables natural language interactions for task requests and queries.

2. Python Code Execution:

   • Runs Python code within a secure Jupyter notebook environment.
   • Executes data analysis tasks using popular libraries.
   • Displays code results directly in the chat interface.

3. Dynamic React Component Creation:

   • Generates and renders React components on-demand.
   • Allows real-time preview of created components.

4. Integrated File Operations:

   • Facilitates file uploads for AI processing.
   • Enables downloads of AI-generated files.
   • Manages files within the application environment.

5. Advanced Data Visualization:

   • Creates diverse charts and graphs using matplotlib and other libraries.
   • Presents data visually to enhance understanding and analysis.

6. LangGraph-based Workflow:

   • Orchestrates AI decision-making processes.
   • Provides a real-time Mermaid diagram of the workflow in the sidebar.

7. Intuitive Streamlit Interface:

   • Offers a clean, user-friendly interface for seamless interaction.

8. Adaptive Tool Utilization:

   • Switches between various functionalities (Python, React, file operations) based on context.

9. Flexible Package Management:

   • Supports installation of additional Python packages as required.

10. Web Resource Access:

    • Capable of making API requests and accessing online information.

11. Robust Error Handling:

    • Delivers clear error messages and explanations for troubleshooting.

Setup and Usage

Python Dependency Installation

Before running the application, ensure you have configured the necessary API keys in the .env file located at the root of the project directory. Follow these steps for Python dependency installation:

1. Create a virtual environment by running:

   python -m venv venv

   This command creates a new directory named venv in your project directory, which will contain the Python executable and libraries.

2. Activate the virtual environment:

   • On Windows, run:

     .\venv\Scripts\activate

   • On macOS and Linux, run:

     source venv/bin/activate

   After activation, your terminal prompt will change to indicate that the virtual environment is active.

3. With the virtual environment activated, install the required Python packages by running:

   pip install -r requirements.txt

   This command reads the requirements.txt file and installs all the listed packages along with their dependencies.

Remember to activate the virtual environment (venv) every time you work on this project. To deactivate the virtual environment and return to your global Python environment, simply run deactivate.

Node.js Package Installation and Build

After setting up the Python environment, proceed with the Node.js setup:

1. Install the required Node.js packages:

   npm install

2. Build the packages:

   npm run build

Starting the Application

Finally, to start the application:

1. Launch the Streamlit application:

   streamlit run main.py

2. Access the application via your web browser to start interacting with the AI assistant.

Note: The application automatically initiates the React development server in a subprocess, eliminating the need to manually run npm start.

Important Note

This application combines advanced AI capabilities with code execution. Always review and understand any code before execution, particularly in production environments.