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Build an LLM Web App in Python from Scratch: Part 2 (Streamlit & FSM)
- The article discusses building an AI-powered image generator via an interactive web application where users can generate images from text prompts and provide feedback within a user-friendly interface.
- Python tools used include Streamlit for web app conversion, PocketFlow for workflow management, and Finite State Machine (FSM) for tracking the process.
- Streamlit is highlighted for simplifying web app development without requiring HTML/CSS skills and its 'rerun' model that re-executes the Python script each time a user action occurs.
- An example of a simple click counter web app using Streamlit is provided.
- PocketFlow is used to organize the workflow with Nodes acting as building blocks for different steps like getting user input, AI image generation, and user feedback.
- The article also introduces the concept of Finite State Machines (FSMs) for managing interactive web states, providing a framework to guide the app through different screens or modes.
- The FSM ensures the app remains responsive and helps in transitioning between different states based on user actions.
- A complete working example integrating PocketFlow, Streamlit, and FSM is offered for reference.
- The FSM approach enhances the interactive user experience of the PocketFlow workflow and allows for seamless transitions between states.
- The article concludes by emphasizing the power of the FSM approach, clean code maintenance, Streamlit compatibility, and user-friendliness in AI app development.
- The future direction of the tutorial series includes adding real-time features with FastAPI and WebSockets for live interactions and updates.
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