Lecture Notes: Building "Deepsite" with Marlene

Introduction

• Presenter: Marlene
• Platform: YouTube
• Topic: Building "Deepsite", an application that generates a website UI using HTML, JavaScript, and CSS through Deepseek R1.
• Platform Highlighted: Hugging Face and its Spaces feature

What is Deepsite?

• Deepsite: An application that generates website UIs from simple prompts.
• Deepseek R1: The underlying model used, which employs HTML, JavaScript, and CSS.
• Hugging Face Spaces: A place to find and share machine learning and AI projects.

Goal of the Video

• Rebuild Deepsite in Python (currently in TypeScript).
• Maintain the core functionalities by utilizing a system prompt.

Steps to Build Deepsite in Python

Accessing the Original Code

• Start at Hugging Face Spaces page.
• Navigate to GitHub repository for the code.
• Focus on service.js where AI code is located.
• Key Element: System prompt drives the application.

System Prompt

• Defines constraints: HTML, CSS, JavaScript, Tailwind CSS preference.
• Ensures responses are delivered in a single HTML file.

Implementation in Python

Setup Environment

• Create a new Jupyter Notebook.
• Define a deep_site_nb notebook in VS Code.

Dependencies

• Use requirements.txt for dependencies:
  • langchain
  • Azure AI
  • Python
• Install with pip install requirements.txt.

Importing Libraries

• Import necessary modules for Azure AI and Langchain.

Initialize Deepseek Model

• Set up using Azure AI endpoint and API key.
• Define model parameters like temperature and max tokens.

Message Handling

• Define system prompt and human prompt for interactions.
• Use Langchain's deepseek_modelstream for processing.

Generate Website Code

• Define function stream_response to handle prompt input.
• Collect website code into a variable by streaming responses.

Testing the Output

• Prompt example: "Create a simple purple to-do list."
• Run code and print the website code.
• Evaluate HTML output using VS Code live server.

Demonstration

• Example Output: A purple-themed to-do list app.
• Code Simplicity: Achieved with minimal lines of code.

Vibe Coding the Application

• Utilize Copilot and agent mode for enhancing the application.
• Final application named "Deep Site Python".
• Deploy the application and demonstrate the UI.

Conclusion

• Highlight the efficiency of generating applications with minimal code.
• Future potential of integrating AI in application development.

Closing Remarks

• Links to related videos and code are provided.
• Engagement Prompt: Like, subscribe, and comment on the video.
• Ending: Marlene signs off with a farewell.

These notes capture the essence of Marlene's tutorial on building "Deepsite" using Hugging Face's Deepseek R1, illustrating the power of AI-driven application development.