Integrating Your CLI LLM Coding Tool with a FastHTML Frontend

This guide provides a comprehensive walkthrough for integrating your CLI-based AI coding assistant with a dynamic web interface using FastHTML. By leveraging FastHTML, HTMX, and modern web technologies, you can transform your CLI tool into a powerful web application with real-time updates, shared conversation histories, and enhanced user experiences.

## Table of Contents

1. Introduction
2. Setting Up the FastHTML Project
3. Adapting CLI Functions for Web Routes
4. Integrating Database for Shared Conversation Histories
5. Implementing Real-Time Updates with WebSockets
6. Enhancing Interactivity with HTMX
7. Managing Projects and Variable Embedding
8. Conclusion

---

## Introduction

Transforming your CLI-based AI coding assistant into a web application enhances accessibility and user experience. FastHTML, a Python library built upon Starlette and Uvicorn, provides a powerful yet concise framework for creating server-rendered hypermedia applications. By integrating your CLI tool with a FastHTML frontend, you enable features like shared conversation histories, real-time code generation, and interactive project management.

---

## Setting Up the FastHTML Project

### Installation

Begin by installing FastHTML:

pip install python-fasthtml


### Minimal Application Structure

Create a basic FastHTML application to serve as the foundation:

from fasthtml.common import *

app, rt = fast_app()

@rt("/")
def get():
    return Titled("AI Coding Assistant", P("Welcome to your assistant's web interface!"))

serve()


### Running the Application

Run your application:

python main.py


Access it via http://127.0.0.1:5001.

---

## Adapting CLI Functions for Web Routes

### Modularizing CLI Functions

Extract the core logic of your CLI tool into modular functions. This separation allows you to reuse these functions in both CLI and web contexts.

Example:

def generate_code(prompt):
    # Your existing code generation logic
    return code_output


### Creating FastHTML Routes

Use FastHTML's @rt decorator to map URLs to functions. Adapt your CLI commands into route handlers:

@app.post("/generate")
def generate(prompt: str):
    code_output = generate_code(prompt)
    return Div(Pre(Code(code_output)))


### Handling HTTP Methods

FastHTML matches function names to HTTP methods (get, post, etc.). Use post() to handle form submissions:

@rt("/submit")
def post(prompt: str):
    result = process_prompt(prompt)
    return Div(result)


---

## Integrating Database for Shared Conversation Histories

### Choosing a Database

Use a database to store conversation histories and user data. FastHTML integrates seamlessly with SQLite via the fastlite library.

Installation:

FastHTML comes with optional support for fastlite. Ensure it's installed:

pip install fastlite


### Setting Up the Database

Initialize the database and create tables for users and conversation histories:

from fasthtml.common import database

db = database('data/conversations.db')
conversations, users = db.t.conversations, db.t.users

if conversations not in db.t:
    users.create(dict(name=str, password=str), pk='name')
    conversations.create(id=int, user=str, message=str, response=str, timestamp=str, pk='id')


### Dataclasses for Data Models

Define dataclasses for type safety and ease of use:

from dataclasses import dataclass

@dataclass
class Conversation:
    id: int
    user: str
    message: str
    response: str
    timestamp: str


### CRUD Operations

Implement Create, Read, Update, and Delete (CRUD) operations for managing conversation histories:

@rt("/conversations")
def get(user: str):
    convos = conversations(user=user)
    return Div(*[render_conversation(c) for c in convos])

@rt("/conversation")
def post(convo: Conversation):
    conversations.insert(convo)
    return Div(f"Conversation {convo.id} saved.")


---

## Implementing Real-Time Updates with WebSockets

### Enabling WebSocket Support

FastHTML supports WebSockets for real-time communication.

Example WebSocket Endpoint:

@app.ws('/ws')
async def websocket_endpoint(send):
    await send(Text("Connected to WebSocket"))

    while True:
        # Example: Send updates every 5 seconds
        await asyncio.sleep(5)
        await send(Text("Update"))


### Integrating with the Frontend

Use HTMX with the ws extension to connect to the WebSocket:

<div hx-ext="ws" ws-connect="/ws" hx-swap="outerHTML" id="updates">
    <!-- Updates will appear here -->
</div>


### Streaming Responses

Stream code generation outputs or assistant responses in real-time:

async def generate_code_stream(prompt):
    for chunk in generate_code_chunks(prompt):
        yield sse_message(chunk)
        await asyncio.sleep(0.1)

@rt("/stream")
async def stream(prompt: str):
    return EventStream(generate_code_stream(prompt))


---

## Enhancing Interactivity with HTMX

### Introduction to HTMX

HTMX allows you to perform AJAX requests declaratively in your HTML, enhancing interactivity without custom JavaScript.

### Triggering Server Requests

Use hx-post or hx-get to trigger server-side functions:

<button hx-post="/generate" hx-trigger="click" hx-target="#output">Generate Code</button>
<div id="output"></div>


### Swapping Content

Control how responses replace content with hx-swap:

<div hx-swap="outerHTML" id="code-output">
    <!-- Generated code will appear here -->
</div>


### Loading Indicators and Error Handling

Use HTMX attributes to provide feedback to users:

<div hx-indicator=".loading-indicator">
    <div class="loading-indicator" style="display:none;">Loading...</div>
    <!-- Content -->
</div>


<div hx-target="this" hx-swap="innerHTML">
    <button hx-get="/action" hx-trigger="click" hx-indicator=".loading" hx-error="alert('An error occurred')">
        Click Me
    </button>
</div>


---

## Managing Projects and Variable Embedding

### Project Management Features

Implement routes and forms for managing projects:

@rt("/projects")
def get():
    projects = get_user_projects()
    return Div(*[render_project(p) for p in projects])

@rt("/project/new")
def post(project_name: str):
    create_project(project_name)
    return Redirect("/projects")


### Variable Embedding

Allow users to define variables or context that the assistant can use:

<form hx-post="/set-variables" hx-trigger="change">
    <input type="text" name="variable_name" placeholder="Variable Name">
    <input type="text" name="variable_value" placeholder="Variable Value">
    <button type="submit">Set Variable</button>
</form>


@rt("/set-variables")
def post(variables: dict):
    save_variables(variables)
    return Div("Variables updated.")


Integrate these variables into the assistant's processing functions to customize code generation based on user inputs.

---

## Conclusion

By leveraging FastHTML, HTMX, and modern web development practices, you can seamlessly integrate your CLI-based AI coding assistant into a dynamic web interface. This integration not only enhances the user experience but also unlocks new features like shared conversation histories, real-time updates, and interactive project management.

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## Additional Tips

- Security Considerations: Ensure proper authentication and authorization mechanisms are in place, especially when handling user data and conversation histories.

- Testing: Use Starlette's TestClient for testing your FastHTML views and routes.

- Custom Components: Create reusable FT components (FastTags) for consistent UI elements.

- Styling: FastHTML supports CSS frameworks like Pico CSS. Include custom styles using the Style component.

Example:

from fasthtml.common import Style

app, rt = fast_app(
    hdrs=(Style("""
        body { background-color: #f0f0f0; }
        .code-output { font-family: monospace; }
    """),)
)


---

## References

- FastHTML Documentation: https://fasthtml.readthedocs.io
- HTMX Documentation: https://htmx.org/docs
- Starlette Documentation: https://www.starlette.io
- FastLite (SQLite Integration): https://github.com/fastlite/fastlite

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By following this guide, you should be well-equipped to integrate your CLI LLM coding tool into a web application using FastHTML, providing users with an enhanced, interactive experience.