jph00

FastHTML By Example

An alternative introduction

There are lots of non-FastHTML-specific tricks and patterns involved in building web apps. The goal of this tutorial is to give an alternate introduction to FastHTML, building out example applications to show common patterns and illustrate some of the ways you can build on top of the FastHTML foundations to create your own custom web apps. A secondary goal is to have this be a useful document to add to the context of an LLM to turn it into a useful FastHTML assistant - in fact, in some of the examples we’ll see this kind of assistant in action, thanks to this custom GPT.

Let’s get started.

FastHTML Basics

alexgleith

adamsmith

There are two prompts, that chain together. The first prompt does most of the work, and the second prompt organizes the sections. I found because of the nature of how LLMs write, I couldn't get just one prompt to never jump back and forth in topics.

Prompt 1, which takes as input a raw transcript and generates a structured-text version...

"""# Instructions
A transcript is provided below of a voice memo I recorded as a "note to self". please extract all the points made or thoughts described, and put them in bullet-point form. use nested bullet points to indicate structure, e.g. a top-level bullet for each topic area and sub-bullets underneath. use multi-level nesting as appropriate to organize the thinking logically. use markdown formatting with `*` instead of `-` for bullet points.

DO NOT OMIT ANY POINTS MADE. This is not a summarization task — your only goal is to structure the thoughts there so they are logically organized and easy to read. Be concise because the reader is busy, but again DO NOT omit any

CharlyWargnier

function ConnectButton(){
    console.log("Connect pushed"); 
    document.querySelector("#top-toolbar > colab-connect-button").shadowRoot.querySelector("#connect").click() 
}
setInterval(ConnectButton,60000);

okld

import streamlit as st
from persist import persist, load_widget_state


def main():
    if "page" not in st.session_state:
        # Initialize session state.
        st.session_state.update({
            # Default page.
            "page": "home",

480

MacOS X + oh my zsh + powerline fonts + visual studio code (vscode) terminal settings

Thank you everybody, Your comments makes it better

Install oh my zsh

http://ohmyz.sh/

sh -c "$(curl -fsSL https://raw.github.com/ohmyzsh/ohmyzsh/master/tools/install.sh)"

lmclupr

qingswu

#!/usr/bin/env python
from glob import glob                                                           
import cv2 
pngs = glob('./*.png')

for j in pngs:
    img = cv2.imread(j)
    cv2.imwrite(j[:-3] + 'jpg', img)

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

bearfrieze

Comprehensions in Python the Jedi way

by Bjørn Friese

Beautiful is better than ugly. Explicit is better than implicit.

-- The Zen of Python

I frequently deal with collections of things in the programs I write. Collections of droids, jedis, planets, lightsabers, starfighters, etc. When programming in Python, these collections of things are usually represented as lists, sets and dictionaries. Oftentimes, what I want to do with collections is to transform them in various ways. Comprehensions is a powerful syntax for doing just that. I use them extensively, and it's one of the things that keep me coming back to Python. Let me show you a few examples of the incredible usefulness of comprehensions.