Michael Beale wallabyway

View Revit Model in Browser

To create a simple 'View Revit Model in a Browser', use this prompt in Cursor:

Create a minimal Python Flask backend (with /api/token, /api/upload, /api/status/<urn>, and /api/list endpoints using .env containing APS client ID, secret, and bucket name in utils.py). 
Use `/signeds3upload` and `/batchcompleteupload` endpoint for the upload, encode the urn f"urn:adsk.objects:os.object:{BUCKET_KEY}/{urn}" and then translate to svf2.
Finally, create a index.html frontend with an upload button and a combo box to upload, translate, and view .rvt models using the Autodesk Viewer SDK. 
No comments or error checking, using only server.py, utils.py, and index.html.

	#!/usr/bin/env python3
	"""
	combine_openapi_to_markdown.py
	--------------------------------
	Generate a single Markdown reference from many OpenAPI specs,
	including links to:

	• raw YAML file (…/yaml/ssa.yaml)
	• examples file (…/yaml/ssa-examples.md)

	#!/usr/bin/env python3
	"""
	Compress a Gaussian‑splat PLY using gsplat’s PNG compressor,
	but target Apple‑Silicon/Metal rather than CUDA.

	Example
	-------
	python compress_ply_mps.py \
	--ply my_scene.ply \
	--output-dir ./compressed

	// STEPS:
	// 1. Retrieve manifest.json
	// 2. Retrieve aecmodeldata.json
	// 3. given a #level, use it to create a bounding box, for LMV viewer, as a client side filter string

	// INPUTS:
	// level, URN, Access-token
	https://developer.api.autodesk.com/modelderivative/v2/designdata/dXJuOmFkc2sub2JqZW....xlX3Byb2plY3QucnZ0/manifest

	import fetch from 'node-fetch';

	#!/usr/bin/env node
	// npm install commander dotenv open
	// chmod +x refresher.js

	import http from 'http';
	import { program } from 'commander';
	import open from 'open';
	import dotenv from 'dotenv';
	import fs from 'fs';
	import crypto from 'crypto';

	# https://gist.github.com/wallabyway/3efdaf7ab52368cd019678b027055bd1
	import jwt
	import time
	import requests
	import os

	def generate_jwt_assertion(kid, private_key, APS_clientID, SSA_oxygenID, scope):
	current_time = int(time.time())
	claims = {
	"iss": APS_clientID,

	import googleIt from 'google-it';
	import axios from 'axios';
	import cheerio from 'cheerio';
	import OpenAI from 'openai';
	import readlineSync from 'readline-sync';

	const openai = new OpenAI({
	baseURL: "http://localhost:1234/v1",
	apiKey: 'My API Key'
	});

	/* jshint esversion: 8 */
	'use strict'
	const axios = require('axios');

	/**
	* Handler function that will process the data and return a result.
	*
	* @param {Object} data - Data to be processed.
	* @param {Objcct} logger - Can be used for any logging purposes.
	* @param {Function} logger.log - Use to log anything i.e. logger.log(Object \| String)

	{
	"name": "visual",
	"description": "default_template_value",
	"repository": {
	"type": "default_template_value",
	"url": "default_template_value"
	},
	"license": "MIT",
	"scripts": {
	"pbiviz": "pbiviz",

	<head>
	<title>Viewer Loading Multi-Model with GLB</title>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<link rel="icon" href="data:,">
	<style>
	#panel {
	position: fixed; z-index: 2; margin: 10px;
	font-family:arial; font-size:1.5em; }
	</style>