Yankuba Kuyateh Yankzy

Feature: Dynamic Form Rendering in Chat UI (Local Schema, API Submission, AI Notified by Status Only)

Background

We want our AI chat assistant to trigger dynamic forms (e.g., onboarding, surveys, KYC, feedback) as part of the chat conversation. The AI will send a message with a form name (not the schema itself). The frontend will maintain a local registry of form schemas and render the appropriate form inline in the chat UI when requested. When the user submits the form, the data should be sent to the backend API (not to the AI). Only the result/status should be sent back to the AI via WebSocket.

Goal

Maintain a local registry of form schemas in the frontend.
When the AI sends a message with a form name, dynamically render the corresponding form inline in the chat UI.

1. Set up your Google Cloud Project

Go to Google Cloud Console – Credentials page.
Select/create a project.
Click "+ CREATE CREDENTIALS > OAuth client ID".
Set Application type: Desktop app (recommended for local scripts).
Name it (e.g. "GmailCleanup").
Download the file as credentials.json.

	[
	{
	"id": "29e95298-0ec9-4a78-bbed-96553d664d1c",
	"cusip": null,
	"class": "us_equity",
	"exchange": "OTC",
	"symbol": "VRTT",
	"name": "VIATAR CTC SOLUTIONS INC Common Stock",
	"status": "inactive",
	"tradable": false,

	# -----------------------------------------------------------------------------
	# AI-powered Git Commit Function
	# Copy paste this gist into your ~/.bashrc or ~/.zshrc to gain the `gcm` command. It:
	# 1) gets the current staged changed diff
	# 2) sends them to an LLM to write the git commit message
	# 3) allows you to easily accept, edit, regenerate, cancel
	# But - just read and edit the code however you like
	# the `llm` CLI util is awesome, can get it here: https://llm.datasette.io/en/stable/

	gcm() {

	#! /bin/env python3
	""" A script to generate an Ontario Human Rights application form using GPT-4o API.

	Authorship Credits: ChatGpt: 60%, Myles Dear <[email protected]>: 40%
	Date last modified: Jul 11, 2024
	"""

	"""
	Initial prompt to generate script :

	import re
	from typing import Any, Dict, List, Pattern, Type, Union

	import graphene
	from graphene.types import Scalar
	from graphql.language import ast

	class CustomScalar(Scalar):
	@classmethod
	def __init_subclass_with_meta__(cls, validate_pattern: str = None, **options):

	// USING CSS
	.card {
	background: linear-gradient(135deg, #474c62, #383c4d);
	border-radius: 12px;
	box-shadow: 4px 4px 25px rgba(0, 0, 0, 0.4);
	overflow: hidden;

	.card-header {
	color: #fbfbfc;
	background: linear-gradient(135deg, #3c4256, #313747);


	interface ChatMessage {
	role: string;
	content: string;
	}

	export const chatOrchestrator = (
	chatHistory: ChatMessage[],
	setChatList: (chatList: ChatMessage[]) => void
	): void => {

	@import url("https://fonts.googleapis.com/css2?family=Alegreya+Sans:wght@100;300;400;500;700;800;900&display=swap");

	* {
	margin: 0;
	padding: 0;
	box-sizing: border-box;
	font-family: "Alegreya Sans", sans-serif;
	}

	body {

	import React, { useContext, useEffect, useRef } from "react";
	import { AuthContext } from "../context/AuthContext";
	import { ChatContext } from "../context/ChatContext";
	import Linkify from 'react-linkify';


	const Message = ({ message }) => {
	const { currentUser } = useContext(AuthContext);
	const { data } = useContext(ChatContext);