Blake Jakopovic blakejakopovic

As an adaptive AI polymath, Amy possesses expert knowledge in every field and can provide accurate, detailed, insightful information on any topic. She is able to learn, reason, think independently, and continuously expand her capabilities to better understand and assist her user. She is happy to help with analysis and problem-solving, question answering, math, coding, creative writing, teaching, general discussion, and all sorts of other tasks. She is ready to take on any question or topic, drawing upon her vast knowledge base and reasoning skills to provide expert, in-depth, truthful answers.

Before providing any substantive response, Amy will always check:

Are additional information or context needed?
Are follow-up questions required?
What specific details would help provide the best possible answer?

If any of these apply, Amy will proactively ask for clarification before proceeding with her response.

Building Complex Drag & Drop Visual AI Workflows with React Flow and Python Flask

Created by @rUv

This tutorial demonstrates how to create a Python Flask implementation with React Flow to build complex AI workflows, reasoning systems, and comprehension modeling tools.

React Flow is a powerful open-source library for building interactive node-based interfaces in React applications. Its flexibility and extensibility make it an excellent choice for creating sophisticated AI applications.

Context

Sure.

	from transformers import AutoProcessor, MoonshineForConditionalGeneration
	from datasets import load_dataset

	processor = AutoProcessor.from_pretrained("UsefulSensors/moonshine-tiny")
	model = MoonshineForConditionalGeneration.from_pretrained("UsefulSensors/moonshine-tiny")

	ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
	inputs = processor([ds[0]["audio"]["array"], ds[0]["audio"]["array"]], return_tensors="pt")
	input_values = inputs.input_values

	#!/usr/bin/env bash

	# Default values for percentages
	DEFAULT_WIRED_LIMIT_PERCENT=85
	DEFAULT_WIRED_LWM_PERCENT=75

	# Read input parameters or use default values
	WIRED_LIMIT_PERCENT=${1:-$DEFAULT_WIRED_LIMIT_PERCENT}
	WIRED_LWM_PERCENT=${2:-$DEFAULT_WIRED_LWM_PERCENT}

	import pygame
	import random
	import math
	import os

	# Initialize Pygame
	pygame.init()

	# Screen dimensions
	WIDTH, HEIGHT = 800, 600

	<#meta#>
	- Date: 2023-10-05
	- Task: summary
	<#system#>
	Your main objective is to condense the content of the document into a concise summary, capturing the main points and themes.
	<#chat#>
	<#user#>
	To craft a Final Summary:

	1. Read Summarized Sections: Carefully review all the summarized sections of the document. Ensure that you have a clear understanding of the main points, key details, and essential information presented in each section.

	<#meta#>
	- Date: 2023-10-05
	- Task: summary
	<#system#>
	Your main objective is to condense the content of the document into a concise summary, capturing the main points and themes.
	<#chat#>
	<#user#>
	Please read the provided Original section to understand the context and content. Use this understanding to generate a summary of the Original section, incorporating relevant details and maintaining coherence with the Prior Summary.

	Notes:

	from langchain.agents import load_tools
	from langchain.agents import initialize_agent
	from langchain.agents import AgentType
	from langchain.llms import OpenAI

	llm = OpenAI(temperature=0, model="gpt-3.5-turbo-instruct")

	from metaphor_python import Metaphor

	client = Metaphor("")

Blake Jakopovic blakejakopovic

Building Complex Drag & Drop Visual AI Workflows with React Flow and Python Flask

hypothetical but relatively concrete