anotherjesse

package main

import (
	"fmt"
	"math/rand"
	"os"
	"os/signal"
)

const (

anotherjesse

seed: 0 output: The Complete Book of Modern Paisley (Paisley Collection)
seed: 1 output: The Complete Idiot's Guide to Doing Nothing
seed: 2 output: The Complete Guide to Cat Care
seed: 3 output: The Complete Idiot's Guide to Sewing, Stitching, Needlepoint, and More
seed: 4 output: The Complete Idiot's Guide to Tennis: Everything You Need to Know About Tennis, One Game at a Time
seed: 5 output: The Complete Guide to Building a Website (The Complete Guides)
seed: 6 output: The Complete Idiot's Guide to Baking
seed: 7 output: The Complete Guide to Bicycle Repair
seed: 8 output: The Complete Idiot's Guide to Gardening
seed: 9 output: Big Bird Singing Classic

anotherjesse

def generate(prompt, filename):
    # Simulate the generation process (you can replace this with your actual function)
    print(f"Generating '{prompt}' and saving as '{filename}'")

# Options for each category
expressions = ["happy", "sad", "crying", "angry", "open mouth"]
genders = ["male", "female", "non-binary"]
ages = ["kid", "teen", "young adult", "adult", "old"]
ethnicities = ["Caucasian", "Hispanic", "Black", "Middle-Eastern", "South Asian", "East Asian"]
styles = ["realistic", "cartoon", "comics"]

anotherjesse

A first pass at creating a @langchain vectorstore for each tweet I have "liked"

I already store them as a json blob on https://lets.m4ke.org/tweets

This ingests them documents - each looking like @twitter_user_name says tweet_text_here

Samples:

>>> qa.run("what is @jakedahn tweeting about")

' @jakedahn is tweeting about art and creativity.'

anotherjesse

polling

model = replicate.models.get("replicate/hello-world")
version = model.versions.get("5c7d5dc6dd8bf75c1acaa8565735e7986bc5b66206b55cca93cb72c9bf15ccaa")
inputs = {
  "text": "world",
}
output = version.predict(**inputs)

webhook

anotherjesse

import json
import random


db = {}


def get_data(kind, count=1):
    if kind not in db:
        with open("clip_interrogator/data/{}.txt".format(kind)) as f:

anotherjesse

big picture

Build your docker image in a way that works with replicate, while limiting image "layers" to less than 20GB.

Steps

Build COG without weights

First build a version of the cog without weights. To do this ensure remove extra files/weights from your cog directory and then build.

anotherjesse

!pip install diffusers==0.4.0
!pip install transformers  ftfy

from transformers import CLIPTokenizer, CLIPTextModel
import torch

torch_device = "cuda" if torch.cuda.is_available() else "cpu"  # or just let it be cpu

prompt = ["a photograph of an astronaut riding a horse"]

anotherjesse

#!/usr/bin/env ruby

require 'open-uri'
require 'rexml/document'
require 'rexml/xpath'

url = 'http://rpm.newrelic.com/accounts.xml?include=application_health'

headers = {'x-license-key' => 'YOUR LICENSE KEY'}

anotherjesse

import React, { useState } from "react";
import ReactDOM from "react-dom";
import { extend, Canvas } from "react-three-fiber";
import DatGui, { DatColor, DatNumber, DatSelect } from "react-dat-gui";
import { Text } from "troika-three-text";
import fonts from "./fonts";

import "react-dat-gui/build/react-dat-gui.css";
import "./styles.css";