cceyda

#! python3.7

import argparse
import io
import os
import torch
from transformers import pipeline
import speech_recognition as sr

from datetime import datetime, timedelta

cceyda

/**
 * General-purpose NodeJS CLI/API wrapping the Stable-Diffusion python scripts.
 *
 * Note that this uses an older fork of stable-diffusion
 * with the 'txt2img.py' script, and that script was modified to
 * support the --outfile command.
 */

var { spawn, exec } = require("child_process");
var path = require("path");

cceyda

# Categorizer, by Peter Baylies (@pbaylies)
# Unsupervised categorization of generated or real images using deep features, dimensionality reduction, and clustering
import click
from tqdm import tqdm
import math
import numpy as np
import torch
import pickle
import PIL.Image
import os.path

cceyda

javascript:(async () => {
  /* while on the forks page, collect all the hrefs and pop off the first one (original repo) */
  const aTags = [...document.querySelectorAll('div.repo a:last-of-type')].slice(1);

  for (const aTag of aTags) {
    /* fetch the forked repo as html, search for the "This branch is [n commits ahead,] [m commits behind]", print it directly onto the web page */
    await fetch(aTag.href)
      .then(x => x.text())
      .then(html => aTag.outerHTML += `${html.match(/This branch is.*/).pop().replace('This branch is', '').replace(/([0-9]+ commits? ahead)/, '<font color="#0c0">$1</font>').replace(/([0-9]+ commits? behind)/, '<font color="red">$1</font>')}`)
      .catch(console.error);

cceyda

#!/usr/bin/env bash
 
sudo goaccess /var/log/caddy/logs/access.log \
  --html-pref='{"theme":"darkBlue","perPage":50,"layout":"vertical","showTables":true,"visitors":{"plot":{"chartType":"area-spline"}}}' \
  -o /full/path/to/new/report.html \
  --with-mouse \
  --hl-header \
  --with-output-resolver \
  --ignore-crawlers \
  --all-static-files \

cceyda

#!/bin/bash

IN=$1
OUT=$2

true ${SD_PARAMS:="-55dB:d=0.3"};
true ${MIN_FRAGMENT_DURATION:="20"};
export MIN_FRAGMENT_DURATION

if [ -z "$OUT" ]; then

cceyda

from graphviz import Digraph
import torch
from torch.autograd import Variable, Function

def iter_graph(root, callback):
    queue = [root]
    seen = set()
    while queue:
        fn = queue.pop()
        if fn in seen:

cceyda

pip3 install fastai==1.0.61
pip3 uninstall --force pillow -y

# install libjpeg-turbo to $HOME/turbojpeg
git clone https://github.com/libjpeg-turbo/libjpeg-turbo
pushd libjpeg-turbo
mkdir build
cd build
cmake .. -DCMAKE_INSTALL_PREFIX:PATH=$HOME/turbojpeg
make

cceyda

# -*- coding: utf-8 -*-
"""Parse Python source code and get or print docstrings."""

__all__ = ('get_docstrings', 'print_docstrings')

import ast

from itertools import groupby
from os.path import basename, splitext

cceyda

"""
Example of a Streamlit app for an interactive Prodigy dataset viewer that also lets you
run simple training experiments for NER and text classification.

Requires the Prodigy annotation tool to be installed: https://prodi.gy
See here for details on Streamlit: https://streamlit.io.
"""
import streamlit as st
from prodigy.components.db import connect
from prodigy.models.ner import EntityRecognizer, merge_spans, guess_batch_size