| /* | |
| Written by: patpawlowski | |
| Created On: Oct 26, 2015 at 4:51:52 PM | |
| Description: This is a rough attempt to create a funciton to strip the markup from | |
| the Act TBL_NOTE.NOTETEXT field. It appears to work for what I need | |
| but could probably use some work with the escaped characters. | |
| It's not particularly fast but it is faster than other solutions I've come | |
| across. It takes about 4 seconds to parse 2700 records. |
| /* | |
| * This script will download a package (and all of its dependencies) from the | |
| * online NPM registry, then create a gzip'd tarball containing that package | |
| * and all of its dependencies. This archive can then be copied to a machine | |
| * without internet access and installed using npm. | |
| * | |
| * The idea is pretty simple: | |
| * - npm install [package] | |
| * - rewrite [package]/package.json to copy dependencies to bundleDependencies | |
| * - npm pack [package] |
| ''' ExportModel.py - TF-Serving | |
| # Basically we are wrapping your pretrained model | |
| # in a tensorflow serving compatible format. | |
| # This excepts base64 encoded png images and uses | |
| # them as input to your model. Then we convert | |
| # your models output into a png encoded image and | |
| # it gets returned by tensorflow serving base64 encoded. | |
| ''' | |
| import tensorflow as tf |
| # Having a requirements.txt file has follows | |
| torch==1.5.0 | |
| numpy==1.18.1 | |
| # Add channels. Last added is with the highest priorety | |
| conda config --add channels pytorch | |
| conda config --add channels conda-forge | |
| conda config --add channels anaconda | |
| # Install pip for fallback |
| pip install bert-for-tf2 | |
| pip install bert-tokenizer | |
| pip install tensorflow-hub | |
| pip install bert-tensorflow | |
| pip install sentencepiece | |
| import tensorflow_hub as hub | |
| import tensorflow as tf | |
| import bert |
Inspired from: https://github.com/vigente/gerardus/wiki/Integrate-git-diffs-with-word-docx-files
Usage:
cd /path/to/your/repo/with/docx
sh -c "$(curl -fsSL https://gist.githubusercontent.com/ttimasdf/7965baab972b901c1f8349ce5fcb5e09/raw/setup-git-hook.sh)"Done!
| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <link rel="stylesheet" href="gh-fork-ribbon.css"> | |
| <link rel="stylesheet" href="style.css" /> | |
| <style> | |
| .right .github-fork-ribbon { | |
| background-color: #333; | |
| } | |
| </style> |
| const tf = require('@tensorflow/tfjs-node'); | |
| const Jimp = require('jimp'); | |
| // Directory path for model files (model.json, metadata.json, weights.bin) | |
| // NOTE: It can be obtained from [Export Model] -> [Tensorflow.js] -> [Download my model] | |
| // on https://teachablemachine.withgoogle.com/train/image | |
| const MODEL_DIR_PATH = `${__dirname}`; | |
| // Path for image file to predict class | |
| const IMAGE_FILE_PATH = `${__dirname}/example.jpg`; |
| <html> | |
| <head> | |
| <title>Simple Line Graph using SVG and d3.js</title> | |
| <script src="http://mbostock.github.com/d3/d3.v2.js"></script> | |
| <style> | |
| /* tell the SVG path to be a thin blue line without any area fill */ | |
| path { | |
| stroke: steelblue; | |
| stroke-width: 1; | |
| fill: none; |
| # Pass the env-vars to MYCOMMAND | |
| eval $(egrep -v '^#' .env | xargs) MYCOMMAND | |
| # … or ... | |
| # Export the vars in .env into your shell: | |
| export $(egrep -v '^#' .env | xargs) |