JMSwag

from flask import Flask, request, redirect, url_for, make_response, abort
from mongoengine.fields import get_db
from bson import ObjectId
from gridfs import GridFS
from gridfs.errors import NoFile
from <your_app> import app

@app.route('/files/<oid>')
def serve_gridfs_file(oid):
	try:

JMSwag

[program:btsync]
command=/usr/local/btsync/btsync --nodaemon --config /usr/local/btsync/sync.conf
user=<%= btuser %>
redirect_stderr=true
stdout_logfile=/tmp/btsync.log
stdout_logfile_maxbytes=1MB
stdout_logfile_backups=3

JMSwag

How to recover lost Python source code if it's still resident in-memory

I screwed up using git ("git checkout --" on the wrong file) and managed to delete the code I had just written... but it was still running in a process in a docker container. Here's how I got it back, using https://pypi.python.org/pypi/pyrasite/ and https://pypi.python.org/pypi/uncompyle6

Attach a shell to the docker container

Install GDB (needed by pyrasite)

apt-get update && apt-get install gdb

JMSwag

diff --git a/pyupdater/client/updates.py b/pyupdater/client/updates.py
index f610963..547e60e 100644
--- a/pyupdater/client/updates.py
+++ b/pyupdater/client/updates.py
@@ -624,16 +624,29 @@ class AppUpdate(LibUpdate):
                 temp_dir = get_mac_dot_app_dir(self._current_app_dir)
                 self._current_app_dir = temp_dir
 
-        app_update = os.path.join(self.update_folder, self.name)
+        #app_update = os.path.join(self.update_folder, self.name)

JMSwag

#!/bin/bash

# Kill transmission-daemon if it is running
transmission_da_pid=$(pgrep transmission-da)
if [ $transmission_da_pid ]; then
  killall transmission-daemon && echo "Closing existing tranmission-daemon processes ..." && sleep 8
fi

# Get VPN IP to bind to
bind_address=$(ip addr show tun0 | grep inet | awk '{print $2}')

JMSwag

Install OpenCV 4.1.0 on Raspbian Buster

$ chmod +x *.sh
$ ./download-opencv.sh
$ ./install-deps.sh
$ ./build-opencv.sh
$ cd ~/opencv/opencv-4.1.0/build
$ sudo make install

JMSwag

'use strict';

const parseExpression = require("./parse-expression");

function findAll(regex, target) {
	let results = [], match;
	
	while (match = regex.exec(target)) {
		results.push(match);
	}

JMSwag

Exploring Tokenizers from Hugging Face

Hugging Face (HF) has made NLP (Natural Language Processing) a breeze. In this post, we are going to take a look at tokenization using a hands on approach with the help of the Tokenizers library. We are going to load a real world dataset containing 10-K filings of public firms and see how to train a tokenizer from scratch based on the BERT tokenization scheme. In the process we will understand tokenization in detail and some gotchas to keep an eye out for.

Background on NLP (Optional)

If you already have an understanding of the NLP pipeline, you can safely skip this section.

For any NLP task, one of the first steps is pre-processing the data so that it can be fed into our NLP models. For those new to NLP, the general pipeline for any NLP task (text classification, question answering, etc.) is as follows: