bigsnarfdude

Running Flask on an Internal Network Interface

This guide explains how to configure a Flask application to run on a specific network interface on a macOS server with multiple NICs.

Prerequisites

• macOS server with multiple network interfaces
• Python installed
• Flask installed (pip install flask)

bigsnarfdude

#!/usr/bin/env python3

import sys
import os

# --- Configuration ---
# Define prefixes of directory paths to exclude.
# Any file whose path starts with one of these prefixes will be removed.
# Add or remove prefixes as needed.
EXCLUDE_PREFIXES = (

bigsnarfdude

How to get "Workshops" Rails application up and running on your local machine for development.

Goal: Set up a local development environment for the Workshops application using Docker.

1. Prerequisites:

• Git: To clone the repository.
• Docker: To build and run containerized applications. (Install Docker)
• Docker Compose: To manage multi-container Docker applications. It's usually included with Docker Desktop.

bigsnarfdude

Setting Up the Zoom Meeting SDK Linux Bot - Simple Guide for Beginners

Hey there! Let me walk you through setting up this Zoom bot on Linux in simple steps. This is perfect for someone who's just getting started with Linux.

What You'll Need

• A computer running Linux
• Docker installed
• A Zoom account
• Basic knowledge of the command line

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Installing Docker on Ubuntu can be done in a few ways, but the most common and recommended method is through Docker's official apt repository. Here's a breakdown of the process, combining key information from the search results:

Installation Using the Apt Repository:

This method ensures you get the latest stable version and simplifies future updates. Here's a step-by-step guide:

• 1. Update the package index and install necessary packages:
  • sudo apt-get update
  • sudo apt-get install ca-certificates curl gnupg
• 2. Add Docker's official GPG key:

bigsnarfdude

import os
import subprocess
import sys
from pathlib import Path

def process_video(input_file, output_file):
    """Process a video file to remove seconds 4-15."""
    cmd = [
        'ffmpeg',
        '-i', input_file,

bigsnarfdude

from flask import Flask, render_template
import altair as alt
import pandas as pd

app = Flask(__name__)

@app.route('/')
def index():
    return render_template('index.html')

bigsnarfdude

I need to refactor my application to better separate concerns between functionality and API endpoints. The project is a Flask-based API that processes and analyzes data.

Currently, I have these related components that need to be decoupled:

1. Data processing logic
2. API endpoint handling
3. Background task management

I'd like help with:

1. Separating core processing functionality from API endpoint handling
2. Creating cleaner interfaces between components

bigsnarfdude

import torch
from transformers import pipeline

pipe = pipeline(
    "image-text-to-text",
    device="mps",
    model="google/gemma-3-12b-it" # "google/gemma-3-27b-it"
)

bigsnarfdude

import json
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np
import dash
from dash import dcc, html
from dash.dependencies import Input, Output

# Load the data