si3mshady

# Check existing PyTorch
!python -c "import torch, torchvision; print(f'PyTorch: {torch.__version__}, TorchVision: {torchvision.__version__}')"

# System packages
!apt-get update -qq
!apt-get install -y -qq git wget curl build-essential

# Clone MapAnything
!git clone https://github.com/facebookresearch/map-anything.git
%cd map-anything

si3mshady

IoT Water-Leak Detection (Audio) — AWS (Kinesis → Lambda → SageMaker → SNS/SQS)

This project deploys a lean, secure, and production-minded pipeline for detecting water leaks (e.g., a continuously running toilet) using short audio snippets analyzed by a pre-deployed SageMaker inference endpoint.

How it works

1. Edge device (Pi/PC/Phone) records a 1–2s audio snippet when a trigger fires (timer, sound level, manual), base64-encodes it, and sends a JSON payload to Kinesis Data Streams.
2. Lambda consumer reads Kinesis records, calls your SageMaker endpoint with the audio, and parses the model’s response.
3. If the model predicts your alert label (default: toilet) with confidence ≥ threshold (default: 0.5), Lambda publishes a JSON alert to SNS.
4. SQS subscribes to the topic so a Streamlit dashboard (or any worker) can read alerts reliably.

si3mshady

audioprep

audioprep is a simple command-line tool for preparing audio datasets for machine learning. It resamples, pads/trims, normalizes, and augments audio files, then exports WAVs and optional spectrogram / mel features. It also prints step-by-step explanations so you can see what’s happening.

---

✨ Features

si3mshady

#!/bin/bash

# === STEP 0: Update System ===
echo "[*] Updating system..."
sudo apt-get update && sudo apt-get upgrade -y

# === STEP 1: Install XFCE Desktop Environment ===
echo "[*] Installing XFCE4 desktop environment..."
sudo DEBIAN_FRONTEND=noninteractive apt-get install -y xfce4 xfce4-session

si3mshady

import os
import base64
from dotenv import load_dotenv
from crewai import Agent, Task, Crew
from crewai_tools import BaseTool
from google.auth.transport.requests import Request
from google.oauth2.credentials import Credentials
from google_auth_oauthlib.flow import InstalledAppFlow
from googleapiclient.discovery import build
from email.mime.text import MIMEText

si3mshady

apiVersion: v1
kind: List
items:
# Deployment for meal-app
- apiVersion: apps/v1
  kind: Deployment
  metadata:
    name: meal-app
  spec:
    replicas: 1

si3mshady

#!/bin/bash

DOCKER_USERNAME="si3mshady"
API_KEY="your_openai_api_key_here" # Replace with your actual OpenAI API key

# Create the project directory structure
mkdir -p prometheus grafana
mkdir -p meal_app/public drink_app/public workout_app/public nutrition_app/public

# Create Prometheus configuration file

si3mshady

#!/bin/bash

# Variables
LOCATION="centralus"                 # Azure region
RESOURCEGROUP="aro-rg"               # Resource group name
CLUSTER="aro-cluster"                # OpenShift cluster name
VNET="aro-vnet"                      # Virtual network name
MASTER_SUBNET="master-subnet"        # Master subnet name
WORKER_SUBNET="worker-subnet"        # Worker subnet name
PULL_SECRET_PATH="pull-secret.txt"   # Path to your Red Hat pull secret

si3mshady

import argparse
import time
from dronekit import connect, VehicleMode, LocationGlobalRelative, Command
from pymavlink import mavutil


def connect_drone(connection_string):
    """
    Connects to the drone and waits until the connection is established.
    """

si3mshady

import argparse
from dronekit import connect, VehicleMode, LocationGlobalRelative
import time
from pymavlink import mavutil

def connect_and_print_attributes(connection_string):
    print(f"Connecting to drone on {connection_string}...")

    # Connect to the vehicle
    try: