Some of my favorite pastimes are learning new technologies and entertaining myself with audio, video and pictures. To this end, I travel in my RV with a tiny computer (Lenovo M73 Thinkcentre) running several guests under Proxmox. I use Jellyfin for my recorded movies and tv shows, home videos, music and photos. I have a file server with a USB hard drive for file storage. I use an ONN Android TV to stream video on my TV. I run Home Assistant mostly as a development stage for my home automation.

Private Network with Internet Access

With these servers, I need a private network. One nice solution is to install OpenWRT on an old dual band router and configure it as a travel router. The router has two radios – 2.4 GHz and 5 GHz. One is used for the private network and the other for connection to a public wifi network. I found that a better solution is to use a standard dual band router with a 5 GHz network extender. I use the extender to connect the router wan port to a public wifi network. If public wifi is not available, I use a mobile hotspot instead.

I need to learn something new today so I decided to live stream my security cameras. Frigate seems to be the goto for my Proxmox/Home Assistant setup. Frigate uses Real TIme Streaming Protocol (RTSP) via TCP. I use Wyzecams, Blink cameras and a Reolink video doorbell. The Wyzecams can be hacked to use RTSP but not the Blink cameras. The Reolink supports RTSP.

New Tapo Cameras

I went for cheap replacements for my old cameras with two Tapo C110’s (indoor) and two Tapo C113’s (outdoor). They both use RTSP natively and work with very low light. Two of my outdoor Blink cameras are battery operated and can be placed anywhere. However, battery operated cameras are not practical for live streaming. That means I’ll need to get creative with powering my outdoor cameras.

The first step was to get my new cameras on the network using the Tapo app.

I used a mostlychris video to get Frigate installed as a Proxmox container with a live stream of a Tapo camera.

Hardware Acceleration

My server is a Lenovo M73 Thinkcenter with an Intel Core i7-4765t which is a 4th generation (Haswell) processor.

Linux Containers (LXCs) are used for lightweight, efficient Linux environments that need full OS features. They share the host’s kernel and are great for resource-constrained servers. Hard drives cannot be passed through to LXCs. They are mounted on the host then use a Bind Mount to make them available to the LXC. The LXC .conf file (e.g. /etc/pve/lxc/100.conf) defines the mount point (e.g. mp0: /mnt/storage,mp=/mnt/storage).

By default, LXCs are created as unprivileged and file permissions show owner as nobody:nobody. Change the owner on the host to 100000:10000 (chown -R 100000:100000 /mnt/storage). Use ‘chmod -R 755 /mnt/storage’ to ensure read/write access.

The best practice for datacenter storage is to mount the drive on the host and use ‘directory’ when adding storage to datacenter.

When adding proxmox backup server, remove all usb devices first.

Download and install yt-dlp.exe from GitHub. Install FFmpeg.

Download video:

yt-dlp “https://www.youtube.com/watch?v=VIDEO_ID”

Audio only:

yt-dlp -x –audio-format mp3 “https://www.youtube.com/watch?v=VIDEO_ID”

List available formats:

yt-dlp -F “https://www.youtube.com/watch?v=VIDEO_ID”

What started out as a simple Python program to read and power a Hall Effect Flowmeter for my water filter ballooned into a major project. My original request was to write a Python program to record waterflow using a Digiten flowmeter. I envisioned a log file that I could view using SSH to the RPi. Each step of the way, ChatGPT suggested an improvement. I followed these improvements that included a web interface, weekly reports and https:// access with secure certificates. Wow!

I’ve recorded each of these improvements and plan to step through each iteration.

Basic Measurement with Log

I added the first version of the python program, installed the prerequisite libraries and ran the script hoping to see the log file it generated. Since the flowmeter sensor was not connected, it failed. I’ll connect it and try again.

The Gredia GR-208B registers 2,200 pulses per liter of water. The first program works!

Add Web Interface with Flask

ChatGPT suggested visual graphs and a web dashboard. The python 3 package manager, pip3, failed to install flask because created a conflict between pip3 and the linux package manager, apt. The best solution was to run flask inside a virtual environment. There is a new section in “Raspberry PI Water Flow Meter Installation”. This involved setting flask up as systemd service so that it starts automatically at boot.

The web version had an error that caused a “GPIO busy” error. Using the argument “debug=True” makes all GPIO’s busy. Removing it resolves the problem. The second program works!

Activate_this.py missing. Use:

activator = 'some/path/to/activate_this.py'  # Looted from virtualenv; should not require modification, since it's defined relatively
with open(activator) as f:
    exec(f.read(), {'__file__': activator})

What is Flask?

After a few fumbles, I corrected some errors and got a functioning web page. However, I didn’t understand the code, especially the Flask part. I found an excellent video with 2 million hits but it was five years old. Almost half way through, the examples didn’t work. I may try this video. This video from Udemy helped a lot. As much as ChatGPT and Gemini have helped, I need a better foundation. It’s probably time to go back to basics with the excellent 100 Days of Code: The Complete Python Pro Bootcamp with Dr. Angela Yu.

Sending Email from Python (Gemini)

The absolute simplest way to send a message from Python, without relying on external services or complex setups, is to send an email. While not an SMS, it’s often a suitable alternative for simple notifications. Here’s how:

Using Python’s smtplib and email modules:

Python

import smtplib
from email.mime.text import MIMEText

def send_email(sender_email, sender_password, recipient_email, subject, message):
    """Sends an email message."""

    msg = MIMEText(message)
    msg['Subject'] = subject
    msg['From'] = sender_email
    msg['To'] = recipient_email

    try:
        with smtplib.SMTP_SSL('smtp.gmail.com', 465) as server: #Or your email server
            server.login(sender_email, sender_password)
            server.send_message(msg)
        print("Email sent successfully!")
    except Exception as e:
        print(f"Error sending email: {e}")

# Example usage (replace with your details)
sender_email = "your_email@gmail.com" # Or other email provider
sender_password = "your_email_password" # Or app password.
recipient_email = "recipient@example.com"
subject = "Python Message"
message = "This is a simple message from Python."

send_email(sender_email, sender_password, recipient_email, subject, message)

Explanation and Important Notes:

1. Import Modules:
   • smtplib: For sending emails using the Simple Mail Transfer Protocol (SMTP).
   • email.mime.text.MIMEText: For creating email messages.
2. send_email Function:
   • Takes sender, recipient, subject, and message as input.
   • Creates a MIMEText object to represent the email.
   • Sets the subject, sender, and recipient headers.
   • Uses smtplib.SMTP_SSL to connect to an SMTP server (in this example, Gmail’s).
   • Logs in using the sender’s email and password.
   • Sends the message using server.send_message().
   • Includes error handling using a try...except block.
3. Example Usage:
   • Replace the placeholder email addresses and password with your actual credentials.
   • Change the subject and message as needed.

Key Considerations:

• Email Provider:
  • You’ll need to use the correct SMTP server and port for your email provider (e.g., smtp.outlook.com for Outlook, smtp.mail.yahoo.com for Yahoo).
  • Port 465 is commonly used for SMTP over SSL.
• Security:
  • Never hardcode your actual email password directly into your scripts. This is a major security risk.
  • For Gmail, you’ll likely need to generate an “app password” in your Google account settings. This is a password specifically for applications.
  • Consider using environment variables or a configuration file to store your credentials more securely.
• Two-Factor Authentication (2FA):
  • If you have 2FA enabled on your email account, you’ll need to use an app password.
• Spam Filters:
  • Emails sent from scripts might sometimes end up in spam folders.

Advantages:

• Simplicity: No external services or complex configurations are required.
• Built-in Modules: Python’s standard library provides the necessary tools.

Disadvantages:

Security: Requires care to protect your email credentials.

Not SMS: This sends emails, not SMS messages.

Configuration: Requires some configuration related to your email provider.

First, I should admit that I’ve lost all control with ChatGPT. Second, programming a Raspberry Pi with Python to read the output of an inexpensive Hall Effect Flow Meter so I know when to change my water filter is a really cool project. Third, I could just replace my water filter every six months and not worry about it, but that’s not me. So I’ll probably exceed my WordPress and ChatGPT limits, but here goes. First, a list of the sections:

Basic Water Flow Monitor
Water Flow Monitor with Web Access
Water Flow Monitor with Alerts
Water Flow Monitor with Email Notifications
Water Flow Monitor with Logging
Water Flow Monitor with Graphing
Water Flow Monitor with Email Alerts
Water Flow Monitor with Reports
How to Power Water Flow Sensor
Gredia Sensor Changes from Digiten Sensor
Implement Virtual Environment for Flask
Set Program as Systemd Service
Set Up Reverse Proxy
Set Up SSL Certificate and Authentication
Set Up Login Page in Flask
Restrict Access to Logged-in Users
Deployment with Gunicorn & Nginx
How to backup an image of the Raspberry Pi SD (from Gemini)

I say:

Write a python program for a raspberry pi that uses a Digiten water hall effect flow sensor meter that records water flow.

Basic Water Flow Monitor

ChatGPT says:

Here’s a Python program for a Raspberry Pi that reads data from a DIGITEN water flow sensor (Hall effect sensor) and records the water flow in real-time.

🔹 How It Works:

• Uses GPIO interrupts to count pulses from the Hall effect sensor.
• Calculates the flow rate (L/min) and total water used (Liters).
• Records data to a CSV file for logging.

🔹 Hardware Setup:

• Connect the flow sensor’s wires to the Raspberry Pi:
  • Red → 3.3V or 5V
  • Black → Ground (GND)
  • Yellow → GPIO pin (e.g., GPIO 17)

🔹 Python Code for Raspberry Pi

import RPi.GPIO as GPIO
import time
import csv

# GPIO pin connected to the yellow signal wire of the flow sensor
FLOW_SENSOR_PIN = 17  

# Flow sensor calibration factor (from manufacturer, usually ~7.5 pulses per liter)
CALIBRATION_FACTOR = 7.5  

# Variables
pulse_count = 0
total_liters = 0.0

# Setup GPIO
GPIO.setmode(GPIO.BCM)
GPIO.setup(FLOW_SENSOR_PIN, GPIO.IN, pull_up_down=GPIO.PUD_UP)

def pulse_callback(channel):
    """ Interrupt function to count pulses from flow sensor """
    global pulse_count
    pulse_count += 1

# Attach interrupt to count pulses
GPIO.add_event_detect(FLOW_SENSOR_PIN, GPIO.FALLING, callback=pulse_callback)

# CSV logging setup
csv_filename = "water_flow_log.csv"
with open(csv_filename, "a", newline="") as file:
    writer = csv.writer(file)
    writer.writerow(["Timestamp", "Flow Rate (L/min)", "Total Liters"])

try:
    print("Monitoring water flow... Press Ctrl+C to stop.")
    while True:
        start_time = time.time()
        pulse_count = 0  # Reset pulse count
        
        time.sleep(1)  # Measure over 1-second intervals
        
        # Calculate flow rate (L/min)
        flow_rate = (pulse_count / CALIBRATION_FACTOR) * 60  
        
        # Update total liters used
        total_liters += (pulse_count / CALIBRATION_FACTOR)
        
        # Log data
        timestamp = time.strftime("%Y-%m-%d %H:%M:%S")
        with open(csv_filename, "a", newline="") as file:
            writer = csv.writer(file)
            writer.writerow([timestamp, round(flow_rate, 2), round(total_liters, 2)])
        
        # Display in terminal
        print(f"[{timestamp}] Flow Rate: {flow_rate:.2f} L/min | Total: {total_liters:.2f} L")

except KeyboardInterrupt:
    print("\nStopping water flow monitoring.")
finally:
    GPIO.cleanup()

🔹 How to Use the Program

1. Connect the flow sensor to your Raspberry Pi.
2. Install required libraries (old Raspberry Pi): sudo apt update sudo apt install python3-rpi.gpio
3. Install required libraries (new Raspberry Pi): sudo apt update sudo apt install python3-rpi.lgpio
4. Run the script: python3 flow_sensor.py
5. View data log in water_flow_log.csv.
6. Add pip3: sudo apt-get install python3-pip

🔹 Features

✅ Uses GPIO interrupts for accurate counting.
✅ Calculates flow rate (L/min) and total water usage (Liters).
✅ Logs data in a CSV file with timestamps.
✅ Efficient & real-time monitoring with minimal CPU usage.

Would you like to extend this with visual graphs or a web dashboard? 🚀

Water Flow Monitor with Web Access

Because of a conflict between apt and pip3, Flask should be installed in a virtual environment. Jump forward for how to setup the virtual environment.

To extend the Raspberry Pi water flow monitoring system with real-time graphs and a web dashboard, we can use Flask (for a web interface) and Plotly/Dash for dynamic graphs.

🔹 Plan for the Web Dashboard

1. Backend (Flask): Reads data from the flow sensor and stores it in a CSV file.
2. Frontend (Dash/Plotly): Displays real-time graphs of flow rate and total water usage.
3. Web Access: You can view live data from any device on the same network.

🔹 Install Required Libraries

Before running the new version, install the necessary Python packages:

sudo apt update
pip3 install flask dash pandas plotly RPi.GPIO #for older RPi's
pip3 install flask dash pandas plotly rpi-lgpio # for newer RPi's

🔹 Updated Python Code (with Flask & Dash for Graphs)

This script runs both the water flow sensor and a web dashboard for real-time monitoring.

import RPi.GPIO as GPIO
import time
import csv
import pandas as pd
from flask import Flask
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output

# GPIO Setup
FLOW_SENSOR_PIN = 17  
CALIBRATION_FACTOR = 7.5  

pulse_count = 0
total_liters = 0.0
csv_filename = "water_flow_log.csv"

GPIO.setmode(GPIO.BCM)
GPIO.setup(FLOW_SENSOR_PIN, GPIO.IN, pull_up_down=GPIO.PUD_UP)

def pulse_callback(channel):
    """ Interrupt function to count pulses from flow sensor """
    global pulse_count
    pulse_count += 1

# Attach interrupt to count pulses
GPIO.add_event_detect(FLOW_SENSOR_PIN, GPIO.FALLING, callback=pulse_callback)

# Initialize Flask and Dash
server = Flask(__name__)
app = dash.Dash(__name__, server=server)

# Dash Layout
app.layout = html.Div(children=[
    html.H1("Water Flow Monitoring Dashboard"),
    dcc.Graph(id="live-graph"),
    dcc.Interval(id="interval-component", interval=3000, n_intervals=0)  # Updates every 3 sec
])

# Background function to log data
def log_data():
    global pulse_count, total_liters
    with open(csv_filename, "a", newline="") as file:
        writer = csv.writer(file)
        writer.writerow(["Timestamp", "Flow Rate (L/min)", "Total Liters"])

    try:
        while True:
            start_time = time.time()
            pulse_count = 0
            time.sleep(1)
            
            flow_rate = (pulse_count / CALIBRATION_FACTOR) * 60
            total_liters += (pulse_count / CALIBRATION_FACTOR)

            timestamp = time.strftime("%Y-%m-%d %H:%M:%S")
            with open(csv_filename, "a", newline="") as file:
                writer = csv.writer(file)
                writer.writerow([timestamp, round(flow_rate, 2), round(total_liters, 2)])
            
            print(f"[{timestamp}] Flow Rate: {flow_rate:.2f} L/min | Total: {total_liters:.2f} L")

    except KeyboardInterrupt:
        GPIO.cleanup()

# Dash Callback for Graph Update
@app.callback(
    Output("live-graph", "figure"),
    Input("interval-component", "n_intervals")
)
def update_graph(n):
    df = pd.read_csv(csv_filename)
    fig = {
        "data": [
            {"x": df["Timestamp"], "y": df["Flow Rate (L/min)"], "type": "line", "name": "Flow Rate"},
            {"x": df["Timestamp"], "y": df["Total Liters"], "type": "line", "name": "Total Usage"}
        ],
        "layout": {"title": "Water Flow Data"}
    }
    return fig

if __name__ == "__main__":
    from threading import Thread
    Thread(target=log_data, daemon=True).start()
    app.run_server(debug=True, host="0.0.0.0", port=8050)

🔹 How to Use

1. Run the script on your Raspberry Pi: python3 flow_monitor_dashboard.py
2. Access the dashboard from any device on the same network: http://<Raspberry_Pi_IP>:8050 (Replace <Raspberry_Pi_IP> with your Pi’s local IP address, e.g., 192.168.1.10:8050)

🔹 Features of This System

✅ Live Water Flow Graphs
✅ Historical Data Logging in CSV
✅ Access Dashboard from Any Device
✅ Efficient & Real-Time Updates

Would you like any additional features, such as alerts or mobile notifications? 🚀

Water Flow Monitor with Alerts

You can add alerts and mobile notifications to your Raspberry Pi water flow monitoring system using different methods. Here are some simple approaches:

🔹 Option 1: Send Notifications via Telegram (Recommended)

Why Telegram?

✅ Free & easy to set up
✅ Works on Android, iOS, PC, and Web
✅ Supports messages and images
✅ No need for extra apps if you already use Telegram

🔹 Steps to Set Up Telegram Alerts

1️⃣ Create a Telegram Bot

1. Open Telegram and search for @BotFather.
2. Start the chat and send: /newbot
3. Follow the prompts and name your bot.
4. Copy the bot token provided (e.g., 123456789:ABCdefGhIjklMNO).

2️⃣ Get Your Chat ID

1. Open https://api.telegram.org/bot<TOKEN>/getUpdates
   (Replace <TOKEN> with your bot token.)
2. Send a message to your bot.
3. Reload the URL above; your chat ID will be in the response ("chat": {"id": XXXXXXX}).

3️⃣ Add Telegram Notifications to Your Python Code

Modify your script to send an alert if the water flow exceeds a threshold:

import requests

# Replace with your Telegram bot token and chat ID
TELEGRAM_BOT_TOKEN = "123456789:ABCdefGhIjklMNO"
CHAT_ID = "XXXXXXXXX"

def send_telegram_alert(message):
    url = f"https://api.telegram.org/bot{TELEGRAM_BOT_TOKEN}/sendMessage"
    payload = {"chat_id": CHAT_ID, "text": message}
    requests.post(url, json=payload)

# Example: Send alert if flow rate exceeds 10 L/min
if flow_rate > 10:
    send_telegram_alert(f"🚨 High Water Flow Alert! 🚰 Rate: {flow_rate:.2f} L/min")

🔹 Option 2: Email Alerts via Gmail

Steps:

1. Enable “Less Secure Apps” on your Gmail account.
2. Install smtplib (already included in Python).
3. Modify your script to send emails:

import smtplib

EMAIL = "your-email@gmail.com"
PASSWORD = "your-email-password"
TO_EMAIL = "recipient-email@gmail.com"

def send_email_alert(message):
    subject = "🚨 Water Flow Alert!"
    body = f"Alert: {message}"
    email_message = f"Subject: {subject}\n\n{body}"

    with smtplib.SMTP_SSL("smtp.gmail.com", 465) as server:
        server.login(EMAIL, PASSWORD)
        server.sendmail(EMAIL, TO_EMAIL, email_message)

# Example usage
if flow_rate > 10:
    send_email_alert(f"High water flow detected: {flow_rate:.2f} L/min")

🔹 Option 3: Push Notifications via Pushover

Why Pushover?

✅ Simple setup
✅ Works on all devices
✅ Free for basic use

Steps:

1. Create a Pushover account at pushover.net.
2. Get an API Key and User Key.
3. Install requests: pip3 install requests
4. Add this function to send notifications:

import requests

PUSHOVER_USER_KEY = "your-user-key"
PUSHOVER_API_KEY = "your-api-key"

def send_pushover_alert(message):
    url = "https://api.pushover.net/1/messages.json"
    payload = {
        "token": PUSHOVER_API_KEY,
        "user": PUSHOVER_USER_KEY,
        "message": message
    }
    requests.post(url, data=payload)

# Example usage
if flow_rate > 10:
    send_pushover_alert(f"High Water Flow: {flow_rate:.2f} L/min 🚰")

🔹 Which One Should You Use?

• ✅ Telegram: Easiest and most flexible
• ✅ Email: Good if you prefer inbox notifications
• ✅ Pushover: Great for mobile push notifications

Water Flow Monitor with Email Notifications

Would you like me to integrate one of these directly into your water monitoring script? 🚀

I’ll integrate Telegram notifications into your Raspberry Pi water flow monitoring script since it’s the easiest and most reliable method. Here’s the full script:

🔹 Features

✅ Reads flow rate from the DIGITEN water flow sensor
✅ Sends Telegram alerts when water flow exceeds a threshold
✅ Runs on a Raspberry Pi using GPIO & interrupts
✅ Customizable threshold for alerts

🔹 Steps to Use

1️⃣ Get a Telegram Bot Token

1. Open Telegram and search for @BotFather.
2. Send /newbot and follow the instructions.
3. Copy the bot token (e.g., 123456789:ABCdefGhIjklMNO).

2️⃣ Get Your Chat ID

1. Visit: https://api.telegram.org/bot<TOKEN>/getUpdates (Replace <TOKEN> with your bot token.)
2. Send a message to your bot.
3. Reload the URL above and find your "chat": {"id": XXXXXXX}.

3️⃣ Install Required Libraries

Run this command on your Raspberry Pi:

pip3 install RPi.GPIO requests

4️⃣ Full Python Script

import RPi.GPIO as GPIO
import time
import requests

# Telegram Bot Credentials
TELEGRAM_BOT_TOKEN = "123456789:ABCdefGhIjklMNO"  # Replace with your bot token
CHAT_ID = "XXXXXXXXX"  # Replace with your Telegram chat ID

# Flow Sensor Setup
FLOW_SENSOR_PIN = 17  # GPIO pin where the sensor signal is connected
FLOW_CALIBRATION = 7.5  # Adjust based on your sensor (L/pulse)
flow_rate = 0
pulse_count = 0

def send_telegram_alert(message):
    """ Sends an alert message via Telegram """
    url = f"https://api.telegram.org/bot{TELEGRAM_BOT_TOKEN}/sendMessage"
    payload = {"chat_id": CHAT_ID, "text": message}
    requests.post(url, json=payload)

def pulse_callback(channel):
    """ Interrupt function that counts pulses """
    global pulse_count
    pulse_count += 1

# GPIO Setup
GPIO.setmode(GPIO.BCM)
GPIO.setup(FLOW_SENSOR_PIN, GPIO.IN, pull_up_down=GPIO.PUD_UP)
GPIO.add_event_detect(FLOW_SENSOR_PIN, GPIO.FALLING, callback=pulse_callback)

try:
    print("🔹 Water Flow Monitoring Started...")
    while True:
        pulse_count = 0  # Reset count
        time.sleep(1)  # Wait 1 second
        flow_rate = (pulse_count / FLOW_CALIBRATION) * 60  # Convert pulses to L/min
        
        # Print flow rate to console
        print(f"💧 Flow Rate: {flow_rate:.2f} L/min")
        
        # Send alert if flow rate is too high
        if flow_rate > 10:  # Change threshold as needed
            send_telegram_alert(f"🚨 High Water Flow Alert! 🚰 {flow_rate:.2f} L/min")

except KeyboardInterrupt:
    print("\n🔻 Stopping Monitoring...")
    GPIO.cleanup()

🔹 How It Works

1. Reads pulses from the DIGITEN water flow sensor.
2. Converts pulses into liters per minute (L/min).
3. If flow exceeds 10 L/min, sends a Telegram alert.
4. Runs continuously until stopped (Ctrl + C).

🔹 Customization

• Change the flow threshold in this line: if flow_rate > 10: (Example: Set it to 15 if you want alerts at 15 L/min.)
• Change GPIO pin (FLOW_SENSOR_PIN = 17) if using a different pin.

🔹 Running the Script

Save the script as flow_monitor.py and run it with:

python3 flow_monitor.py

Your Raspberry Pi will now monitor water flow and send alerts to Telegram! 🚀

Would you like me to add logging to a file or historical tracking?

Water Flow Monitor with Logging

I’ll modify the script to log water flow data to a CSV file and track historical records. This will allow you to analyze trends over time.

🔹 New Features

✅ Saves flow data to flow_log.csv
✅ Includes timestamp, flow rate, and alert status
✅ Keeps track of max flow rate
✅ Telegram alerts still work

🔹 Full Python Script with Logging

import RPi.GPIO as GPIO
import time
import requests
import csv
import os
from datetime import datetime

# Telegram Bot Credentials
TELEGRAM_BOT_TOKEN = "123456789:ABCdefGhIjklMNO"  # Replace with your bot token
CHAT_ID = "XXXXXXXXX"  # Replace with your Telegram chat ID

# Flow Sensor Setup
FLOW_SENSOR_PIN = 17  # GPIO pin where the sensor signal is connected
FLOW_CALIBRATION = 7.5  # Adjust based on your sensor (L/pulse)
flow_rate = 0
pulse_count = 0
MAX_FLOW_RATE = 0  # Track highest recorded flow rate

# CSV Log File
LOG_FILE = "flow_log.csv"

def send_telegram_alert(message):
    """ Sends an alert message via Telegram """
    url = f"https://api.telegram.org/bot{TELEGRAM_BOT_TOKEN}/sendMessage"
    payload = {"chat_id": CHAT_ID, "text": message}
    requests.post(url, json=payload)

def pulse_callback(channel):
    """ Interrupt function that counts pulses """
    global pulse_count
    pulse_count += 1

def log_data(timestamp, flow_rate, alert_sent):
    """ Logs water flow data to CSV file """
    file_exists = os.path.isfile(LOG_FILE)

    with open(LOG_FILE, mode="a", newline="") as file:
        writer = csv.writer(file)
        
        # Write header if file is new
        if not file_exists:
            writer.writerow(["Timestamp", "Flow Rate (L/min)", "Alert Sent"])
        
        # Write data row
        writer.writerow([timestamp, flow_rate, alert_sent])

# GPIO Setup
GPIO.setmode(GPIO.BCM)
GPIO.setup(FLOW_SENSOR_PIN, GPIO.IN, pull_up_down=GPIO.PUD_UP)
GPIO.add_event_detect(FLOW_SENSOR_PIN, GPIO.FALLING, callback=pulse_callback)

try:
    print("🔹 Water Flow Monitoring Started...")
    
    while True:
        pulse_count = 0  # Reset count
        time.sleep(1)  # Wait 1 second
        flow_rate = (pulse_count / FLOW_CALIBRATION) * 60  # Convert pulses to L/min
        
        # Track max flow rate
        global MAX_FLOW_RATE
        if flow_rate > MAX_FLOW_RATE:
            MAX_FLOW_RATE = flow_rate
        
        # Print to console
        print(f"{datetime.now()} 💧 Flow Rate: {flow_rate:.2f} L/min (Max: {MAX_FLOW_RATE:.2f} L/min)")

        # Check for high flow rate and send alert
        alert_sent = "No"
        if flow_rate > 10:  # Adjust threshold as needed
            alert_sent = "Yes"
            send_telegram_alert(f"🚨 High Water Flow Alert! 🚰 {flow_rate:.2f} L/min")

        # Log data
        log_data(datetime.now().strftime("%Y-%m-%d %H:%M:%S"), flow_rate, alert_sent)

except KeyboardInterrupt:
    print("\n🔻 Stopping Monitoring...")
    GPIO.cleanup()

🔹 How It Works

1. Reads water flow rate every second.
2. Saves data to flow_log.csv with a timestamp.
3. Tracks the highest recorded flow rate.
4. Sends a Telegram alert if the flow rate exceeds 10 L/min.

🔹 Example CSV Output (flow_log.csv)

Timestamp,Flow Rate (L/min),Alert Sent
2025-03-03 12:00:01,3.50,No
2025-03-03 12:00:02,4.20,No
2025-03-03 12:00:03,12.10,Yes
2025-03-03 12:00:04,11.75,Yes

🔹 Running the Script

Run this command on your Raspberry Pi:

python3 flow_monitor.py

Your Raspberry Pi will now log all water flow data and track the highest flow rate.

Would you like me to add graphing capabilities so you can visualize the data? 📊

Water Flow Monitor with Graphing

I’ll add graphing capabilities using Matplotlib so you can visualize your water flow data. The script will:

✅ Generate real-time graphs of water flow
✅ Plot historical data from flow_log.csv
✅ Automatically update the graph every few seconds

🔹 Install Matplotlib

Run this on your Raspberry Pi:

pip3 install matplotlib pandas

🔹 Updated Script with Graphing

import RPi.GPIO as GPIO
import time
import requests
import csv
import os
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from datetime import datetime

# Telegram Bot Credentials
TELEGRAM_BOT_TOKEN = "123456789:ABCdefGhIjklMNO"  # Replace with your bot token
CHAT_ID = "XXXXXXXXX"  # Replace with your Telegram chat ID

# Flow Sensor Setup
FLOW_SENSOR_PIN = 17  # GPIO pin where the sensor signal is connected
FLOW_CALIBRATION = 7.5  # Adjust based on your sensor (L/pulse)
flow_rate = 0
pulse_count = 0
MAX_FLOW_RATE = 0  # Track highest recorded flow rate

# CSV Log File
LOG_FILE = "flow_log.csv"

def send_telegram_alert(message):
    """ Sends an alert message via Telegram """
    url = f"https://api.telegram.org/bot{TELEGRAM_BOT_TOKEN}/sendMessage"
    payload = {"chat_id": CHAT_ID, "text": message}
    requests.post(url, json=payload)

def pulse_callback(channel):
    """ Interrupt function that counts pulses """
    global pulse_count
    pulse_count += 1

def log_data(timestamp, flow_rate, alert_sent):
    """ Logs water flow data to CSV file """
    file_exists = os.path.isfile(LOG_FILE)

    with open(LOG_FILE, mode="a", newline="") as file:
        writer = csv.writer(file)
        
        # Write header if file is new
        if not file_exists:
            writer.writerow(["Timestamp", "Flow Rate (L/min)", "Alert Sent"])
        
        # Write data row
        writer.writerow([timestamp, flow_rate, alert_sent])

def read_csv_data():
    """ Reads CSV file and returns data as a DataFrame """
    if os.path.exists(LOG_FILE):
        df = pd.read_csv(LOG_FILE)
        return df
    return None

# Graphing Function
def update_graph(frame):
    """ Updates the real-time graph from CSV data """
    plt.clf()
    df = read_csv_data()
    
    if df is not None and not df.empty:
        df["Timestamp"] = pd.to_datetime(df["Timestamp"])
        df["Flow Rate (L/min)"] = df["Flow Rate (L/min)"].astype(float)
        
        plt.plot(df["Timestamp"], df["Flow Rate (L/min)"], label="Flow Rate (L/min)", color="b")
        plt.axhline(y=10, color='r', linestyle='--', label="Alert Threshold (10 L/min)")
        
        plt.xlabel("Time")
        plt.ylabel("Flow Rate (L/min)")
        plt.title("Water Flow Monitoring")
        plt.xticks(rotation=45)
        plt.legend()
        plt.grid()

# GPIO Setup
GPIO.setmode(GPIO.BCM)
GPIO.setup(FLOW_SENSOR_PIN, GPIO.IN, pull_up_down=GPIO.PUD_UP)
GPIO.add_event_detect(FLOW_SENSOR_PIN, GPIO.FALLING, callback=pulse_callback)

try:
    print("🔹 Water Flow Monitoring Started...")
    
    fig = plt.figure()
    ani = animation.FuncAnimation(fig, update_graph, interval=5000)  # Update every 5 seconds
    plt.show(block=False)

    while True:
        pulse_count = 0  # Reset count
        time.sleep(1)  # Wait 1 second
        flow_rate = (pulse_count / FLOW_CALIBRATION) * 60  # Convert pulses to L/min
        
        # Track max flow rate
        global MAX_FLOW_RATE
        if flow_rate > MAX_FLOW_RATE:
            MAX_FLOW_RATE = flow_rate
        
        # Print to console
        print(f"{datetime.now()} 💧 Flow Rate: {flow_rate:.2f} L/min (Max: {MAX_FLOW_RATE:.2f} L/min)")

        # Check for high flow rate and send alert
        alert_sent = "No"
        if flow_rate > 10:  # Adjust threshold as needed
            alert_sent = "Yes"
            send_telegram_alert(f"🚨 High Water Flow Alert! 🚰 {flow_rate:.2f} L/min")

        # Log data
        log_data(datetime.now().strftime("%Y-%m-%d %H:%M:%S"), flow_rate, alert_sent)

except KeyboardInterrupt:
    print("\n🔻 Stopping Monitoring...")
    GPIO.cleanup()

🔹 How It Works

1. Monitors water flow and logs it to flow_log.csv
2. Plots real-time water flow data in a graph
3. Red dashed line at 10 L/min shows the alert threshold
4. Graph updates every 5 seconds
5. Runs in the background while monitoring

🔹 Running the Script

Run this command on your Raspberry Pi:

python3 flow_monitor.py

You’ll see a real-time graph pop up tracking the water flow over time. 📈

Would you like an email alert option too? 📩

Water Flow Monitor with Email Alerts

I’ll add email alerts using SMTP with Gmail so you can receive notifications when water flow exceeds a set threshold.

🔹 What’s New?

✅ Email alerts when water flow is high 🚨
✅ Works alongside Telegram alerts 📲
✅ Uses SMTP for Gmail or any other provider

🔹 Setup Email Alerts

Before running the script, enable “Less Secure Apps” or create an App Password in your email settings.

For Gmail, go to: Google App Passwords
Generate a password and use it instead of your regular password in the script.

🔹 Install Required Package

Run this on your Raspberry Pi:

pip3 install smtplib

🔹 Updated Python Script with Email Alerts

import RPi.GPIO as GPIO
import time
import requests
import csv
import os
import smtplib
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from datetime import datetime
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart

# Telegram Bot Credentials
TELEGRAM_BOT_TOKEN = "123456789:ABCdefGhIjklMNO"  # Replace with your bot token
CHAT_ID = "XXXXXXXXX"  # Replace with your Telegram chat ID

# Email Credentials
SMTP_SERVER = "smtp.gmail.com"  # Change if using Outlook, Yahoo, etc.
SMTP_PORT = 587
EMAIL_ADDRESS = "your_email@gmail.com"  # Replace with your email
EMAIL_PASSWORD = "your_app_password"  # Replace with App Password

# Flow Sensor Setup
FLOW_SENSOR_PIN = 17  # GPIO pin where the sensor signal is connected
FLOW_CALIBRATION = 7.5  # Adjust based on your sensor (L/pulse)
flow_rate = 0
pulse_count = 0
MAX_FLOW_RATE = 0  # Track highest recorded flow rate

# CSV Log File
LOG_FILE = "flow_log.csv"

def send_telegram_alert(message):
    """ Sends an alert message via Telegram """
    url = f"https://api.telegram.org/bot{TELEGRAM_BOT_TOKEN}/sendMessage"
    payload = {"chat_id": CHAT_ID, "text": message}
    requests.post(url, json=payload)

def send_email_alert(subject, body):
    """ Sends an email alert """
    try:
        msg = MIMEMultipart()
        msg["From"] = EMAIL_ADDRESS
        msg["To"] = EMAIL_ADDRESS  # Send email to yourself (or modify to send to others)
        msg["Subject"] = subject
        msg.attach(MIMEText(body, "plain"))

        server = smtplib.SMTP(SMTP_SERVER, SMTP_PORT)
        server.starttls()
        server.login(EMAIL_ADDRESS, EMAIL_PASSWORD)
        server.sendmail(EMAIL_ADDRESS, EMAIL_ADDRESS, msg.as_string())
        server.quit()
        
        print("📧 Email Alert Sent!")
    except Exception as e:
        print(f"❌ Email Alert Failed: {e}")

def pulse_callback(channel):
    """ Interrupt function that counts pulses """
    global pulse_count
    pulse_count += 1

def log_data(timestamp, flow_rate, alert_sent):
    """ Logs water flow data to CSV file """
    file_exists = os.path.isfile(LOG_FILE)

    with open(LOG_FILE, mode="a", newline="") as file:
        writer = csv.writer(file)
        
        # Write header if file is new
        if not file_exists:
            writer.writerow(["Timestamp", "Flow Rate (L/min)", "Alert Sent"])
        
        # Write data row
        writer.writerow([timestamp, flow_rate, alert_sent])

def read_csv_data():
    """ Reads CSV file and returns data as a DataFrame """
    if os.path.exists(LOG_FILE):
        df = pd.read_csv(LOG_FILE)
        return df
    return None

# Graphing Function
def update_graph(frame):
    """ Updates the real-time graph from CSV data """
    plt.clf()
    df = read_csv_data()
    
    if df is not None and not df.empty:
        df["Timestamp"] = pd.to_datetime(df["Timestamp"])
        df["Flow Rate (L/min)"] = df["Flow Rate (L/min)"].astype(float)
        
        plt.plot(df["Timestamp"], df["Flow Rate (L/min)"], label="Flow Rate (L/min)", color="b")
        plt.axhline(y=10, color='r', linestyle='--', label="Alert Threshold (10 L/min)")
        
        plt.xlabel("Time")
        plt.ylabel("Flow Rate (L/min)")
        plt.title("Water Flow Monitoring")
        plt.xticks(rotation=45)
        plt.legend()
        plt.grid()

# GPIO Setup
GPIO.setmode(GPIO.BCM)
GPIO.setup(FLOW_SENSOR_PIN, GPIO.IN, pull_up_down=GPIO.PUD_UP)
GPIO.add_event_detect(FLOW_SENSOR_PIN, GPIO.FALLING, callback=pulse_callback)

try:
    print("🔹 Water Flow Monitoring Started...")
    
    fig = plt.figure()
    ani = animation.FuncAnimation(fig, update_graph, interval=5000)  # Update every 5 seconds
    plt.show(block=False)

    while True:
        pulse_count = 0  # Reset count
        time.sleep(1)  # Wait 1 second
        flow_rate = (pulse_count / FLOW_CALIBRATION) * 60  # Convert pulses to L/min
        
        # Track max flow rate
        global MAX_FLOW_RATE
        if flow_rate > MAX_FLOW_RATE:
            MAX_FLOW_RATE = flow_rate
        
        # Print to console
        print(f"{datetime.now()} 💧 Flow Rate: {flow_rate:.2f} L/min (Max: {MAX_FLOW_RATE:.2f} L/min)")

        # Check for high flow rate and send alerts
        alert_sent = "No"
        if flow_rate > 10:  # Adjust threshold as needed
            alert_sent = "Yes"
            message = f"🚨 High Water Flow Alert! 🚰 {flow_rate:.2f} L/min"
            send_telegram_alert(message)
            send_email_alert("🚨 Water Flow Alert!", message)

        # Log data
        log_data(datetime.now().strftime("%Y-%m-%d %H:%M:%S"), flow_rate, alert_sent)

except KeyboardInterrupt:
    print("\n🔻 Stopping Monitoring...")
    GPIO.cleanup()

🔹 How It Works

1. Monitors water flow and logs it to flow_log.csv
2. Plots real-time water flow data 📈
3. Sends alerts via Telegram and Email if water flow exceeds 10 L/min
4. Automatically updates the graph every 5 seconds

🔹 Running the Script

Run this command on your Raspberry Pi:

python3 flow_monitor.py

Your Raspberry Pi will now log all water flow data, track alerts, and notify you via Telegram & Email.

🔹 Example Email Alert

Subject: 🚨 Water Flow Alert!
Body:

🚨 High Water Flow Alert! 🚰 12.5 L/min

Now, you’ve got a fully automated monitoring system! Want weekly reports emailed with summary stats? 📊📧

Water Flow Monitor with Reports

I’ll add weekly email reports that summarize water usage and include a graph. The report will:

✅ Show total water usage for the week
✅ Include highest & average flow rates
✅ Attach a graph of water flow trends

🔹 Install Required Packages

Before running the script, install the required dependencies:

pip3 install pandas matplotlib smtplib

🔹 Updated Script with Weekly Email Reports

import os
import csv
import pandas as pd
import matplotlib.pyplot as plt
import smtplib
from datetime import datetime, timedelta
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
from email.mime.base import MIMEBase
from email import encoders

# Email Credentials
SMTP_SERVER = "smtp.gmail.com"
SMTP_PORT = 587
EMAIL_ADDRESS = "your_email@gmail.com"  # Replace with your email
EMAIL_PASSWORD = "your_app_password"  # Use App Password

# CSV Log File
LOG_FILE = "flow_log.csv"
REPORT_IMAGE = "weekly_report.png"

def generate_weekly_report():
    """ Reads CSV data, calculates stats, and generates a weekly graph. """
    if not os.path.exists(LOG_FILE):
        print("❌ No log data found!")
        return None

    # Load data
    df = pd.read_csv(LOG_FILE)
    df["Timestamp"] = pd.to_datetime(df["Timestamp"])
    
    # Filter for the last 7 days
    last_week = datetime.now() - timedelta(days=7)
    df = df[df["Timestamp"] >= last_week]

    if df.empty:
        print("❌ No data available for the past week!")
        return None

    # Calculate statistics
    total_usage = df["Flow Rate (L/min)"].sum()
    avg_flow = df["Flow Rate (L/min)"].mean()
    max_flow = df["Flow Rate (L/min)"].max()

    # Generate graph
    plt.figure(figsize=(10, 5))
    plt.plot(df["Timestamp"], df["Flow Rate (L/min)"], label="Flow Rate", color="b")
    plt.axhline(y=10, color='r', linestyle='--', label="Alert Threshold (10 L/min)")
    
    plt.xlabel("Date")
    plt.ylabel("Flow Rate (L/min)")
    plt.title("Weekly Water Flow Report")
    plt.xticks(rotation=45)
    plt.legend()
    plt.grid()
    plt.savefig(REPORT_IMAGE)
    plt.close()

    return total_usage, avg_flow, max_flow

def send_weekly_email():
    """ Sends a weekly email with stats and an attached graph. """
    stats = generate_weekly_report()
    if not stats:
        return

    total_usage, avg_flow, max_flow = stats

    # Email Content
    subject = "💧 Weekly Water Flow Report"
    body = (
        f"Here is your water usage summary for the past week:\n\n"
        f"📊 Total Water Usage: {total_usage:.2f} L\n"
        f"📉 Average Flow Rate: {avg_flow:.2f} L/min\n"
        f"🚀 Highest Flow Rate: {max_flow:.2f} L/min\n\n"
        f"See the attached graph for trends. 📈"
    )

    msg = MIMEMultipart()
    msg["From"] = EMAIL_ADDRESS
    msg["To"] = EMAIL_ADDRESS  # Send to yourself
    msg["Subject"] = subject
    msg.attach(MIMEText(body, "plain"))

    # Attach the graph
    with open(REPORT_IMAGE, "rb") as attachment:
        part = MIMEBase("application", "octet-stream")
        part.set_payload(attachment.read())
        encoders.encode_base64(part)
        part.add_header("Content-Disposition", f"attachment; filename={REPORT_IMAGE}")
        msg.attach(part)

    # Send Email
    try:
        server = smtplib.SMTP(SMTP_SERVER, SMTP_PORT)
        server.starttls()
        server.login(EMAIL_ADDRESS, EMAIL_PASSWORD)
        server.sendmail(EMAIL_ADDRESS, EMAIL_ADDRESS, msg.as_string())
        server.quit()
        print("📧 Weekly Report Sent!")
    except Exception as e:
        print(f"❌ Email Failed: {e}")

# Schedule to run once a week
if __name__ == "__main__":
    send_weekly_email()

🔹 How It Works

1. Runs once a week (you can automate this with a cron job)
2. Reads the past 7 days of water flow data
3. Calculates total, average, and max flow rate
4. Generates a graph 📈
5. Sends an email with stats + graph attachment

🔹 Automate with Cron Job

To send the report every Monday at 8 AM, edit your crontab:

crontab -e

Add this line:

0 8 * * 1 python3 /home/pi/send_weekly_email.py

🔹 Example Email Report

Subject: 💧 Weekly Water Flow Report

Here is your water usage summary for the past week:

📊 Total Water Usage: 320.5 L  
📉 Average Flow Rate: 4.8 L/min  
🚀 Highest Flow Rate: 12.3 L/min  

See the attached graph for trends. 📈

📎 Attachment: weekly_report.png (Graph of water flow trends)

Now you’ll get automatic weekly reports summarizing your water usage! Let me know if you want custom thresholds or other features. 🚀

How should i provide power to the sensor?

How to Power Water Flow Sensor

The DIGITEN water flow sensor is typically powered by 5V DC, which is compatible with the Raspberry Pi’s 5V pin. Here’s how you can provide power safely:

🔹 Recommended Wiring for Raspberry Pi

🔹 Powering the Sensor Directly from Raspberry Pi

1. Use the Raspberry Pi’s 5V Pin
   • The 5V pin on the Raspberry Pi can provide enough power for the sensor.
   • Connect the Red wire of the sensor to the 5V pin.
2. Connect Ground (GND)
   • The Black wire should go to a GND pin on the Raspberry Pi.
3. Signal to GPIO
   • The Yellow wire carries the pulses from the Hall effect sensor.
   • Connect it to GPIO 17 (or another available GPIO pin).

🔹 Alternative: External 5V Power Supply (If Needed)

If you’re using multiple sensors or don’t want to draw power from the Raspberry Pi, you can:

• Use a separate 5V power supply (USB adapter or battery pack).
• Common ground is required: Connect the GND of the external power supply to the GND of the Raspberry Pi.

Would you like a wiring diagram for better clarity? 🚀

Here’s a simple wiring diagram to help you connect your DIGITEN water flow sensor to a Raspberry Pi properly.

🔹 Wiring Guide

Option 1: Powering the Sensor from Raspberry Pi (Recommended)

• Red wire (Power) → Raspberry Pi 5V (Pin 2 or 4)
• Black wire (Ground) → Raspberry Pi GND (Pin 6, 9, 14, 20, etc.)
• Yellow wire (Signal) → Raspberry Pi GPIO 17 (Pin 11)

+------------------------+
|  Raspberry Pi (GPIO)   |
+------------------------+
| 5V (Pin 2)   ──── Red  │  → Power to sensor
| GND (Pin 6)  ──── Black│  → Ground
| GPIO17 (Pin 11) ── Yellow │  → Signal from sensor
+------------------------+

Option 2: Powering the Sensor with an External 5V Power Supply

• Red wire → External 5V Power Supply (+)
• Black wire → External Power Supply GND & Raspberry Pi GND
• Yellow wire → Raspberry Pi GPIO 17

+------------------------+         +--------------------+
|  Raspberry Pi (GPIO)   |         |  External 5V PSU  |
+------------------------+         +--------------------+
| GND (Pin 6)  ───────────┐        | GND (Black)       |
| GPIO17 (Pin 11) ─── Yellow │      | 5V  (Red)        |
|                         │────────|                  |
+------------------------+        +--------------------+

⚠ Important: Make sure the Raspberry Pi GND and external GND are connected together.

🔹 Wiring Diagram (Visual Representation)

I’ll generate an image of the wiring diagram for better clarity. Give me a moment.

Here is a wiring diagram showing how to connect a DIGITEN water flow sensor to a Raspberry Pi. Let me know if you need any modifications or further explanations! 🚀

How do i start this program when the rpi boots?

Start Program at Boot

You can set up your Raspberry Pi to run the script at boot using one of these methods:

Method 1: Use systemd (Recommended)

Systemd is the most reliable way to start a script at boot and restart it if it crashes.

1️⃣ Create a systemd Service File

Run the following command to create a new service file:

sudo nano /etc/systemd/system/flow_monitor.service

Paste this into the file (update the paths if needed):

[Unit]
Description=Water Flow Monitor
After=network.target

[Service]
ExecStart=/usr/bin/python3 /home/pi/send_weekly_email.py
WorkingDirectory=/home/pi
Restart=always
User=pi

[Install]
WantedBy=multi-user.target

Save the file (CTRL + X, then Y, then Enter).

2️⃣ Enable the Service

Run these commands to start the service at boot:

sudo systemctl daemon-reload
sudo systemctl enable flow_monitor.service
sudo systemctl start flow_monitor.service

Check if it’s running:

sudo systemctl status flow_monitor.service

To restart manually:

sudo systemctl restart flow_monitor.service

To stop it:

sudo systemctl stop flow_monitor.service

Method 2: Use Cron (Simple but Less Reliable)

1. Open the crontab editor: crontab -e
2. Add this line at the bottom to run the script at boot: @reboot python3 /home/pi/send_weekly_email.py &
3. Save and exit.

Which Method to Use?

• ✅ Use systemd if you want automatic restarts and better reliability.
• ✅ Use cron if you need a quick and simple setup.

Let me know if you need help with any step! 🚀

The Digiten sensor wasn’t available so I got a Gredia GR-402B sensor. Will there be any changes?

Changes for Gredia Sensor Instead of Digiten Sensor

Yes, the program should work almost the same with a GREDIA water flow sensor, as it also uses a Hall-effect sensor to measure water flow. However, you may need to adjust the calibration factor (pulse-per-liter value) depending on the specific model of your GREDIA sensor.

🔹 What to Check?

1. Pinout: Ensure you connect the GREDIA sensor’s VCC, GND, and Signal (Yellow/Green) wire correctly to your Raspberry Pi.
2. Operating Voltage: Most GREDIA sensors work with 5V, while some also support 3.3V.
3. Pulse-per-Liter Factor: Different flow sensors have different pulse-per-liter ratios.
   • DIGITEN sensors often use 450 pulses per liter.
   • GREDIA sensors could have 450, 500, or 550 pulses per liter, depending on the model.

To find the correct pulse-per-liter for your sensor, check its datasheet or do a manual calibration.

🔹 Adjusting the Code for a GREDIA Sensor

Find this line in your script:

pulse_per_liter = 450  # Adjust based on sensor model

✅ Change this value based on your GREDIA sensor’s specifications.

🔹 How to Manually Calibrate?

1. Run the script and measure how much water you actually use.
2. Compare it to what the program records.
3. Adjust the pulse_per_liter value until the recorded water usage matches real-world usage.

🔹 Will Alerts & Email Reports Still Work?

Yes! The rest of the program (data logging, alerts, and email reports) will work the same. The only thing that needs adjusting is the pulse-per-liter value.

Would you like help finding the correct value for your specific GREDIA sensor? 🚀

the flask install had the error: externally-managed-environment

The externally-managed-environment error occurs because you’re trying to install Flask (or another package) in a system-managed Python environment, like the one on Raspberry Pi OS (Debian-based Linux). This happens because newer versions of pip prevent modifying system-wide Python packages.

🔹 How to Fix It

You have a few options to install Flask properly:

✅ Option 1: Use a Virtual Environment (Recommended)

This is the best method because it keeps your Python packages separate and avoids system conflicts.

1️⃣ Create a Virtual Environment

Run these commands:

cd /home/pi  # Go to your working directory
python3 -m venv venv  # Create a virtual environment named 'venv'

2️⃣ Activate the Virtual Environment

source venv/bin/activate

You should see (venv) appear in your terminal.

3️⃣ Install Flask

pip install flask

4️⃣ Run Your Flask App

Now, when running your Flask script, make sure the virtual environment is activated:

python your_flask_script.py

💡 To exit the virtual environment, type:

deactivate

✅ Option 2: Use pip install --break-system-packages

If you really want to install Flask system-wide (not recommended), use:

pip install flask --break-system-packages

⚠️ Warning: This could break system dependencies.

✅ Option 3: Use --user Flag (Less Recommended)

pip install --user flask

This installs Flask in the user’s home directory, avoiding system conflicts.

🔹 Which Method to Use?

• ✅ Use a Virtual Environment if you’re working on a project.
• ✅ Use --break-system-packages only if you fully control your Raspberry Pi and don’t mind breaking things.
• ✅ Use --user for a quick fix, but it’s not as clean as a virtual environment.

Would you like me to update your Python script to run inside a virtual environment automatically? 🚀

I’ll update your Python script to automatically activate a virtual environment and install dependencies if needed. Here’s how to do it:

🔹 Updated Python Script

Replace your existing script with this version:

import os
import sys
import subprocess

# Define the virtual environment path
VENV_PATH = os.path.expanduser("~/venv")

# Function to check if Flask is installed
def check_flask():
    try:
        import flask
    except ImportError:
        return False
    return True

# Function to set up the virtual environment
def setup_virtualenv():
    if not os.path.exists(VENV_PATH):
        print("Creating virtual environment...")
        subprocess.run([sys.executable, "-m", "venv", VENV_PATH], check=True)

    # Install Flask if not present
    activate_script = os.path.join(VENV_PATH, "bin", "activate")
    pip_path = os.path.join(VENV_PATH, "bin", "pip")

    if not check_flask():
        print("Installing Flask in virtual environment...")
        subprocess.run([pip_path, "install", "flask"], check=True)

# Setup virtual environment
setup_virtualenv()

# Activate the virtual environment
activate_script = os.path.join(VENV_PATH, "bin", "activate_this.py")
exec(open(activate_script).read(), dict(__file__=activate_script))

# Now you can safely import Flask and run your app
from flask import Flask

app = Flask(__name__)

@app.route("/")
def home():
    return "Flask is running in a virtual environment!"

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=5000)

🔹 How This Works

1. Automatically creates a virtual environment in ~/venv if it doesn’t exist.
2. Installs Flask inside the virtual environment if it’s not already installed.
3. Activates the virtual environment before running the Flask app.
4. Starts a simple Flask web server on http://0.0.0.0:5000.

🔹 How to Run It

Save the script as app.py and run:

python3 app.py

Flask will now run inside the virtual environment automatically!

Would you like help setting this up as a systemd service so it starts at boot? 🚀

Would you like help setting this up as a systemd service so it starts at boot? 🚀

Set Program as Systemd Service

Set Up Flask as a Systemd Service on Raspberry Pi

This will ensure your Flask app starts automatically at boot and keeps running in the background.

🔹 Step 1: Create a systemd Service File

Run this command to open a new service file:

sudo nano /etc/systemd/system/flask_app.service

Paste the following inside (update the User, WorkingDirectory, and ExecStart paths if needed):

[Unit]
Description=Flask Web Application
After=network.target

[Service]
User=pi
WorkingDirectory=/home/pi
ExecStart=/home/pi/venv/bin/python3 /home/pi/app.py
Restart=always

[Install]
WantedBy=multi-user.target

✅ Save the file: Press CTRL + X, then Y, then Enter.

🔹 Step 2: Enable and Start the Service

Run these commands:

sudo systemctl daemon-reload
sudo systemctl enable flask_app.service
sudo systemctl start flask_app.service

🔹 Step 3: Check If Flask is Running

To check the status:

sudo systemctl status flask_app.service

You should see output indicating that Flask is active (running).

If there’s an error, check the logs:

journalctl -u flask_app.service --no-pager --lines=50

🔹 Step 4: Restarting or Stopping Flask

To restart manually:

sudo systemctl restart flask_app.service

To stop it:

sudo systemctl stop flask_app.service

🔹 Step 5: Test Flask in a Web Browser

Open a browser and go to:

http://<Your-Raspberry-Pi-IP>:5000

If running locally, use:

http://localhost:5000

🚀 Your Flask App Now Starts at Boot!

Would you like help setting up nginx as a reverse proxy for better performance and HTTPS support?

Set Up Reverse Proxy

Set Up Nginx as a Reverse Proxy for Flask on Raspberry Pi

This will allow your Flask app to be accessible using port 80 (HTTP) instead of port 5000 and make it more efficient. It also allows for SSL encryption if you add a certificate later.

🔹 Step 1: Install Nginx

Run:

sudo apt update
sudo apt install nginx -y

🔹 Step 2: Create an Nginx Configuration File

1. Open a new configuration file: sudo nano /etc/nginx/sites-available/flask_app
2. Paste this configuration (adjust server_name if needed): server { listen 80; server_name _; # Change this to your domain if using one location / { proxy_pass http://127.0.0.1:5000; proxy_set_header Host $host; proxy_set_header X-Real-IP $remote_addr; proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; proxy_set_header X-Forwarded-Proto $scheme; } error_page 404 /404.html; location = /404.html { internal; } }
3. Save and exit: Press CTRL + X, then Y, then Enter.

🔹 Step 3: Enable the Configuration

1. Create a symbolic link to enable the configuration: sudo ln -s /etc/nginx/sites-available/flask_app /etc/nginx/sites-enabled/
2. Remove the default configuration (optional but recommended): sudo rm /etc/nginx/sites-enabled/default
3. Test Nginx for errors: sudo nginx -t ✅ If you see syntax is OK, you’re good to go!
4. Restart Nginx: sudo systemctl restart nginx

🔹 Step 4: Test the Setup

• Open your browser and visit: http://<Your-Raspberry-Pi-IP>/ or if you set up a domain: http://yourdomain.com/
• Your Flask app should now be accessible without :5000.

🔹 Step 5: Optional – Enable HTTPS with Let’s Encrypt (Recommended)

If you have a domain, you can secure your site with Let’s Encrypt SSL:

sudo apt install certbot python3-certbot-nginx -y
sudo certbot --nginx -d yourdomain.com

This automatically configures Nginx to use HTTPS.

🚀 Flask is Now Running Behind Nginx!

Would you like me to help with auto-renewing SSL certificates or adding authentication to your Flask app?

Set Up SSL Certificate and Authentication

🔹 Auto-Renew SSL Certificates & Add Authentication to Your Flask App

Now that your Flask app runs behind Nginx with HTTPS, let’s ensure:
✅ SSL certificates auto-renew
✅ Your Flask app is protected with authentication (optional)

1️⃣ Auto-Renew SSL Certificates

Let’s Encrypt SSL certificates expire every 90 days, but we can automate renewal.

🔹 Step 1: Test Manual Renewal

Run this command to test renewing your SSL certificate:

sudo certbot renew --dry-run

✅ If no errors appear, the renewal process is working.

🔹 Step 2: Set Up Automatic Renewal

Certbot already installs a systemd timer for renewal. But to be sure:

1. Edit the crontab to check renewal daily: sudo crontab -e
2. Add this line at the bottom to renew certificates automatically and reload Nginx: 0 2 * * * certbot renew --quiet && systemctl reload nginx 🔹 This runs every day at 2 AM (0 2 * * *).
3. Save and exit (CTRL + X, then Y, then Enter).

✅ Now, SSL auto-renews when needed!

2️⃣ Add Authentication to Your Flask App

If you only want authorized users to access the Flask app, you can require a login using Basic Authentication in Nginx.

🔹 Step 1: Install apache2-utils

This provides the htpasswd command to create a username/password file:

sudo apt install apache2-utils -y

🔹 Step 2: Create a User for Authentication

Replace yourusername with your preferred login name:

sudo htpasswd -c /etc/nginx/.htpasswd yourusername

🔹 Enter a secure password when prompted.

✅ This stores the credentials securely in /etc/nginx/.htpasswd.

🔹 Step 3: Update the Nginx Config

Edit your Flask app’s Nginx configuration:

sudo nano /etc/nginx/sites-available/flask_app

Find this section:

location / {
    proxy_pass http://127.0.0.1:5000;
    proxy_set_header Host $host;
    proxy_set_header X-Real-IP $remote_addr;
    proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
    proxy_set_header X-Forwarded-Proto $scheme;
}

🔹 Modify it to require authentication:

location / {
    auth_basic "Restricted Access";
    auth_basic_user_file /etc/nginx/.htpasswd;

    proxy_pass http://127.0.0.1:5000;
    proxy_set_header Host $host;
    proxy_set_header X-Real-IP $remote_addr;
    proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
    proxy_set_header X-Forwarded-Proto $scheme;
}

✅ This forces users to enter a username and password before accessing the Flask app.

🔹 Step 4: Restart Nginx

Apply the changes:

sudo systemctl restart nginx

3️⃣ Testing Your Secure Setup

🛠 Test Auto-Renewal

Run:

sudo certbot renew --dry-run

If no errors appear, it’s set to auto-renew!

🔑 Test Authentication

1. Open your browser and go to: http://yourdomain.com/ or http://<Your-Raspberry-Pi-IP>/
2. You should see a username/password prompt.

✅ Enter your htpasswd credentials to access your Flask app.

🎯 Your Flask App is Now:

✔ Running behind Nginx
✔ Secured with SSL
✔ Auto-renewing certificates
✔ Password-protected (if enabled)

Would you like me to help with OAuth (Google Login) or adding a custom login page in Flask? 🚀

Set Up Login Page in Flask

🔹 Add OAuth (Google Login) to Your Flask App

Instead of using basic authentication, let’s allow users to log in with Google! This method is more secure, user-friendly, and scalable.

1️⃣ Set Up Google OAuth Credentials

Google provides OAuth authentication via Google Cloud Console.

🔹 Step 1: Create a Google OAuth App

1. Go to Google Cloud Console and sign in.
2. Click “Select a Project” → “New Project”.
3. Give it a name (e.g., FlaskOAuthApp) and click “Create”.
4. In the left sidebar, go to APIs & Services > Credentials.
5. Click “Create Credentials” → “OAuth 2.0 Client ID”.
6. Select “Web Application” as the application type.
7. Under “Authorized Redirect URIs”, add: http://yourdomain.com/login/callback or, if testing locally: http://localhost:5000/login/callback
8. Click “Create”, then copy your:
   • Client ID
   • Client Secret

2️⃣ Install Required Python Packages

Your Flask app needs the Flask-OAuthlib package:

pip install flask flask-oauthlib

3️⃣ Modify Your Flask App to Use Google Login

Update your app.py to include Google OAuth authentication:

from flask import Flask, redirect, url_for, session
from flask_oauthlib.client import OAuth
import os

app = Flask(__name__)
app.secret_key = os.urandom(24)  # Change this to a fixed secret key in production

oauth = OAuth(app)

google = oauth.remote_app(
    'google',
    consumer_key='YOUR_GOOGLE_CLIENT_ID',
    consumer_secret='YOUR_GOOGLE_CLIENT_SECRET',
    request_token_params={
        'scope': 'email profile'
    },
    base_url='https://www.googleapis.com/oauth2/v1/',
    request_token_url=None,
    access_token_method='POST',
    access_token_url='https://accounts.google.com/o/oauth2/token',
    authorize_url='https://accounts.google.com/o/oauth2/auth'
)

@app.route('/')
def home():
    return 'Welcome! <a href="/login">Login with Google</a>'

@app.route('/login')
def login():
    return google.authorize(callback=url_for('authorized', _external=True))

@app.route('/login/callback')
def authorized():
    response = google.authorized_response()
    if response is None or response.get('access_token') is None:
        return 'Access Denied'
    
    session['google_token'] = (response['access_token'], '')
    user_info = google.get('userinfo')  # Get user details
    return f"Hello, {user_info.data['name']}! You are logged in."

@google.tokengetter
def get_google_oauth_token():
    return session.get('google_token')

@app.route('/logout')
def logout():
    session.pop('google_token', None)
    return redirect(url_for('home'))

if __name__ == '__main__':
    app.run(debug=True)

🔹 Replace:

• YOUR_GOOGLE_CLIENT_ID
• YOUR_GOOGLE_CLIENT_SECRET
  with your credentials.

4️⃣ Restart Flask & Test Login

1. Run the Flask app: python3 app.py
2. Open your browser and visit: http://localhost:5000/
3. Click “Login with Google”.
4. Sign in with Google.
5. You should see a welcome message with your name!

5️⃣ Secure OAuth in Production

• Use a fixed secret key in app.secret_key.
• Deploy behind Nginx & use HTTPS.
• Restrict Google OAuth to trusted domains (in Google Cloud settings).

🚀 Google Login is Now Enabled!

Would you like to: ✅ Store user data in a database
✅ Restrict access to logged-in users
✅ Add logout redirection?

Restrict Access to Logged-in Users

🔹 Store User Data & Restrict Access in Your Flask App

Now that Google OAuth is working, let’s:
✅ Store user data in a database
✅ Restrict access to logged-in users
✅ Add logout redirection

1️⃣ Install & Set Up SQLite (or Use MySQL/PostgreSQL)

We’ll use SQLite for simplicity, but this can be replaced with MySQL/PostgreSQL.

🔹 Install SQLite & Flask-SQLAlchemy

Run:

pip install flask-sqlalchemy

🔹 Modify app.py to Use a Database

Update your Flask app to store user details:

from flask import Flask, redirect, url_for, session, render_template
from flask_sqlalchemy import SQLAlchemy
from flask_oauthlib.client import OAuth
import os

app = Flask(__name__)
app.secret_key = os.urandom(24)  # Change this for production

# Configure SQLite Database
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///users.db'
app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
db = SQLAlchemy(app)

# Define User Model
class User(db.Model):
    id = db.Column(db.Integer, primary_key=True)
    google_id = db.Column(db.String(100), unique=True, nullable=False)
    name = db.Column(db.String(100), nullable=False)
    email = db.Column(db.String(100), unique=True, nullable=False)

# OAuth Setup
oauth = OAuth(app)
google = oauth.remote_app(
    'google',
    consumer_key='YOUR_GOOGLE_CLIENT_ID',
    consumer_secret='YOUR_GOOGLE_CLIENT_SECRET',
    request_token_params={'scope': 'email profile'},
    base_url='https://www.googleapis.com/oauth2/v1/',
    request_token_url=None,
    access_token_method='POST',
    access_token_url='https://accounts.google.com/o/oauth2/token',
    authorize_url='https://accounts.google.com/o/oauth2/auth'
)

@app.route('/')
def home():
    if 'google_token' in session:
        user = User.query.filter_by(google_id=session['google_id']).first()
        return f"Hello, {user.name}! <a href='/logout'>Logout</a>"
    return '<a href="/login">Login with Google</a>'

@app.route('/login')
def login():
    return google.authorize(callback=url_for('authorized', _external=True))

@app.route('/login/callback')
def authorized():
    response = google.authorized_response()
    if response is None or response.get('access_token') is None:
        return 'Access Denied'

    session['google_token'] = (response['access_token'], '')
    user_info = google.get('userinfo').data

    # Store User Info in Database
    user = User.query.filter_by(google_id=user_info['id']).first()
    if not user:
        user = User(google_id=user_info['id'], name=user_info['name'], email=user_info['email'])
        db.session.add(user)
        db.session.commit()

    # Store user details in session
    session['google_id'] = user_info['id']
    session['name'] = user_info['name']

    return redirect(url_for('home'))

@google.tokengetter
def get_google_oauth_token():
    return session.get('google_token')

@app.route('/logout')
def logout():
    session.pop('google_token', None)
    session.pop('google_id', None)
    session.pop('name', None)
    return redirect(url_for('home'))

# Initialize Database
with app.app_context():
    db.create_all()

if __name__ == '__main__':
    app.run(debug=True)

2️⃣ Run Your Flask App & Test

1. Initialize the database (Run this once): python3 app.py This creates users.db to store logged-in users.
2. Start Flask again: python3 app.py
3. Open your browser and visit: http://localhost:5000/
4. Login with Google.
5. Your name should appear, and your details will be stored in the database.
6. Logout and try logging in again—your data is now persistent!

3️⃣ Restrict Access to Logged-In Users

If you want to protect certain routes (e.g., a dashboard), modify your Flask app:

from functools import wraps

def login_required(f):
    @wraps(f)
    def decorated_function(*args, **kwargs):
        if 'google_id' not in session:
            return redirect(url_for('login'))
        return f(*args, **kwargs)
    return decorated_function

@app.route('/dashboard')
@login_required
def dashboard():
    return f"Welcome to your dashboard, {session['name']}!"

Now, if users visit /dashboard, they must log in first.

🚀 Your Flask App Now Has:

✔ Google OAuth login
✔ User database storage
✔ Restricted access for logged-in users
✔ Logout functionality

Would you like help deploying this on your Raspberry Pi server with Gunicorn & Nginx?

Deployment with Gunicorn & Nginx

🚀 Deploy Your Flask App on a Raspberry Pi with Gunicorn & Nginx

Now that your Flask app is working, let’s deploy it properly on your Raspberry Pi. This setup ensures that your app:
✅ Runs in the background without needing to manually start it
✅ Is accessible over the network
✅ Uses Nginx as a reverse proxy for better performance

1️⃣ Install Required Packages

Run the following on your Raspberry Pi:

sudo apt update && sudo apt upgrade -y
sudo apt install python3-pip python3-venv nginx -y

2️⃣ Set Up a Virtual Environment

Navigate to your Flask project directory and create a virtual environment:

cd /home/pi/my_flask_app
python3 -m venv venv
source venv/bin/activate

🔹 Install Dependencies

Reinstall Flask and other required packages inside the virtual environment:

pip install flask flask-oauthlib flask-sqlalchemy gunicorn

3️⃣ Test Gunicorn

Gunicorn is a production-ready WSGI server that will serve your Flask app.

Run:

gunicorn -w 3 -b 0.0.0.0:8000 app:app

This:

• Runs 3 worker processes (-w 3) for handling requests
• Binds to port 8000 (-b 0.0.0.0:8000)

Test by opening:

http://<YOUR_RPI_IP>:8000

If it’s working, press Ctrl+C to stop it.

4️⃣ Create a Systemd Service

To automatically start the app at boot, create a systemd service:

sudo nano /etc/systemd/system/flask_app.service

Paste this inside:

[Unit]
Description=Gunicorn instance to serve Flask app
After=network.target

[Service]
User=pi
Group=pi
WorkingDirectory=/home/pi/my_flask_app
Environment="PATH=/home/pi/my_flask_app/venv/bin"
ExecStart=/home/pi/my_flask_app/venv/bin/gunicorn -w 3 -b 0.0.0.0:8000 app:app

[Install]
WantedBy=multi-user.target

Save & exit (Ctrl+X, Y, Enter).

🔹 Enable & Start the Service

sudo systemctl daemon-reload
sudo systemctl enable flask_app
sudo systemctl start flask_app

Check if it’s running:

sudo systemctl status flask_app

If you see “active (running)”, the app is working!

5️⃣ Set Up Nginx as a Reverse Proxy

Now, we’ll configure Nginx to serve the Flask app.

🔹 Create an Nginx Config File

sudo nano /etc/nginx/sites-available/flask_app

Paste this inside:

server {
    listen 80;
    server_name _;

    location / {
        proxy_pass http://127.0.0.1:8000;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
    }
}

Save & exit (Ctrl+X, Y, Enter).

🔹 Enable the Nginx Config

sudo ln -s /etc/nginx/sites-available/flask_app /etc/nginx/sites-enabled
sudo systemctl restart nginx

6️⃣ Test Your Deployment

Now, open your browser and visit:

http://<YOUR_RPI_IP>

If everything is set up correctly, you should see your Flask app!

7️⃣ Optional: Use a Custom Domain

If you have a domain, update server_name in the Nginx config:

server_name yourdomain.com;

Then restart Nginx:

sudo systemctl restart nginx

🚀 Your Flask App Is Now Deployed!

✔ Runs automatically on boot
✔ Uses Gunicorn for performance
✔ Uses Nginx as a reverse proxy

Would you like to secure it with HTTPS using Let’s Encrypt? 🔐

🔐 Secure Your Flask App with HTTPS (Let’s Encrypt & Certbot)

Now that your Flask app is running with Nginx, let’s secure it with free SSL certificates from Let’s Encrypt using Certbot.

1️⃣ Install Certbot

Run the following on your Raspberry Pi:

sudo apt update
sudo apt install certbot python3-certbot-nginx -y

2️⃣ Obtain an SSL Certificate

Replace yourdomain.com with your actual domain and run:

sudo certbot --nginx -d yourdomain.com -d www.yourdomain.com

🔹 What Happens Here?

• Certbot will automatically configure Nginx for HTTPS.
• It will redirect HTTP traffic to HTTPS.
• It will ask for your email for renewal reminders.

If the setup is successful, you’ll see a message like:

Congratulations! Your certificate and chain have been saved at: /etc/letsencrypt/live/yourdomain.com/fullchain.pem

3️⃣ Test Your HTTPS Setup

Open a browser and visit:

https://yourdomain.com

Your site should now be secure with a padlock icon! 🔒

4️⃣ Auto-Renew SSL Certificates

Let’s Encrypt certificates expire every 90 days, but Certbot can automatically renew them.

Run:

sudo certbot renew --dry-run

If everything looks good, Certbot will renew your certificates automatically in the background.

🚀 Your Flask App Is Now Fully Secured with HTTPS!

✔ Runs on boot
✔ Uses Gunicorn & Nginx for performance
✔ Automatically renews SSL certificates

Would you like to set up a custom firewall (UFW) to further secure your Raspberry Pi? 🔥

🔥 Secure Your Raspberry Pi with a Firewall (UFW)

Now that your Flask app is running securely with HTTPS, let’s set up a firewall using UFW (Uncomplicated Firewall) to protect your Raspberry Pi from unwanted access.

1️⃣ Install UFW

First, make sure UFW is installed:

sudo apt update
sudo apt install ufw -y

2️⃣ Allow Essential Services

We’ll allow only the necessary ports for your Flask app and system management:

• SSH (22) – If you connect remotely via SSH
• HTTP (80) – Needed for the initial Certbot challenge
• HTTPS (443) – Secured access to your Flask app

Run these commands:

sudo ufw allow OpenSSH  # Allow SSH access
sudo ufw allow 80/tcp    # Allow HTTP (needed for Let's Encrypt renewal)
sudo ufw allow 443/tcp   # Allow HTTPS for secure access

3️⃣ Enable the Firewall

Once you’ve added the rules, enable UFW:

sudo ufw enable

⚠ Important: If you’re connected via SSH, make sure you’ve allowed SSH (sudo ufw allow OpenSSH) before enabling the firewall. Otherwise, you might get locked out!

To check the firewall status:

sudo ufw status

You should see something like:

Status: active

To                         Action      From
--                         ------      ----
OpenSSH                    ALLOW       Anywhere
80/tcp                     ALLOW       Anywhere
443/tcp                    ALLOW       Anywhere

4️⃣ (Optional) Allow Local Network Access

If you want to allow access from devices on your home network (but not the internet), find your local subnet (e.g., 192.168.1.0/24) and allow it:

sudo ufw allow from 192.168.1.0/24

🚀 Your Raspberry Pi is Now Secure!

✔ Firewall is enabled
✔ Only necessary ports are open
✔ Protects against unauthorized access

Would you like to set up logging & alerts for potential threats? 📊🔔

📊 Set Up Logging & Alerts for Security on Raspberry Pi

Now that your firewall is active, let’s enable logging and set up email alerts for potential threats. This will help you track any unauthorized access attempts.

1️⃣ Enable UFW Logging

By default, UFW logs blocked connections, but we’ll make sure logging is enabled.

Run:

sudo ufw logging on

To check logs, use:

sudo journalctl -u ufw --no-pager | tail -20

Or, check the log file directly:

sudo cat /var/log/ufw.log | tail -20

2️⃣ Install & Configure Fail2Ban

Fail2Ban blocks repeated failed login attempts (e.g., SSH brute force attacks).

🔹 Install Fail2Ban

sudo apt install fail2ban -y

🔹 Configure Fail2Ban

Create a custom config file:

sudo nano /etc/fail2ban/jail.local

Paste this inside:

[DEFAULT]
bantime = 1h
findtime = 10m
maxretry = 5
destemail = your-email@example.com
sender = fail2ban@yourdomain.com
mta = sendmail
action = %(action_mwl)s

[sshd]

enabled = true

• bantime = 1h → Bans IPs for 1 hour after multiple failed attempts
• maxretry = 5 → Blocks an IP after 5 failed attempts
• destemail → Change to your email for notifications

Save & exit (Ctrl+X, Y, Enter).

🔹 Restart Fail2Ban

sudo systemctl restart fail2ban
sudo systemctl enable fail2ban

🔹 Check If It’s Running

sudo fail2ban-client status sshd

If working, you’ll see:

Status for the jail: sshd
|- Filter
|  |- Currently failed: 0
|  |- Total failed: 10
|  `- File list: /var/log/auth.log
`- Actions
   |- Currently banned: 0
   |- Total banned: 3

3️⃣ Get Email Alerts for SSH Login Attempts

If you want email alerts when someone logs into your Raspberry Pi via SSH:

🔹 Install Mail Client

sudo apt install mailutils -y

🔹 Edit SSH Login Notification Script

sudo nano /etc/profile.d/ssh-notify.sh

Paste this inside:

#!/bin/bash
echo "SSH Login Alert: User $USER from $(who | awk '{print $5}')" | mail -s "SSH Login Alert" your-email@example.com

Replace your-email@example.com with your actual email.

🔹 Make It Executable

sudo chmod +x /etc/profile.d/ssh-notify.sh

Now, whenever someone logs into your Raspberry Pi via SSH, you’ll get an email notification. 📩

🚀 Your Raspberry Pi Is Now Extra Secure!

✔ UFW logging is enabled
✔ Fail2Ban prevents brute-force attacks
✔ SSH login alerts notify you of logins

Would you like to monitor system performance & logs remotely using a tool like Grafana? 📊🔍

📊 Monitor Raspberry Pi Performance & Logs Remotely with Grafana

Now that we’ve secured your Raspberry Pi, let’s set up Grafana with Prometheus to monitor:
✅ CPU & memory usage
✅ Disk & network activity
✅ Uptime & system logs
✅ Firewall & security events

1️⃣ Install Prometheus for System Metrics

Prometheus is a powerful monitoring system that collects system performance data.

🔹 Install Prometheus & Node Exporter

Run:

sudo apt update
sudo apt install prometheus prometheus-node-exporter -y

🔹 Enable and Start Services

sudo systemctl enable prometheus prometheus-node-exporter
sudo systemctl start prometheus prometheus-node-exporter

To check if they are running:

sudo systemctl status prometheus
sudo systemctl status prometheus-node-exporter

Your Raspberry Pi is now collecting system metrics! 🚀

2️⃣ Install Grafana for Data Visualization

Grafana provides a beautiful web-based dashboard to visualize Prometheus data.

🔹 Install Grafana

sudo apt install -y grafana

🔹 Enable and Start Grafana

sudo systemctl enable grafana-server
sudo systemctl start grafana-server

To check if it’s running:

sudo systemctl status grafana-server

🔹 Access Grafana Dashboard

Open a browser and go to:

http://your_raspberry_pi_ip:3000

• Default username: admin
• Default password: admin (You’ll be asked to change it after first login)

3️⃣ Connect Prometheus to Grafana

1. In Grafana, go to Configuration → Data Sources
2. Click “Add data source”
3. Select Prometheus
4. Set the URL to: http://localhost:9090
5. Click Save & Test (It should confirm the connection)

4️⃣ Import a Raspberry Pi Dashboard

1. Go to Dashboards → Import
2. Enter 1860 (Official Raspberry Pi dashboard ID)
3. Click Load, then Import

🎉 Now you have a live monitoring dashboard for your Raspberry Pi!

5️⃣ (Optional) Monitor Security Logs in Grafana

If you want to monitor firewall (UFW) & Fail2Ban logs in Grafana:

1. Install Loki (Grafana’s log system) sudo apt install -y loki
2. Configure UFW logs in Loki
3. Add Loki as a data source in Grafana
4. Visualize firewall blocks & SSH login attempts

🚀 Your Raspberry Pi Now Has:

✔ Live performance monitoring
✔ Security event tracking
✔ Beautiful, remote dashboards

Would you like to set up alerts in Grafana for CPU spikes, low disk space, or security threats? 🔔

🔔 Set Up Alerts in Grafana for CPU, Disk, and Security Events

Now that your Raspberry Pi is being monitored in Grafana, let’s set up real-time alerts for:
✅ High CPU usage 🚀
✅ Low disk space 💾
✅ Network overload 📶
✅ Security threats (SSH attempts, firewall blocks) 🔐

1️⃣ Enable Grafana Alerting

By default, Grafana alerting is disabled on Raspberry Pi. Enable it with:

sudo nano /etc/grafana/grafana.ini

Find the section [alerting] and set:

enabled = true

Save & exit (Ctrl+X, Y, Enter), then restart Grafana:

sudo systemctl restart grafana-server

2️⃣ Configure Notification Channels

Grafana supports Email, Telegram, Slack, Discord, Webhooks, etc.

🔹 Set Up Email Alerts

1. In Grafana, go to Alerting → Notification Channels
2. Click “Add Channel”
3. Set:
   • Name: System Alerts
   • Type: Email
   • Addresses: your-email@example.com
4. Click Save

If you prefer Telegram, Slack, or Discord, let me know! 🤖

3️⃣ Create an Alert for High CPU Usage

1. Open your Raspberry Pi System Dashboard
2. Find the CPU Usage graph
3. Click the panel title → Edit
4. Go to the Alert tab
5. Click “Create Alert”, then set:
   • Condition: If CPU > 85% for 5 minutes
   • Evaluate every: 1m
   • Notification Channel: System Alerts
6. Click Save

4️⃣ Add Alerts for Disk Space & Security Logs

🔹 Low Disk Space Alert

1. Open the Disk Usage panel
2. Set an alert: If disk space < 2GB for 5 minutes
3. Save

🔹 Security Alerts (SSH & Firewall Logs)

If you set up Loki for logs, you can create alerts for:
✅ Multiple failed SSH logins
✅ IP bans by Fail2Ban
✅ Blocked IPs by UFW

5️⃣ Test Your Alerts!

1. Go to Alerting → Alert Rules
2. Click “Test Rule” on your alerts
3. If set up correctly, you should receive an email alert 📩

🚀 Your Raspberry Pi Now Has:

✔ Live monitoring in Grafana
✔ Real-time alerts for system health & security
✔ Notifications via Email (or Telegram/Slack)

Would you like to automate actions (e.g., restart a service if CPU is too high)? 🔄

🔄 Automate Actions Based on Alerts on Your Raspberry Pi

Now that Grafana can send alerts, let’s automate responses!
✅ Restart a service if CPU is too high 🚀
✅ Block an IP after repeated SSH login failures 🔐
✅ Send a custom notification (e.g., text message)

1️⃣ Auto-Restart a Service if CPU is Too High

We’ll create a script that restarts a service when Grafana detects high CPU usage.

🔹 Step 1: Create the Auto-Restart Script

Run:

sudo nano /usr/local/bin/restart_service.sh

Paste this inside:

#!/bin/bash

CPU_USAGE=$(top -bn1 | grep "Cpu(s)" | awk '{print $2 + $4}')
THRESHOLD=85.0

if (( $(echo "$CPU_USAGE > $THRESHOLD" | bc -l) )); then
    echo "$(date) - High CPU detected: $CPU_USAGE%" >> /var/log/cpu_alert.log
    sudo systemctl restart your-service-name
    echo "Restarted your-service-name" >> /var/log/cpu_alert.log
fi

Replace your-service-name with the actual service (e.g., nginx, grafana-server).

Save & exit (Ctrl+X, Y, Enter).

🔹 Step 2: Make the Script Executable

sudo chmod +x /usr/local/bin/restart_service.sh

🔹 Step 3: Set Up a Cron Job

Run:

crontab -e

Add this line at the bottom:

*/5 * * * * /usr/local/bin/restart_service.sh

✅ This checks CPU usage every 5 minutes and restarts the service if needed.

2️⃣ Auto-Ban an IP After Multiple SSH Failures

Let’s automate IP banning when Grafana detects multiple failed SSH attempts.

🔹 Step 1: Create a Fail2Ban Rule for Auto-Banning

Run:

sudo nano /etc/fail2ban/jail.local

Add:

[sshd]
enabled = true
filter = sshd
logpath = /var/log/auth.log
maxretry = 5
bantime = 1h

Save & restart Fail2Ban:

sudo systemctl restart fail2ban

🔹 Step 2: Check Banned IPs

sudo fail2ban-client status sshd

🚀 Now, after 5 failed login attempts, the attacker’s IP will be automatically blocked.

3️⃣ Send a Custom Notification (Telegram or SMS)

If you want to receive a Telegram or SMS alert, we can do that too!

Would you like Telegram notifications, Twilio SMS, or both? 📲

📲 Automate Telegram & SMS Notifications for Alerts

Now, let’s set up both Telegram and SMS (via Twilio) notifications when an alert is triggered.

1️⃣ Send a Telegram Message When an Alert is Triggered

🔹 Step 1: Create a Telegram Bot

1. Open Telegram and search for BotFather
2. Send the command: /newbot
3. Follow the instructions and get your bot token (It looks like 123456:ABC-DEF1234ghIkl-zyx57W2v1u123ew11).
4. Get your chat ID:
   • Go to https://api.telegram.org/bot<your-bot-token>/getUpdates
   • Send a message to your bot and refresh the link above.
   • Find your chat ID (a number like -123456789).

🔹 Step 2: Create a Telegram Alert Script

Run:

sudo nano /usr/local/bin/telegram_alert.sh

Paste this:

#!/bin/bash

BOT_TOKEN="your-telegram-bot-token"
CHAT_ID="your-chat-id"
MESSAGE="🚨 Alert! High CPU usage detected on your Raspberry Pi!"

curl -s -X POST "https://api.telegram.org/bot$BOT_TOKEN/sendMessage" -d chat_id=$CHAT_ID -d text="$MESSAGE"

Replace your-telegram-bot-token and your-chat-id with your actual values.

Save & exit (Ctrl+X, Y, Enter).

Make it executable:

sudo chmod +x /usr/local/bin/telegram_alert.sh

2️⃣ Send an SMS Alert Using Twilio

🔹 Step 1: Create a Twilio Account

1. Sign up at Twilio
2. Get your Twilio Account SID, Auth Token, and Twilio Phone Number

🔹 Step 2: Install Twilio Python Library

Run:

pip install twilio

🔹 Step 3: Create an SMS Alert Script

Run:

sudo nano /usr/local/bin/sms_alert.py

Paste this:

from twilio.rest import Client

# Twilio credentials
ACCOUNT_SID = "your-account-sid"
AUTH_TOKEN = "your-auth-token"
TWILIO_NUMBER = "+your-twilio-number"
TO_NUMBER = "+your-phone-number"

# Alert message
message_body = "🚨 Alert! High CPU usage detected on your Raspberry Pi!"

# Send SMS
client = Client(ACCOUNT_SID, AUTH_TOKEN)
message = client.messages.create(
    body=message_body,
    from_=TWILIO_NUMBER,
    to=TO_NUMBER
)

print(f"SMS sent: {message.sid}")

Replace your-account-sid, your-auth-token, your-twilio-number, and your-phone-number.

Save & exit (Ctrl+X, Y, Enter).

Make it executable:

sudo chmod +x /usr/local/bin/sms_alert.py

3️⃣ Link Alerts to Grafana

Now, configure Grafana to trigger these scripts when alerts occur.

🔹 Step 1: Modify the Auto-Restart Script

Edit:

sudo nano /usr/local/bin/restart_service.sh

Update it:

#!/bin/bash

CPU_USAGE=$(top -bn1 | grep "Cpu(s)" | awk '{print $2 + $4}')
THRESHOLD=85.0

if (( $(echo "$CPU_USAGE > $THRESHOLD" | bc -l) )); then
    echo "$(date) - High CPU detected: $CPU_USAGE%" >> /var/log/cpu_alert.log
    sudo systemctl restart your-service-name
    echo "Restarted your-service-name" >> /var/log/cpu_alert.log

    # Send Telegram Alert
    /usr/local/bin/telegram_alert.sh

    # Send SMS Alert
    python3 /usr/local/bin/sms_alert.py
fi

Save & exit (Ctrl+X, Y, Enter).

4️⃣ Test Your Alerts

1. Manually run the script: /usr/local/bin/restart_service.sh
2. You should receive both a Telegram message and an SMS alert! 📲

🚀 Your Raspberry Pi Now Has:

✔ Real-time alerts in Telegram & SMS
✔ Automated service restarts
✔ IP banning for security threats

Would you like to log all alerts into a file for tracking? 📜

📜 Log All Alerts for Tracking

Now, let’s make sure every alert (CPU spikes, service restarts, IP bans, etc.) is logged into a file so you can review past alerts.

1️⃣ Create a Log File for Alerts

We’ll use /var/log/alert_log.log to store alerts.

Run:

sudo touch /var/log/alert_log.log
sudo chmod 666 /var/log/alert_log.log

✅ This creates the log file and allows any script to write to it.

2️⃣ Modify the Restart Script to Log Alerts

Run:

sudo nano /usr/local/bin/restart_service.sh

Update it:

#!/bin/bash

CPU_USAGE=$(top -bn1 | grep "Cpu(s)" | awk '{print $2 + $4}')
THRESHOLD=85.0
LOG_FILE="/var/log/alert_log.log"

if (( $(echo "$CPU_USAGE > $THRESHOLD" | bc -l) )); then
    TIMESTAMP=$(date "+%Y-%m-%d %H:%M:%S")
    echo "$TIMESTAMP - 🚨 High CPU detected: $CPU_USAGE%" | tee -a $LOG_FILE
    sudo systemctl restart your-service-name
    echo "$TIMESTAMP - 🔄 Restarted your-service-name" | tee -a $LOG_FILE

    # Send Telegram Alert
    /usr/local/bin/telegram_alert.sh
    echo "$TIMESTAMP - 📩 Telegram alert sent" | tee -a $LOG_FILE

    # Send SMS Alert
    python3 /usr/local/bin/sms_alert.py
    echo "$TIMESTAMP - 📲 SMS alert sent" | tee -a $LOG_FILE
fi

✅ This logs every action taken (CPU spike, service restart, alerts sent).

Save & exit (Ctrl+X, Y, Enter).

3️⃣ Modify IP Ban Script to Log Banned IPs

If you want to track banned IPs, update Fail2Ban logging.

Run:

sudo nano /etc/fail2ban/action.d/log_ban.conf

Paste this:

[Definition]
actionstart =
actionstop =
actioncheck =
actionban = echo "$(date "+%Y-%m-%d %H:%M:%S") - 🚫 IP Banned: <ip>" >> /var/log/alert_log.log
actionunban = echo "$(date "+%Y-%m-%d %H:%M:%S") - ✅ IP Unbanned: <ip>" >> /var/log/alert_log.log

Save & restart Fail2Ban:

sudo systemctl restart fail2ban

✅ Now, banned/unbanned IPs will be logged.

4️⃣ View & Monitor Logs

To see logs in real time, run:

tail -f /var/log/alert_log.log

This will show updates as they happen.

🚀 Now every alert is logged! Would you like logs to be emailed to you daily? 📧

📧 Daily Email Reports for Alerts

Now, let’s set up automatic daily email reports so you get a summary of all alerts, service restarts, and banned IPs every day.

1️⃣ Install Mail Utilities on Raspberry Pi

Run:

sudo apt update && sudo apt install postfix mailutils -y

During setup, select “Internet Site” and set your system mail name (usually your Pi’s hostname).

2️⃣ Create a Script to Send the Email

Run:

sudo nano /usr/local/bin/send_alert_email.sh

Paste this:

#!/bin/bash

LOG_FILE="/var/log/alert_log.log"
EMAIL="your-email@example.com"
SUBJECT="🚨 Daily Alert Report from Raspberry Pi"

# Get log summary
LOG_SUMMARY=$(tail -n 50 $LOG_FILE)

# Send email
echo -e "Hello,\n\nHere is your daily alert report:\n\n$LOG_SUMMARY\n\n- Raspberry Pi Alert System" | mail -s "$SUBJECT" $EMAIL

Replace your-email@example.com with your actual email.

Save & exit (Ctrl+X, Y, Enter).

Make it executable:

sudo chmod +x /usr/local/bin/send_alert_email.sh

3️⃣ Automate Daily Email Using Cron

Run:

crontab -e

At the bottom, add:

0 8 * * * /usr/local/bin/send_alert_email.sh

✅ This will send the email every day at 8 AM. Change the time as needed.

4️⃣ Test the Email Setup

Run manually:

/usr/local/bin/send_alert_email.sh

Check your email! 📩

🚀 Now, you’ll receive a daily report of all alerts, restarts, and security events!

Would you like the report to include system stats like CPU, RAM, and disk usage? 📊

📊 Add System Stats to Daily Email Reports

Now, we’ll enhance your daily email to include CPU, RAM, and disk usage, so you get a complete system health report.

1️⃣ Update the Email Script to Include System Stats

Run:

sudo nano /usr/local/bin/send_alert_email.sh

Replace the previous script with this:

#!/bin/bash

LOG_FILE="/var/log/alert_log.log"
EMAIL="your-email@example.com"
SUBJECT="🚨 Daily Alert & System Report from Raspberry Pi"

# Get system stats
HOSTNAME=$(hostname)
TIMESTAMP=$(date "+%Y-%m-%d %H:%M:%S")
UPTIME=$(uptime -p)
CPU_USAGE=$(top -bn1 | grep "Cpu(s)" | awk '{print $2 + $4}')
MEMORY_USAGE=$(free -m | awk 'NR==2{printf "%.2f%% (Used: %sMB / Total: %sMB)", $3*100/$2, $3, $2}')
DISK_USAGE=$(df -h / | awk 'NR==2{print $5 " (Used: " $3 " / Total: " $2 ")"}')
LOG_SUMMARY=$(tail -n 50 $LOG_FILE)

# Email body
EMAIL_BODY="Hello,\n\nHere is your daily alert & system report:\n
📅 Date: $TIMESTAMP  
🖥️ Hostname: $HOSTNAME  
⏳ Uptime: $UPTIME  

🔹 **System Stats:**  
🔥 CPU Usage: $CPU_USAGE%  
💾 Memory Usage: $MEMORY_USAGE  
📀 Disk Usage: $DISK_USAGE  

🔹 **Recent Alerts & Events:**  
$LOG_SUMMARY  

- Raspberry Pi Alert System"

# Send email
echo -e "$EMAIL_BODY" | mail -s "$SUBJECT" $EMAIL

✅ This adds CPU, RAM, and disk usage to the email.

Save & exit (Ctrl+X, Y, Enter).

2️⃣ Test the New Report

Run:

/usr/local/bin/send_alert_email.sh

Check your email! 📩 It should now include system stats along with alerts.

3️⃣ Ensure the Daily Email is Scheduled

If you haven’t already set up a cron job, run:

crontab -e

Add this line at the bottom:

0 8 * * * /usr/local/bin/send_alert_email.sh

✅ This automates the report every day at 8 AM.

🚀 Now, you’ll get a daily system health and alert report!
Would you like me to include network status (IP, speed test, etc.) in the report too? 🌐📡

🌐 Add Network Status to Daily Email Reports

Now, we’ll enhance your daily email to include:
✅ Current IP Address (Public & Local)
✅ Wi-Fi or Ethernet Connection Status
✅ Internet Speed Test (Download & Upload speeds)

1️⃣ Install Speedtest Tool

Run:

sudo apt update && sudo apt install speedtest-cli -y

✅ This installs a command-line tool to check your internet speed.

2️⃣ Update the Email Script to Include Network Info

Run:

sudo nano /usr/local/bin/send_alert_email.sh

Replace the script with this:

#!/bin/bash

LOG_FILE="/var/log/alert_log.log"
EMAIL="your-email@example.com"
SUBJECT="🚨 Daily Alert & System Report from Raspberry Pi"

# Get system stats
HOSTNAME=$(hostname)
TIMESTAMP=$(date "+%Y-%m-%d %H:%M:%S")
UPTIME=$(uptime -p)
CPU_USAGE=$(top -bn1 | grep "Cpu(s)" | awk '{print $2 + $4}')
MEMORY_USAGE=$(free -m | awk 'NR==2{printf "%.2f%% (Used: %sMB / Total: %sMB)", $3*100/$2, $3, $2}')
DISK_USAGE=$(df -h / | awk 'NR==2{print $5 " (Used: " $3 " / Total: " $2 ")"}')

# Get network info
LOCAL_IP=$(hostname -I | awk '{print $1}')
PUBLIC_IP=$(curl -s https://api64.ipify.org)
CONNECTION_TYPE=$(ip route get 8.8.8.8 | grep -o "dev .*" | awk '{print $2}')
SPEEDTEST_RESULT=$(speedtest-cli --simple)

# Get latest log summary
LOG_SUMMARY=$(tail -n 50 $LOG_FILE)

# Email body
EMAIL_BODY="Hello,\n\nHere is your daily alert & system report:\n
📅 Date: $TIMESTAMP  
🖥️ Hostname: $HOSTNAME  
⏳ Uptime: $UPTIME  

🔹 **System Stats:**  
🔥 CPU Usage: $CPU_USAGE%  
💾 Memory Usage: $MEMORY_USAGE  
📀 Disk Usage: $DISK_USAGE  

🔹 **Network Status:**  
🌐 Local IP: $LOCAL_IP  
🌍 Public IP: $PUBLIC_IP  
🔗 Connection Type: $CONNECTION_TYPE  

📶 **Internet Speed Test:**  
$SPEEDTEST_RESULT  

🔹 **Recent Alerts & Events:**  
$LOG_SUMMARY  

- Raspberry Pi Alert System"

# Send email
echo -e "$EMAIL_BODY" | mail -s "$SUBJECT" $EMAIL

Replace your-email@example.com with your actual email.

Save & exit (Ctrl+X, Y, Enter).

3️⃣ Test the New Report

Run:

/usr/local/bin/send_alert_email.sh

✅ Check your email—it should now include network details & speed test results.

4️⃣ Ensure the Daily Email is Scheduled

If you haven’t already set up a cron job, run:

crontab -e

Add this line at the bottom:

0 8 * * * /usr/local/bin/send_alert_email.sh

✅ This automates the report every day at 8 AM.

🚀 Now, you’ll get a full system health + network report daily!
Would you like to include disk space alerts if storage gets low? 📀🔴

📀 Add Disk Space Alerts to Daily Email Reports

Now, we’ll add disk space alerts to your daily report. If your storage drops below 10% free space, the email will include a ⚠️ warning.

1️⃣ Update the Email Script to Check Disk Space

Run:

sudo nano /usr/local/bin/send_alert_email.sh

Replace the script with this:

#!/bin/bash

LOG_FILE="/var/log/alert_log.log"
EMAIL="your-email@example.com"
SUBJECT="🚨 Daily Alert & System Report from Raspberry Pi"

# Get system stats
HOSTNAME=$(hostname)
TIMESTAMP=$(date "+%Y-%m-%d %H:%M:%S")
UPTIME=$(uptime -p)
CPU_USAGE=$(top -bn1 | grep "Cpu(s)" | awk '{print $2 + $4}')
MEMORY_USAGE=$(free -m | awk 'NR==2{printf "%.2f%% (Used: %sMB / Total: %sMB)", $3*100/$2, $3, $2}')
DISK_USAGE=$(df -h / | awk 'NR==2{print $5 " (Used: " $3 " / Total: " $2 ")"}')

# Check disk space percentage
DISK_PERCENT=$(df -h / | awk 'NR==2 {print $5}' | sed 's/%//')
DISK_ALERT=""
if [ "$DISK_PERCENT" -gt 90 ]; then
    DISK_ALERT="⚠️ Warning: Low Disk Space! Only $((100 - DISK_PERCENT))% free."
fi

# Get network info
LOCAL_IP=$(hostname -I | awk '{print $1}')
PUBLIC_IP=$(curl -s https://api64.ipify.org)
CONNECTION_TYPE=$(ip route get 8.8.8.8 | grep -o "dev .*" | awk '{print $2}')
SPEEDTEST_RESULT=$(speedtest-cli --simple)

# Get latest log summary
LOG_SUMMARY=$(tail -n 50 $LOG_FILE)

# Email body
EMAIL_BODY="Hello,\n\nHere is your daily alert & system report:\n
📅 Date: $TIMESTAMP  
🖥️ Hostname: $HOSTNAME  
⏳ Uptime: $UPTIME  

🔹 **System Stats:**  
🔥 CPU Usage: $CPU_USAGE%  
💾 Memory Usage: $MEMORY_USAGE  
📀 Disk Usage: $DISK_USAGE  
$DISK_ALERT  

🔹 **Network Status:**  
🌐 Local IP: $LOCAL_IP  
🌍 Public IP: $PUBLIC_IP  
🔗 Connection Type: $CONNECTION_TYPE  

📶 **Internet Speed Test:**  
$SPEEDTEST_RESULT  

🔹 **Recent Alerts & Events:**  
$LOG_SUMMARY  

- Raspberry Pi Alert System"

# Send email
echo -e "$EMAIL_BODY" | mail -s "$SUBJECT" $EMAIL

Replace your-email@example.com with your actual email.

Save & exit (Ctrl+X, Y, Enter).

2️⃣ Test the New Report

Run:

/usr/local/bin/send_alert_email.sh

✅ Check your email—you should now see a disk space warning if your storage is low.

3️⃣ Ensure the Daily Email is Scheduled

If you haven’t already set up a cron job, run:

crontab -e

Add this line at the bottom:

0 8 * * * /usr/local/bin/send_alert_email.sh

✅ This automates the report every day at 8 AM.

🚀 Now, your daily email includes disk space alerts!
Would you like to add temperature monitoring for your Raspberry Pi’s CPU? 🌡️🔥

🌡️ Add Raspberry Pi CPU Temperature Monitoring

Now, we’ll add CPU temperature monitoring to your daily report. If the temperature goes above 70°C, the email will include a ⚠️ high-temperature warning.

1️⃣ Update the Email Script to Include CPU Temperature

Run:

sudo nano /usr/local/bin/send_alert_email.sh

Replace the script with this:

#!/bin/bash

LOG_FILE="/var/log/alert_log.log"
EMAIL="your-email@example.com"
SUBJECT="🚨 Daily Alert & System Report from Raspberry Pi"

# Get system stats
HOSTNAME=$(hostname)
TIMESTAMP=$(date "+%Y-%m-%d %H:%M:%S")
UPTIME=$(uptime -p)
CPU_USAGE=$(top -bn1 | grep "Cpu(s)" | awk '{print $2 + $4}')
MEMORY_USAGE=$(free -m | awk 'NR==2{printf "%.2f%% (Used: %sMB / Total: %sMB)", $3*100/$2, $3, $2}')
DISK_USAGE=$(df -h / | awk 'NR==2{print $5 " (Used: " $3 " / Total: " $2 ")"}')

# Check disk space percentage
DISK_PERCENT=$(df -h / | awk 'NR==2 {print $5}' | sed 's/%//')
DISK_ALERT=""
if [ "$DISK_PERCENT" -gt 90 ]; then
    DISK_ALERT="⚠️ Warning: Low Disk Space! Only $((100 - DISK_PERCENT))% free."
fi

# Get CPU temperature
CPU_TEMP_RAW=$(cat /sys/class/thermal/thermal_zone0/temp)
CPU_TEMP_C=$((CPU_TEMP_RAW / 1000))
CPU_TEMP_ALERT=""
if [ "$CPU_TEMP_C" -gt 70 ]; then
    CPU_TEMP_ALERT="🔥 Warning: High CPU Temperature! $CPU_TEMP_C°C"
fi

# Get network info
LOCAL_IP=$(hostname -I | awk '{print $1}')
PUBLIC_IP=$(curl -s https://api64.ipify.org)
CONNECTION_TYPE=$(ip route get 8.8.8.8 | grep -o "dev .*" | awk '{print $2}')
SPEEDTEST_RESULT=$(speedtest-cli --simple)

# Get latest log summary
LOG_SUMMARY=$(tail -n 50 $LOG_FILE)

# Email body
EMAIL_BODY="Hello,\n\nHere is your daily alert & system report:\n
📅 Date: $TIMESTAMP  
🖥️ Hostname: $HOSTNAME  
⏳ Uptime: $UPTIME  

🔹 **System Stats:**  
🔥 CPU Usage: $CPU_USAGE%  
🌡️ CPU Temperature: $CPU_TEMP_C°C  
$CPU_TEMP_ALERT  
💾 Memory Usage: $MEMORY_USAGE  
📀 Disk Usage: $DISK_USAGE  
$DISK_ALERT  

🔹 **Network Status:**  
🌐 Local IP: $LOCAL_IP  
🌍 Public IP: $PUBLIC_IP  
🔗 Connection Type: $CONNECTION_TYPE  

📶 **Internet Speed Test:**  
$SPEEDTEST_RESULT  

🔹 **Recent Alerts & Events:**  
$LOG_SUMMARY  

- Raspberry Pi Alert System"

# Send email
echo -e "$EMAIL_BODY" | mail -s "$SUBJECT" $EMAIL

Replace your-email@example.com with your actual email.

Save & exit (Ctrl+X, Y, Enter).

2️⃣ Test the New Report

Run:

/usr/local/bin/send_alert_email.sh

✅ Check your email—you should now see a CPU temperature reading, and if it’s high, a warning message.

3️⃣ Ensure the Daily Email is Scheduled

If you haven’t already set up a cron job, run:

crontab -e

Add this line at the bottom:

0 8 * * * /usr/local/bin/send_alert_email.sh

✅ This automates the report every day at 8 AM.

🚀 Now, your daily report includes CPU temperature alerts!
Would you like to add automatic shutdown or throttling if the CPU temperature gets dangerously high? 🔥🛑

🛑 Automatically Throttle or Shutdown Raspberry Pi If CPU Overheats

If your Raspberry Pi’s CPU temperature goes above 80°C, it will automatically throttle performance.
If it reaches 85°C, the system will shut down to prevent damage.

1️⃣ Create a Temperature Monitoring Script

Run:

sudo nano /usr/local/bin/temp_monitor.sh

Paste this code:

#!/bin/bash

LOG_FILE="/var/log/temperature_log.log"
MAX_TEMP=85  # Shutdown threshold
THROTTLE_TEMP=80  # Throttle warning threshold

while true; do
    # Get CPU temperature
    CPU_TEMP_RAW=$(cat /sys/class/thermal/thermal_zone0/temp)
    CPU_TEMP=$((CPU_TEMP_RAW / 1000))

    # Log temperature
    TIMESTAMP=$(date "+%Y-%m-%d %H:%M:%S")
    echo "$TIMESTAMP - CPU Temp: $CPU_TEMP°C" >> $LOG_FILE

    # Check for throttling
    if [ "$CPU_TEMP" -ge "$THROTTLE_TEMP" ] && [ "$CPU_TEMP" -lt "$MAX_TEMP" ]; then
        echo "$TIMESTAMP - ⚠️ Warning: CPU is overheating ($CPU_TEMP°C). Consider reducing load!" | mail -s "⚠️ Raspberry Pi High Temperature Alert" your-email@example.com
    fi

    # Check for shutdown condition
    if [ "$CPU_TEMP" -ge "$MAX_TEMP" ]; then
        echo "$TIMESTAMP - 🚨 Critical: CPU temperature reached $CPU_TEMP°C! Shutting down..." | mail -s "🚨 Raspberry Pi Overheat Shutdown" your-email@example.com
        sudo shutdown -h now
    fi

    sleep 60  # Check every minute
done

Replace your-email@example.com with your actual email.

Save & exit (Ctrl+X, Y, Enter).

2️⃣ Make the Script Executable

Run:

sudo chmod +x /usr/local/bin/temp_monitor.sh

3️⃣ Run the Script at Startup

Run:

sudo nano /etc/systemd/system/temp_monitor.service

Paste this:

[Unit]
Description=Raspberry Pi Temperature Monitor
After=network.target

[Service]
ExecStart=/usr/local/bin/temp_monitor.sh
Restart=always
User=root

[Install]
WantedBy=multi-user.target

Save & exit (Ctrl+X, Y, Enter).

4️⃣ Enable and Start the Service

Run:

sudo systemctl daemon-reload
sudo systemctl enable temp_monitor.service
sudo systemctl start temp_monitor.service

✅ The script will now run automatically at startup and check the temperature every minute.

🚀 Now, Your Raspberry Pi is Protected from Overheating!

If the CPU temperature exceeds 80°C, you’ll get a warning email.
If it reaches 85°C, the Pi will shut down automatically.

Would you like to also log all temperature data for later analysis? 📊

📊 Log All Raspberry Pi CPU Temperature Data for Analysis

Now, we’ll store all temperature data in a CSV file so you can analyze trends over time.

1️⃣ Modify the Temperature Monitoring Script

Run:

sudo nano /usr/local/bin/temp_monitor.sh

Replace the script with this updated version:

#!/bin/bash

LOG_FILE="/var/log/temperature_log.log"
CSV_FILE="/var/log/cpu_temp_data.csv"
MAX_TEMP=85  # Shutdown threshold
THROTTLE_TEMP=80  # Throttle warning threshold

# Create CSV file with headers if it doesn't exist
if [ ! -f "$CSV_FILE" ]; then
    echo "Timestamp,CPU_Temperature_C" > "$CSV_FILE"
fi

while true; do
    # Get CPU temperature
    CPU_TEMP_RAW=$(cat /sys/class/thermal/thermal_zone0/temp)
    CPU_TEMP=$((CPU_TEMP_RAW / 1000))
    
    # Get current timestamp
    TIMESTAMP=$(date "+%Y-%m-%d %H:%M:%S")

    # Log to CSV
    echo "$TIMESTAMP,$CPU_TEMP" >> "$CSV_FILE"

    # Log to text file
    echo "$TIMESTAMP - CPU Temp: $CPU_TEMP°C" >> "$LOG_FILE"

    # Check for overheating
    if [ "$CPU_TEMP" -ge "$THROTTLE_TEMP" ] && [ "$CPU_TEMP" -lt "$MAX_TEMP" ]; then
        echo "$TIMESTAMP - ⚠️ Warning: CPU is overheating ($CPU_TEMP°C). Consider reducing load!" | mail -s "⚠️ Raspberry Pi High Temperature Alert" your-email@example.com
    fi

    # Check for shutdown condition
    if [ "$CPU_TEMP" -ge "$MAX_TEMP" ]; then
        echo "$TIMESTAMP - 🚨 Critical: CPU temperature reached $CPU_TEMP°C! Shutting down..." | mail -s "🚨 Raspberry Pi Overheat Shutdown" your-email@example.com
        sudo shutdown -h now
    fi

    sleep 60  # Check every minute
done

✅ This logs CPU temperature to:

• CSV file (/var/log/cpu_temp_data.csv) – For analysis in Excel or Python
• Log file (/var/log/temperature_log.log) – For quick checking
• Email Alerts if the temperature exceeds 80°C or 85°C

2️⃣ Make the Script Executable

Run:

sudo chmod +x /usr/local/bin/temp_monitor.sh

3️⃣ Restart the Service

Run:

sudo systemctl restart temp_monitor.service

✅ The script will now log temperature every minute and keep a history.

4️⃣ View Your Logged Temperature Data

To see the last 10 entries:

tail -n 10 /var/log/cpu_temp_data.csv

To open the CSV file in Excel, Google Sheets, or Python, copy it to your computer:

scp pi@your-pi-ip:/var/log/cpu_temp_data.csv .

🚀 Your Raspberry Pi Now Logs and Alerts for Overheating!

Would you like a graph to visualize the temperature history? 📈

📈 Graph Raspberry Pi CPU Temperature History

Now, let’s create a Python script to generate a temperature graph from the logged data.

1️⃣ Install Required Libraries

Run:

pip install pandas matplotlib

2️⃣ Create the Python Script

Run:

nano ~/plot_temp.py

Paste this code:

import pandas as pd
import matplotlib.pyplot as plt

# File path of the temperature log
CSV_FILE = "/var/log/cpu_temp_data.csv"

# Read the CSV file
df = pd.read_csv(CSV_FILE, parse_dates=["Timestamp"])

# Plot the data
plt.figure(figsize=(10, 5))
plt.plot(df["Timestamp"], df["CPU_Temperature_C"], label="CPU Temp (°C)", color="red", linewidth=2)
plt.axhline(y=80, color="orange", linestyle="--", label="Throttle Temp (80°C)")
plt.axhline(y=85, color="red", linestyle="--", label="Shutdown Temp (85°C)")

# Formatting
plt.xlabel("Time")
plt.ylabel("CPU Temperature (°C)")
plt.title("Raspberry Pi CPU Temperature Over Time")
plt.legend()
plt.xticks(rotation=45)
plt.grid(True)

# Show the plot
plt.show()

Save & exit (Ctrl+X, Y, Enter).

3️⃣ Run the Graph Script

Run:

python3 ~/plot_temp.py

✅ You’ll see a graph of your CPU temperature over time!

🚀 Your Raspberry Pi Now Logs, Alerts & Graphs Temperature Trends!

Would you like to automate this graph generation and save it as an image for easy access? 🖼️

📊 Automate CPU Temperature Graph Generation & Save as Image

Now, we’ll modify the script to automatically generate and save the graph every hour.

1️⃣ Update the Python Script

Run:

nano ~/plot_temp.py

Replace with:

import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime

# File path of the temperature log
CSV_FILE = "/var/log/cpu_temp_data.csv"
OUTPUT_IMAGE = f"/var/log/cpu_temp_graph_{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png"

# Read the CSV file
df = pd.read_csv(CSV_FILE, parse_dates=["Timestamp"])

# Plot the data
plt.figure(figsize=(10, 5))
plt.plot(df["Timestamp"], df["CPU_Temperature_C"], label="CPU Temp (°C)", color="red", linewidth=2)
plt.axhline(y=80, color="orange", linestyle="--", label="Throttle Temp (80°C)")
plt.axhline(y=85, color="red", linestyle="--", label="Shutdown Temp (85°C)")

# Formatting
plt.xlabel("Time")
plt.ylabel("CPU Temperature (°C)")
plt.title("Raspberry Pi CPU Temperature Over Time")
plt.legend()
plt.xticks(rotation=45)
plt.grid(True)

# Save the graph as an image
plt.savefig(OUTPUT_IMAGE, bbox_inches="tight")
plt.close()

print(f"✅ Graph saved as {OUTPUT_IMAGE}")

Save & exit (Ctrl+X, Y, Enter).

2️⃣ Automate Graph Generation with Cron Job

Run:

crontab -e

At the bottom, add this line:

0 * * * * /usr/bin/python3 /home/pi/plot_temp.py

✅ This will generate and save the graph every hour.

3️⃣ View the Latest Graph

To check the latest saved graph:

ls -lt /var/log/cpu_temp_graph_*.png | head -n 1

To open it on your Raspberry Pi:

xdg-open $(ls -t /var/log/cpu_temp_graph_*.png | head -n 1)

🚀 Now, Your Raspberry Pi Automatically Logs, Alerts & Saves Temperature Graphs!

Would you like to send the graph via email or access it from a web interface? 📧🌍

📧 Send the Temperature Graph via Email & Access via Web Interface

Now, we’ll automate email notifications with the latest graph and serve it via a simple web page.

1️⃣ Install Required Packages

Run:

pip install yagmail flask

• yagmail → For sending emails
• Flask → For hosting the web interface

2️⃣ Set Up Email Alerts

Run:

nano ~/send_graph_email.py

Paste this code:

import yagmail
import glob
import os

# Email Configuration
EMAIL_USER = "your-email@gmail.com"  # Use your Gmail
EMAIL_PASS = "your-app-password"  # Generate an app password in Gmail settings
EMAIL_TO = "recipient-email@example.com"

# Find the latest graph image
latest_graph = max(glob.glob("/var/log/cpu_temp_graph_*.png"), key=os.path.getctime)

# Email setup
yag = yagmail.SMTP(EMAIL_USER, EMAIL_PASS)

# Send email with the latest graph
yag.send(
    to=EMAIL_TO,
    subject="📊 Raspberry Pi CPU Temperature Report",
    contents="Here is the latest CPU temperature graph.",
    attachments=latest_graph,
)

print(f"✅ Email sent with attachment: {latest_graph}")

Save & exit (Ctrl+X, Y, Enter).

3️⃣ Automate Email Sending

Run:

crontab -e

Add this line to send an email every morning at 8 AM:

0 8 * * * /usr/bin/python3 /home/pi/send_graph_email.py

✅ Your Pi will now email the latest temperature graph daily!

4️⃣ Create a Simple Web Interface

Run:

nano ~/web_server.py

Paste this code:

from flask import Flask, send_file
import glob
import os

app = Flask(__name__)

@app.route("/")
def serve_latest_graph():
    # Find the latest graph image
    latest_graph = max(glob.glob("/var/log/cpu_temp_graph_*.png"), key=os.path.getctime)
    return send_file(latest_graph, mimetype="image/png")

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=5000)

Save & exit (Ctrl+X, Y, Enter).

5️⃣ Start the Web Server on Boot

Run:

sudo nano /etc/systemd/system/web_server.service

Paste this:

[Unit]
Description=Raspberry Pi CPU Temp Web Server
After=network.target

[Service]
ExecStart=/usr/bin/python3 /home/pi/web_server.py
Restart=always
User=pi

[Install]
WantedBy=multi-user.target

Save & exit.

Then run:

sudo systemctl daemon-reload
sudo systemctl enable web_server
sudo systemctl start web_server

✅ Your Raspberry Pi is now hosting the latest CPU temperature graph at:
📌 http://your-pi-ip:5000

🚀 Now You Can Receive & View Your Pi’s Temperature Graphs Anytime!

Would you like to add mobile notifications (Telegram or Pushbullet) for real-time alerts? 📲

📲 Get Real-Time Mobile Notifications for High CPU Temperature

Now, let’s set up Telegram or Pushbullet to receive instant alerts when your Raspberry Pi CPU temperature gets too high.

1️⃣ Choose Your Notification Method

You can use Telegram (recommended) or Pushbullet:

• Telegram → Free, secure, and works on all devices.
• Pushbullet → Easier setup but limits free messages.

I’ll walk you through Telegram first—let me know if you prefer Pushbullet instead.

2️⃣ Set Up a Telegram Bot

🔹 Create a Telegram Bot

1. Open Telegram and search for BotFather.
2. Type /newbot and follow the prompts:
   • Choose a name (e.g., “RaspberryPiMonitor”).
   • Choose a username (must end in bot, e.g., “RPiTempBot”).
3. Copy the API token that BotFather gives you.

🔹 Get Your Chat ID

1. Open Telegram and search for @userinfobot.
2. Type /start and note your Chat ID (a number like 123456789).

3️⃣ Install Telegram Library on Raspberry Pi

Run:

pip install python-telegram-bot

4️⃣ Add Telegram Alerts to Your Script

Run:

nano ~/cpu_temp_alert.py

Paste this code:

import os
import requests

# Telegram Bot API Token (from BotFather)
TELEGRAM_BOT_TOKEN = "your_bot_token_here"

# Your Telegram Chat ID
TELEGRAM_CHAT_ID = "your_chat_id_here"

# Read CPU temperature
def get_cpu_temperature():
    temp_output = os.popen("vcgencmd measure_temp").readline()
    return float(temp_output.replace("temp=", "").replace("'C\n", ""))

# Send Telegram Alert
def send_telegram_alert(temp):
    message = f"⚠️ *Raspberry Pi Overheating!*\n\nCPU Temperature: {temp}°C"
    url = f"https://api.telegram.org/bot{TELEGRAM_BOT_TOKEN}/sendMessage"
    data = {"chat_id": TELEGRAM_CHAT_ID, "text": message, "parse_mode": "Markdown"}
    requests.post(url, data=data)

# Check CPU Temp and Alert if Over 80°C
cpu_temp = get_cpu_temperature()
if cpu_temp > 80:
    send_telegram_alert(cpu_temp)

Save & exit (Ctrl+X, Y, Enter).

5️⃣ Automate Alerts with Cron

Run:

crontab -e

Add this line to check the temperature every 5 minutes:

*/5 * * * * /usr/bin/python3 /home/pi/cpu_temp_alert.py

✅ Now, you’ll get a Telegram alert if your Raspberry Pi overheats!

🚀 Your Raspberry Pi Now Logs, Graphs, and Sends Alerts!

Would you also like Pushbullet alerts as an alternative? 📱

📲 Set Up Pushbullet for CPU Temperature Alerts

Pushbullet is another great way to receive real-time notifications when your Raspberry Pi overheats.

1️⃣ Set Up Pushbullet

🔹 Get a Pushbullet API Key

1. Go to Pushbullet.com and sign in.
2. Click on your profile picture → Settings.
3. Go to Access Tokens and click Create Access Token.
4. Copy the token (you’ll need it in the next step).

2️⃣ Install Pushbullet Python Library

Run:

pip install pushbullet.py

3️⃣ Create the Pushbullet Alert Script

Run:

nano ~/cpu_temp_alert_pushbullet.py

Paste this code:

import os
from pushbullet import Pushbullet

# Pushbullet API Token (replace with your actual token)
PUSHBULLET_API_KEY = "your_pushbullet_token_here"

# Read CPU temperature
def get_cpu_temperature():
    temp_output = os.popen("vcgencmd measure_temp").readline()
    return float(temp_output.replace("temp=", "").replace("'C\n", ""))

# Send Pushbullet Alert
def send_pushbullet_alert(temp):
    pb = Pushbullet(PUSHBULLET_API_KEY)
    title = "⚠️ Raspberry Pi Overheating!"
    message = f"CPU Temperature: {temp}°C - Check your system!"
    pb.push_note(title, message)

# Check CPU Temp and Alert if Over 80°C
cpu_temp = get_cpu_temperature()
if cpu_temp > 80:
    send_pushbullet_alert(cpu_temp)

Save & exit (Ctrl+X, Y, Enter).

4️⃣ Automate Alerts with Cron

Run:

crontab -e

Add this line to check the temperature every 5 minutes:

*/5 * * * * /usr/bin/python3 /home/pi/cpu_temp_alert_pushbullet.py

✅ Now, you’ll get Pushbullet notifications when your Raspberry Pi overheats!

🚀 Your Pi Now Supports Telegram & Pushbullet Alerts!

Would you like to customize alert thresholds or send alerts to multiple devices? 📱🔥

How to backup an image of the Raspberry Pi SD (from Gemini)

Creating an image of your Raspberry Pi’s SD card is essential for backups, cloning, or restoring your system. Here’s how you can do it, depending on your operating system:

1. On Linux:

• Using dd (Disk Dump):
  • This is a powerful command-line tool for copying disk images.
  • Identify the SD card:
    • Use lsblk or sudo fdisk -l to list your connected drives. Identify the SD card’s device name (e.g., /dev/sdb, /dev/mmcblk0). Be very careful to select the correct device, as dd can overwrite data.
  • Create the image:
    • sudo dd bs=4M if=/dev/sdX of=rpi-image.img status=progress
      • Replace /dev/sdX with your SD card’s device name.
      • rpi-image.img is the name of the image file.
      • bs=4M sets the block size to 4MB for faster copying.
      • status=progress shows the copy progress.
  • Compress the image (optional):
    • gzip rpi-image.img
      • This will create a compressed image file (rpi-image.img.gz), which is smaller and easier to store.

2. On macOS:

• Using dd (similar to Linux):
  • Identify the SD card:
    • Use diskutil list in the Terminal to find the SD card’s device identifier (e.g., /dev/disk2).
  • Unmount the SD card:
    • diskutil unmountDisk /dev/disk2 (replace /dev/disk2 with your SD card’s identifier).
  • Create the image:
    • sudo dd bs=4m if=/dev/rdisk2 of=rpi-image.img
      • Note the use of /dev/rdisk2 (raw disk) for faster copying.
  • Compress the image (optional):
    • gzip rpi-image.img

3. On Windows:

• Using Raspberry Pi Imager:
  • The Raspberry Pi Imager is a user-friendly tool that can create images and restore them.¹
  • Download and install the Raspberry Pi Imager from the official Raspberry Pi website.²
  • Insert your SD card into your computer.
  • Open the Raspberry Pi Imager.
  • Click “Choose Storage” and select your SD card.
  • Click the gear icon in the lower right corner, and select “Save image to file”.
  • Choose a location and filename for the image.
  • Click “Write” to create the image.
• Using Win32 Disk Imager:
  • Win32 Disk Imager is another popular tool for creating and restoring disk images.³
  • Download and install Win32 Disk Imager.
  • Insert your SD card into your computer.
  • Open Win32 Disk Imager.
  • Select your SD card’s drive letter.
  • Choose a location and filename for the image.
  • Click “Read” to create the image.

Important Notes:

• SD Card Size: The image file will be the same size as your SD card, regardless of the amount of data it contains.
• Backup Data: Before creating an image, ensure you have backed up any important data on the SD card.
• Verification: After creating the image, you can verify it by comparing its checksum with the original SD card.
• Restoring the Image: You can use the same tools (Raspberry Pi Imager, Win32 Disk Imager, or dd) to restore the image to another SD card.
• Power Down: Always properly power down your raspberry pi before removing the SD card.
• Unmount: Always unmount the SD card before removing it from a computer.

I am chronicling a thread of my life to improve my self-knowledge and improve my memory. This has a lot to do with computers.

Public school came easy for me and I failed to develop good study habits. I was good in math and science and enrolled in the School of Engineering at The Ohio State University. I scraped through Electrical Engineering, Mechanical Engineering, Industral Engineering before finishing in Computer Science.

In the late 1970’s, I finally found my passion – personal computing. I learned to progam in Basic on a DEC PDP11 at Tulsa Junior College. I learned how to use the Time Sharing Option (TSO) at Cities Service Oil Company. I got an Atari 800 and I could play games and write Basic programs at home. I was a single father and treasured early Saturday and Sunday mornings when it was just me and my Atari.

I used TSO far more than the stated restrictions and soon became the local expert. With no special privileges, I triggered a bug that deleted the VSAM master catalog that took down the IBM mainframe computer. This affected hundreds of active Cities Service workers and got me a visit to Systems Engineering to explain what I was doing when the system went down. It also got me a new job as Timesharing Coordinator. That started my journey through many versions of operating system software.

To be continued…

Cool Things I’m Doing with Alexa

1. Voice update of shopping list
2. “Alexa, good morning” to start my lights-on routine early
3. “Alexa, good night” to start my lights-off routine early
4. “Alexa, goodbye” to turn off the lights and start robovac
5. “Alexa, hello” to turn on the lights and send robovac home
6. Announce when washer or dryer is done
7. Turn on humidifier when humidity is low
8. Turn on diffuser as part of “good morning”
9. Turn off diffuser after two hours
10. Announce when the garage door opens
11. Set lights to low, medium or maximum brightness
12. Turn yard light on at sunset and off at sunrise
13. Announce “hello” at sunset and sunrise
14. My garage door closes after two hours

Yes, you can absolutely use a 256GB and two 512GB devices for Ceph storage in your Proxmox cluster — but there are a few things to consider for performance and redundancy! Let’s break this down.

✅ Ceph doesn’t require identical drives:

• Ceph treats each device as an OSD (Object Storage Daemon), and the cluster will balance data across them.
• Mixed drive sizes are fine, but Ceph’s performance and usable capacity will be influenced by the smallest drive and overall distribution.

⚠️ Capacity imbalance:

• Since Ceph tries to evenly distribute objects across all OSDs, the smaller 256GB drive will fill up faster than the two 512GB drives.
• Once the 256GB device reaches capacity, Ceph won’t be able to place new data on it, even if the larger drives have space — this could cause cluster warnings or degraded performance.

⭐️ Workarounds:

1. Weighting OSDs:
   • In Ceph, you can set the weight of each OSD to reflect its capacity — this helps the cluster balance data more intelligently.
   • Example: Assign a weight of 1.0 to the 512GB drives and 0.5 to the 256GB drive.
2. Separate pools:
   • If performance or balance becomes a concern, you could create different storage pools — one for the 256GB drive and another for the 512GB drives — though this adds complexity.

✨ Redundancy and performance:

• Ceph replication or erasure coding will still work, but keep in mind that losing the smaller drive would affect the cluster more quickly since it’s likely to fill up first.
• If you’re running 3-replica redundancy, the cluster will ensure data is distributed across all three nodes, even with mismatched drive sizes.

👉 Bottom line: It’ll work fine, but adjusting the OSD weights in Proxmox (via the Ceph GUI or CLI) would help avoid issues. I can walk you through setting that up if you’d like! 🚀

Let’s get your Ceph OSD weights adjusted! I’ll break this down step-by-step.

✅ Step 1: Check current OSDs and weights

1. Open Proxmox Web GUI.
2. Navigate to Datacenter → Ceph → OSD.
3. Here, you should see your three OSDs (one for each drive). Take note of their IDs.

👉 Alternatively, from the Proxmox shell:

ceph osd tree

This shows the OSDs, their weights, and which nodes they’re on.

✅ Step 2: Set the OSD weights

The goal:

• Set the two 512GB devices to a weight of 1.0.
• Set the 256GB device to a weight of 0.5 (reflecting its smaller capacity).

👉 From the Proxmox shell on any node:

ceph osd crush reweight osd.<ID_of_256GB_drive> 0.5
ceph osd crush reweight osd.<ID_of_512GB_drive_1> 1.0
ceph osd crush reweight osd.<ID_of_512GB_drive_2> 1.0

✅ To confirm the changes:

ceph osd tree

You should now see the updated weights.

✅ Step 3: Monitor Ceph health

1. Check the Ceph health status:

ceph health

2. Make sure there are no warnings like “near full OSD” or “backfilling issues.” Ceph should automatically rebalance the data across your drives based on the new weights.

✅ Step 4: Automate monitoring (optional)

Since the 256GB drive could still fill up faster, you might want to set up Proxmox alerts to notify you if the drive is getting close to full.

👉 Check OSD utilization:

ceph osd df

⭐ Why this works:

• Ceph uses the CRUSH algorithm to distribute data — weights help guide how much data each OSD should hold relative to the others.
• By lowering the weight on the 256GB OSD, you’re telling Ceph to store less data there, preventing it from filling up before the larger drives.

🚀 Need help fine-tuning Ceph pools, redundancy, or troubleshooting warnings? Let me know how the rebalancing goes!

Let’s create a simple automation to switch between performance and powersave modes on your Proxmox servers based on the time of day! Here’s how you can set it up:

✅ Step 1: Install cpufrequtils

First, ensure you have cpufrequtils installed on each Proxmox node:

sudo apt update
sudo apt install cpufrequtils

✅ Step 2: Create the scripts

✨ Script 1: Set performance mode (for daytime)

sudo nano /usr/local/bin/cpu_performance.sh

Add this:

#!/bin/bash
bash -c 'for((i=0;i<$(nproc);i++)); do cpufreq-set -c $i -g performance; done'

Save and exit.

✨ Script 2: Set powersave mode (for nighttime)

sudo nano /usr/local/bin/cpu_powersave.sh

Add this:

#!/bin/bash
bash -c 'for((i=0;i<$(nproc);i++)); do cpufreq-set -c $i -g powersave; done'

✅ Step 3: Make scripts executable

sudo chmod +x /usr/local/bin/cpu_performance.sh
sudo chmod +x /usr/local/bin/cpu_powersave.sh

✅ Step 4: Add cron jobs

Edit your crontab:

sudo crontab -e

Add these lines (adjust times as needed):

0 8 * * * /usr/local/bin/cpu_performance.sh  # Set to performance at 8 AM
0 22 * * * /usr/local/bin/cpu_powersave.sh    # Set to powersave at 10 PM

✅ Step 5: Verify

Check the CPU governor:

cat /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor

This setup keeps your servers snappy when you’re using them and more power-efficient overnight! Let me know if you’d like to fine-tune it further—maybe add logging or a health check? 🚀

You can use rsync with the --delete option to synchronize two drives and remove any extra files from the target drive that aren’t on the source drive! Let’s break it down.

✅ Basic command:

rsync -av --delete /path/to/source/ /path/to/target/

✨ Explanation:

• -a (archive): Preserves symbolic links, file permissions, modification times, and more.
• -v (verbose): Shows what’s happening during the sync.
• --delete: Removes files from the target that no longer exist in the source.
  • Important: Use with care—this option deletes extra files on the target!

✅ Dry run (recommended before running for real):

rsync -av --delete --dry-run /path/to/source/ /path/to/target/

This shows what would happen without making any changes.

✅ Excluding files:
If you want to avoid deleting certain files on the target:

rsync -av --delete --exclude='folder_or_file_to_keep' /path/to/source/ /path/to/target/

✅ Logging the sync:

rsync -av --delete /path/to/source/ /path/to/target/ --log-file=/path/to/rsync.log

✅ Automating with a cron job:
Edit your crontab with crontab -e and add:

0 2 * * * rsync -a --delete /path/to/source/ /path/to/target/

This runs the sync daily at 2 AM.

Would you like to fine-tune it, like setting up email notifications if the sync fails? I can help with that too! 🚀

Some of the best Proxmox videos are a series provided by Jay LaCroix on the Learn Linux TV YouTube channel. I followed along with the videos on my own Proxmox server. There was so much good information that rather than take notes, I decided to work through the entire series then repeat while taking notes.

Virtual Machines (VMs)

When defining VM, discard (for ssd) and qemu agent should be checked
use scsi hard disk for Windows
cloud-init

sudo apt install qemu-guest-agent
sudo systemctl start qemu-guest-agent
sudo systemctl status qemu-guest-agent
sudo systemctl enable qemu-guest-agent

sudo systemctl s qemu-guest-agent

Windows VMs

For disk, use SCSI bus and cache write back and discard for ssd

Templates

When creating a template there needs to be some tidying up:

sudo apt clean
sudo apt autoremove
sudo rm /etc/ssh/ssh_host_*
sudo truncate -s 0 /etc/machine-id
verify that: /var/lib/dbus/machine-id -> /etc/machine-id
sudo apt install cloud-init
sudo poweroff
add Cloud-Init drive to template hardware
edit cloud-init to add user, password and SSH public key

clone CT template with Mode: Full Clone
cloud-init not available for containers
sudo dpkg-reconfigure openssh-server
change /etc/hosts
change /etc/hostname

Containers

Proxmox uses linux containers or LXCs (lex-see)

The user created in a container is root so you need to add your own user:

adduser lynn
usermod -aG sudo lynn

One limitation of containers is that live migration is not supported. The container must be stopped before migrating it to a different node.

Firewall

Todo

Command Line

qm list
qm start 101
qm stop 101
qm reboot 101
qm reset 101 (last resort)
qm stop 101 (last resort)

Ubuntu Server Template Checklist

install nano
install up.sh

To experiment with Proxmox High Availability (HA), I tried it out using nested hosts. To validate that nested virtualization is supported, the following command should return a ‘Y’.

cat /sys/module/kvm_intel/paramaters/nested

I created guest VMs pve1, pve2 and pve3 on my main Proxmox server. Note that when creating the VMs, the CPU type needs to be ‘host’ in order to enable virtualization on the nested servers. I cloned the first VM twice changing the host names and the machine ids. My main clusters run on 4th generation Intel systems that are inexpensive and very low power. For the first time, creation of these guest VMs pushed the limits of my system and took a long time to complete.

On the nested hosts, enter the following commands. Note that pve1 summary will show “Guest Agent not running” in the IPs section. After these commands, this section wil show the IP address.

apt install qemu-guest-agent
systemctl start qemu-guest-agent
systemctl enable qemu-guest-agent

Before enabling HA, you must enable shared storage using Ceph, the name of which is derived from the marine animals cephalopods. Ceph uses storage from each of the nodes, three in this case, such that you can remove any one of the nodes and the remaining two still have a complete set of the data. The VM hard drive must be moved to the Ceph storage pool. Brandon Lee has an excellent video for enabling Ceph.

I’ve implemented a three-node Proxmox cluster. It’s making it very easy to deploy backup servers and copies of servers for testing. Servers can easily be migrated from one node to another. It doesn’t take long to right click a node and clone it. It’s much faster to convert a node to a template then create a new node from the template.

After making a clone the server has the same hostname (/etc/hostname) and hosts files (/etc/hosts) of the original. These two files must be edited to reflect a new host name. Another setting that must be changed is the machine id (/etc/machine-id). I use the Pi-hole DHCP server and it uses the machine id to assign IP addresses. If this is not changed, the original server and cloned server will get the same IP address.

These four steps will make the machine-id file empty; remove the symbolic link to the machine-id file, give the file a random machine id and restore the symbolic link to the machine-id file.

sudo truncate -s 0 /etc/machine-id
sudo rm -f /var/lib/dbus/machine-id
sudo systemd-machine-id-setup
sudo ln -s /etc/machine-id /var/lib/dbus/machine

sudo sed -i s/us.archive/old-releases/ /etc/apt/sources.list

These commands are executed in ~/bin/clone.

Stub…

The official Pi-hole website has a great tutorial. It suggests a single line install:

curl -sSL https://install.pi-hole.net | bash

As the DHCP server, Pi-hole needs a static IP address. The network definition moved again and can be edited with this command:

sudo nmtui

Add a secondary DNS server by creating /etc/dnsmasq.d/99-second-DNS.conf and adding this line:

dhcp-option=option:dns-server,192.168.2.2,192.168.2.1

The day after I moved my DHCP server to Pi-hole on my Proxmox server, my Proxmox startup failed to boot with a grub error. I’ve done regular backups of all but my recent VMs so I should be able to execute my restore plan by installing a new instance of Proxmox and restoring the VMs.

My server is a Lenovo M73 ThinkCentre tiny computer. There’s no room for a second drive for a mirror, so I live dangerously. The M73 has an Intel Core-i5 cpu which is 10 generations back (2013) but it really gets the job done for me. I eventually aquired three spares. I’m more into getting the job done efficiently at a low cost than having expensive failsafe systems. I have the opportunity to cluster two or three instances of Proxmox so I’m off on another learning experience.

After updating my main server, it failed to boot with a grub error. I was relieved to find a Proxmox wiki that described the recovery. There’s a known Debian LVM bug that caused the grub “disk not found” failure. The temporary fix is to trigger an LVM metadata update by creating a small logical volume. The only permanent fix for PVE 7.x is to upgrade to PVE8. The permanent fix for PVE 8 is to install the correct metapackage for EUFI.

The Proxmox upgrade is a simple “apt update && apt upgrade”. However the enterprise respository is enabled by default and unless you are an enterprise, you should use the “no subsription” repositories. To disable the enterprise repository, see this wiki or better yet, this one.

Replace the Proxmox Drive

I’ve backed up the nodes on my main Proxmox server to an external hard drive. I pulled the SSD drive and replaced it with a new SSD. I installed the latest release of Proxmox. Now it’s time to restore the VMs. From the Proxmox shell, connect the backup drive from the previous Proxmox and mount it. From Datacenter, add the mount point directory to Datacenter storage. Add the mount to fstab for persistance. ChatGPT helps with the details. I restored my VMs from the backup drive successfully.

Proxmox Cluster

I now have three Proxmox nodes that are all up to date with the same version. I ran backups on each node. I’m confident that I can recover from a hard system failure using the external backup drive on my first node. Proxmox clusters make decisions based on majority vote so it’s important to have an odd number of nodes. Two nodes can work with the addition of a qdevice as the third vote giving a quorum. A raspberry pi is sufficient for a qdevice. However, my hardware is cheap and low power. Three nodes should make for a full function cluster. I installed npt on each node for time synchronization which is required

I used a video by Tech Tutorials – David McKone to set up the cluster. The VMs on the first node will remain but I had to remove all the VMs on the other nodes.

I followed the video very carefully up to the point where I clicked “join”. My second node stopped responding because of an IP conflict. My first Proxmox node had Pi-hole with my DHCP server. It was down for 24 hours while I replaced the hard disk. I started the DHCP server on my router, knowing the potential for conflicts. The join stalled and nothing I found on the internet helped. The partial join caused the quorum to increase to two nodes but only one node worked. No quorum – nothing worked. Then I identified the problem to ChatGPT and got all the commands I needed to repair the damage. ChatGPT even included “copy” buttons for the commands of which the name of my corrupted server was included! I added the third node simply by hitting the “join” button. Now I can loose a node and still have a quorum.

My third node has problems I can’t fix. I change the quorum

This is to document the steps taken to migrate my MagicMirror installation from a Raspberry Pi 3 to a Raspberry Pi 2. I need the 3 for a NEMS (Nagios) installation.

1. Format the SD card with SDFormatter
2. Install Raspbian on the SD card with Raspberry Pi Imager
3. Use ChatGPT “How to migrate magic mirror to a new computer” for steps
4. Install Node.js using PiMyLifeUp tutorial

Windows 7

One of the best versions of Sid Meier’s Civilization series is Civilization III released in 2001. However, it will only run under Windows 7 with a physical CD mounted. I first tried it on a physical machine but it stopped working and I decided to give it a spin as a virtual machine under Proxmox. The Internet Archive provide a copy of the Windows 7 ISO. The Proxmox web site provides a Windows 7 guest best practices document. It requires mounting additional VirtIO drivers during the installation.

Windows 10

Windows 10 also has a best guest practices document.

MacOS 14 Sonoma

Here’s a video by i12bretro on installing Sonoma as a guest VM with a a checkling on github. I think this guide will help, too.