Building a Web Scraper to Track Product Prices and Send Alerts

I created a tutorial on "How to Scrape Websites with Python" and added an affiliate link to a web scraping API. Every time someone signed up, I got a commission.

Eventually, I turned my script into a web app with Flask and hosted it on Heroku. I charged $9/month for unlimited scraping, and within a few months, I had over 50 active users paying for access.

• تقديم الدعم الذكي: توفير ردود فورية للمستخدمين.
• تجربة ممتعة: خلق تجربة تفاعلية تجعل المستخدمين يشعرون بالارتباط.
• توفير التكاليف: يقلل الحاجة للدعم البشري الدائم.

لنبني روبوت الدردشة الخاص بنا، سنحتاج إلى:

   python websocket_server.py

   python app.py

Let’s add functionality to respond to basic tasks like saying the time, performing web searches, or opening apps.

import datetime
import pywhatkit

def process_command(command):
    if "time" in command:
        now = datetime.datetime.now().strftime("%H:%M")
        speak(f"The time is {now}")
    elif "search for" in command:
        query = command.replace("search for", "").strip()
        speak(f"Searching for {query}")
        pywhatkit.search(query)
    elif "play" in command:
        song = command.replace("play", "").strip()
        speak(f"Playing {song}")
        pywhatkit.playonyt(song)
    else:
        speak("I'm sorry, I can't perform that task yet.")

import cv2
import face_recognition
import pickle
from datetime import datetime

# Load encodings
with open("encodings.pickle", "rb") as f:
    data = pickle.load(f)

# Initialize webcam
video_capture = cv2.VideoCapture(0)

# Track attendance
attendance_log = set()

while True:
    ret, frame = video_capture.read()
    if not ret:
        break

    # Resize frame for faster processing
    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
    rgb_frame = small_frame[:, :, ::-1]

    # Detect faces and compare
    face_locations = face_recognition.face_locations(rgb_frame)
    face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

    for face_encoding, face_location in zip(face_encodings, face_locations):
        matches = face_recognition.compare_faces(data["encodings"], face_encoding)
        name = "Unknown"

        if True in matches:
            match_index = matches.index(True)
            name = data["names"][match_index]

            # Log attendance
            if name not in attendance_log:
                attendance_log.add(name)
                print(f"{name} marked present at {datetime.now()}")

        # Display bounding box and name
        top, right, bottom, left = [v * 4 for v in face_location]
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)
        cv2.putText(frame, name, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

    # Show video feed
    cv2.imshow("Attendance System", frame)

    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

video_capture.release()
cv2.destroyAllWindows()

Store the attendance data in an SQLite database for record-keeping.

After completing the above steps, you will have a fully automated Excel report with:

• Formatted data for better readability.
• Conditional highlighting for key metrics.
• A summary sheet with actionable insights.

To activate the virtual environment, use:

source myenv/bin/activate