import tkinter as tk

def perform_search():

    query = entry.get()

    # Perform search with the query (replace this with your actual search functionality)

    result_label.config(text=f"Search Results for: {query}")

# Create the main window

root = tk.Tk()

root.title("Search Bar")

# Create and place the entry widget

entry = tk.Entry(root, width=30)

entry.pack(pady=10)

# Create and place the search button

search_button = tk.Button(root, text="Search", command=perform_search)

search_button.pack()

import tkinter as tk def perform_search(): query = entry.get() # Perform search with the query (replace this with your actual search functionality) result_label.config(text=f"Search Results for: {query}") # Create the main window root = tk.Tk() root.title("Search Bar") # Create and place the entry widget entry = tk.Entry(root, width=30) entry.pack(pady=10) # Create and place the search button search_button = tk.Button(root, text="Search", command=perform_search) search_button.pack() # Create and place a label for displaying search results result_label = tk.Label(root, text="") result_label.pack(pady=10) # Run the Tkinter event loop root.mainloop()

# Create and place a label for displaying search results

result_label = tk.Label(root, text="")

result_label.pack(pady=10)

# Run the Tkinter event loop

root.mainloop()

 

import itertools def generate_keywords(topic, variations=['', 'ideas', 'tips', 'guide', 'tutorial']): # Combine the topic with different variations keywords = [f"{topic} {variation}" for variation in variations] # Generate combinations of the topic and its words topic_words = topic.split() for r in range(1, len(topic_words) + 1): combinations = list(itertools.combinations(topic_words, r)) for combo in combinations: keywords.append(' '.join(combo)) return keywords # Example usage topic = "content marketing" result_keywords = generate_keywords(topic) # Display the generated keywords for keyword in result_keywords: print(keyword)

 

import cv2 import numpy as np def sketch_photo(image_path, output_path, scale_factor=0.6): # Read the image image = cv2.imread(image_path) # Resize the image to improve processing speed height, width = image.shape[:2] new_width = int(scale_factor * width) new_height = int(scale_factor * height) resized_image = cv2.resize(image, (new_width, new_height)) # Convert the image to grayscale grayscale_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2GRAY) # Apply GaussianBlur to reduce noise and improve edge detection blurred_image = cv2.GaussianBlur(grayscale_image, (5, 5), 0) # Use the Canny edge detector to find edges in the image edges = cv2.Canny(blurred_image, 30, 100) # Invert the binary image (black and white) inverted_edges = cv2.bitwise_not(edges) # Create a blank white canvas canvas = np.ones_like(resized_image) * 255 # Combine the original image with the inverted edges sketch = cv2.bitwise_or(canvas, canvas, mask=inverted_edges) # Save the resulting sketch cv2.imwrite(output_path, sketch) # Example usage input_image_path = "path/to/your/photo.jpg" output_sketch_path = "path/to/your/output_sketch.jpg" sketch_photo(input_image_path, output_sketch_path)

THIS EBOOK “ YOUTUBE SUCCESS GUIDE”

 

YOUTUBE SUCCESS

GUIDE