text generator

import random class ArticleGenerator: def __init__(self): self.templates = [ "The {adj1} {noun} {verb} over the {adj2} {noun2}.", "In a {adj1} turn of events, {noun} {verb} {adj2}ly.", "{noun} {verb} {adj1}, making it a {adj2} story." ] self.adjectives = ["quick", "lazy", "brown", "noisy", "blue", "green"] self.nouns = ["fox", "dog", "head", "leg", "cat"] self.verbs = ["jumps", "lifts", "bites", "licks", "kicks"] self.nouns2 = ["table", "chair", "rug", "book", "shoe"] def generate_article(self): template = random.choice(self.templates) adj1 = random.choice(self.adjectives) adj2 = random.choice(self.adjectives) noun = random.choice(self.nouns) verb = random.choice(self.verbs) noun2 = random.choice(self.nouns2) article = template.format(adj1=adj1, adj2=adj2, noun=noun, verb=verb, noun2=noun2) return article # Example usage: generator = ArticleGenerator() for _ in range(5): generated_article = generator.generate_article() print(generated_article)

montage calculator

import tkinter as tk from tkinter import messagebox def calculate(): try: amount = float(entry_amount.get()) years = int(entry_years.get()) interest_rate = float(entry_interest_rate.get()) / 100 total = amount * (1 + interest_rate * years) messagebox.showinfo("ISA Calculator", f"Your ISA will be worth £{total:.2f} after {years} years.") except ValueError: messagebox.showerror("Error", "Please enter valid numbers.") # Create the main window root = tk.Tk() root.title("ISA Calculator") # Create labels label_amount = tk.Label(root, text="Initial Amount:") label_years = tk.Label(root, text="Number of Years:") label_interest_rate = tk.Label(root, text="Interest Rate (%):") label_amount.grid(row=0, column=0, padx=10, pady=5, sticky=tk.E) label_years.grid(row=1, column=0, padx=10, pady=5, sticky=tk.E) label_interest_rate.grid(row=2, column=0, padx=10, pady=5, sticky=tk.E) # Create entry boxes entry_amount = tk.Entry(root) entry_years = tk.Entry(root) entry_interest_rate = tk.Entry(root) entry_amount.grid(row=0, column=1, padx=10, pady=5) entry_years.grid(row=1, column=1, padx=10, pady=5) entry_interest_rate.grid(row=2, column=1, padx=10, pady=5) # Create calculate button calculate_button = tk.Button(root, text="Calculate", command=calculate) calculate_button.grid(row=3, columnspan=2, padx=10, pady=10) # Run the main loop root.mainloop() function calculateMortgage() { var loanAmount = parseFloat(document.getElementById('loanAmount').value); var interestRate = parseFloat(document.getElementById('interestRate').value) / 100 / 12; var loanTerm = parseFloat(document.getElementById('loanTerm').value) * 12; var monthlyPayment = (loanAmount * interestRate) / (1 - Math.pow(1 + interestRate, -loanTerm)); var totalPayment = monthlyPayment * loanTerm; var totalInterest = totalPayment - loanAmount; displayResults(monthlyPayment, totalPayment, totalInterest); } function displayResults(monthlyPayment, totalPayment, totalInterest) { var monthlyPaymentElement = document.getElementById('monthlyPayment'); var totalPaymentElement = document.getElementById('totalPayment'); var totalInterestElement = document.getElementById('totalInterest'); monthlyPaymentElement.textContent = 'Monthly Payment: $' + monthlyPayment.toFixed(2); totalPaymentElement.textContent = 'Total Payment: $' + totalPayment.toFixed(2); totalInterestElement.textContent = 'Total Interest: $' + totalInterest.toFixed(2); }

tag generator 1

Tag Generator

Enter Keyword:

tag generator

from nltk.tokenize import word_tokenize from nltk.corpus import stopwords import spacy from collections import Counter # Define stopwords and maximum number of hashtags stop_words = stopwords.words("english") max_hashtags = 5 # Load spaCy model nlp = spacy.load("en_core_web_sm") def generate_hashtags(text): """ Generates hashtags from a given text. Args: text: The text to analyze. Returns: A list of generated hashtags. """ # Lowercase and tokenize the text tokens = word_tokenize(text.lower()) # Remove stopwords filtered_tokens = [token for token in tokens if token not in stop_words] # Extract named entities doc = nlp(" ".join(filtered_tokens)) entities = [str(ent) for ent in doc.ents] # Combine keywords and entities potential_hashtags = filtered_tokens + entities # Remove invalid characters and duplicates valid_hashtags = [ hashtag.replace(" ", "_") for hashtag in potential_hashtags if hashtag.isalnum() and len(hashtag) <= 25 ] valid_hashtags = list(set(valid_hashtags)) # Count occurrences and select top hashtags hashtag_counts = Counter(valid_hashtags) top_hashtags = hashtag_counts.most_common(max_hashtags) # Return a list of top hashtags return [hashtag for hashtag, count in top_hashtags] # Example usage text = "This is a beautiful article about the Great Barrier Reef and its diverse marine life." hashtags = generate_hashtags(text) print(f"Generated hashtags: {hashtags}")