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from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from bs4 import BeautifulSoup
import time
# !pip install tensorflow tensorflow-hub
import tensorflow as tf
import tensorflow_hub as hub
import numpy as np
# !pip install jellyfish
import jellyfish
# Load the pre-trained Universal Sentence Encoder
embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
def calculate_jaro_similarity(str1, str2):
jaro_similarity = jellyfish.jaro_distance(str1, str2)
return jaro_similarity
def most_similar_sentence(target_topic, labels_list):
# Encode the context sentence and all sentences in the list
context_embedding = embed([target_topic])[0]
sentence_embeddings = embed(labels_list)
# Calculate cosine similarities between the context sentence and each sentence in the list
similarities = np.inner(context_embedding, sentence_embeddings)
# Find the index of the most similar sentence
most_similar_index = np.argmax(similarities)
return labels_list[most_similar_index], similarities[most_similar_index], most_similar_index
def search_wikipedia(query, driver):
# Go to Wikipedia's main page
driver.get("https://www.wikipedia.org/")
# Find the search bar using its name
search_bar = driver.find_element(By.NAME, "search")
# Send the query to the search bar and hit Enter
search_bar.send_keys(query)
search_bar.send_keys(Keys.RETURN)
return driver
def get_topic_context(driver):
# Find the first paragraph of the main article
first_paragraph = driver.find_element(By.CSS_SELECTOR, "div.mw-parser-output > p:not(.mw-empty-elt)").text
context_sentence = first_paragraph.split(". ")[0]
# print(context_sentence)
return context_sentence
def search_wikipedia(query, driver):
# Go to Wikipedia's main page
driver.get("https://www.wikipedia.org/")
# Find the search bar using its name
search_bar = driver.find_element(By.NAME, "search")
# Send the query to the search bar and hit Enter
search_bar.send_keys(query)
search_bar.send_keys(Keys.RETURN)
return driver
def get_topic_context(driver):
# Find the first paragraph of the main article
first_paragraph = driver.find_element(By.CSS_SELECTOR, "div.mw-parser-output > p:not(.mw-empty-elt)").text
context_sentence = first_paragraph.split(". ")[0]
# print(context_sentence)
return context_sentence
def play_wiki_game(starting_topic: str, target_topic: str, limit: int = 100):
##### Setup Chrome options
chrome_options = webdriver.ChromeOptions()
chrome_options.add_argument("--headless") # Ensure GUI is off
chrome_options.add_argument("--no-sandbox")
chrome_options.add_argument("--disable-dev-shm-usage")
driver = webdriver.Chrome(options = chrome_options)
topic = starting_topic
num_pages = 0
used_topics = []
used_links = []
start_time = time.time()
### BEGIN ###
print("-" * 150)
print(f"\nStarting!\n")
print("-" * 150)
driver = search_wikipedia(starting_topic, driver)
used_links.append(driver.current_url)
while True:
# increment the page tracking by 1 for each new page
num_pages += 1
# if not the first page, navigate to the new page
if num_pages > 1:
driver.get(next_link)
context_sentence = get_topic_context(driver)
links_texts = []
current_url = driver.current_url
current_url_suffix = str(current_url).split("/")[-1]
### Use BeautifulSoup and Requests instead of Selenium for link extraction
current_page = driver.page_source # html from Selenium instead of BeautifulSoup
soup = BeautifulSoup(current_page, 'html.parser')
links = soup.find_all('a')
# Iterate through the links and extract their URLs
for link in links:
link_url = link.get('href')
if link_url and link_url.startswith("/wiki/"):
link_url = "https://en.wikipedia.org" + link_url
link_text = link.text.strip() # Get the text and remove leading/trailing spaces
# make sure they are both not None
if link_text and current_url_suffix not in link_url:
if link_url not in used_links and link_text not in used_topics:
# eliminates topic duplicates, non-wiki links, and wiki-help pages (non-content pages)
if topic.lower() not in link_url.lower() and "en.wikipedia.org/wiki/" in link_url and ":" not in "".join(link_url.split("/")[1:]) and "Main_Page" != str(link_url.split("/")[-1]):
links_texts.append((link_url, link_text))
best_label, best_score, loc_idx = most_similar_sentence(target_topic = target_topic, labels_list = [text for link, text in links_texts])
print(f"\nPage: {num_pages}")
print(f"Current topic: '{topic.title()}'")
print(f"Current URL: '{current_url}'")
print(f"Current Topic Context: '{context_sentence}'")
print(f"Next topic: '{best_label.title()}'. Semantic similarity to '{target_topic.title()}': {round((best_score * 100), 2)}%")
next_link, topic = links_texts[loc_idx]
# print(next_link)
# if target_topic.lower() in topic.lower():# or best_score > float(0.85):
if target_topic.lower() == topic.lower() or calculate_jaro_similarity(target_topic.lower(), topic.lower()) > 0.9 or best_score > float(0.90): # if topic text is identical or at least 90% the same spelling
print("\n" + "-" * 150)
print(f"\nFrom '{starting_topic.title()}', to '{target_topic.title()}' in {num_pages} pages, {round(time.time() - start_time, 2)} seconds!")
print(f"Starting topic: '{starting_topic.title()}': '{used_links[0]}'")
print(f"Target topic: '{target_topic.title()}': '{used_links[-1]}'\n")
print("-" * 150)
break
##### ADD DRAMATIC DELAY HERE #####
# time.sleep(0.5)
# time.sleep(10)
if num_pages == limit:
print("\n" + "-" * 150)
print(f"\nUnfortunately, the model couldn't get from '{starting_topic.title()}', to '{target_topic.title()}' in {num_pages} pages or less.")
print(f"In {round(time.time() - start_time, 2)} seconds, it got from '{starting_topic.title()}': '{used_links[0]}', to '{target_topic.title()}': '{used_links[-1]}'")
print(f"\nTry a different combination to see if it can do it!\n")
print("-" * 150)
break
used_links.append(next_link)
used_topics.append(topic)
driver.quit()
###### Example
# starting_topic = "soulja boy"
# target_topic = "test"
# play_wiki_game(starting_topic = starting_topic, target_topic = target_topic, limit = 50) |