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| import requests | |
| from bs4 import BeautifulSoup | |
| import time | |
| import tensorflow as tf | |
| import tensorflow_hub as hub | |
| import numpy as np | |
| import jellyfish | |
| import re | |
| import streamlit as st | |
| # 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): | |
| context_embedding = embed([target_topic])[0] | |
| sentence_embeddings = embed(labels_list) | |
| similarities = np.inner(context_embedding, sentence_embeddings) | |
| most_similar_index = np.argmax(similarities) | |
| return labels_list[most_similar_index], similarities[most_similar_index], most_similar_index | |
| def get_wikipedia_page(query): | |
| response = requests.get(f"https://en.wikipedia.org/wiki/{query}") | |
| return response.text | |
| def get_topic_context(page_source): | |
| soup = BeautifulSoup(page_source, 'html.parser') | |
| first_paragraph = soup.select_one("div.mw-parser-output > p:not(.mw-empty-elt)").text | |
| context_sentence = re.sub(r'\[.*?\]', '', first_paragraph) | |
| context_sentence = context_sentence.split(". ")[0].strip().replace("\n", "") | |
| while " " in context_sentence: | |
| context_sentence = context_sentence.replace(" ", " ") | |
| return context_sentence | |
| def play_wiki_game(starting_topic: str, target_topic: str, limit: int = 100): | |
| topic = starting_topic | |
| num_pages = 0 | |
| used_topics = [] | |
| used_links = [] | |
| start_time = time.time() | |
| st.write("-" * 150) | |
| st.write(f"\nStarting!\n") | |
| st.write("-" * 150) | |
| page_source = get_wikipedia_page(starting_topic) | |
| used_links.append(f"https://en.wikipedia.org/wiki/{starting_topic}") | |
| while True: | |
| num_pages += 1 | |
| if num_pages > 1: | |
| # load url to new page of next topic | |
| page_source = get_wikipedia_page(topic) | |
| # create backup list of links and texts from previous page in case new current page fails | |
| prev_links_texts = links_texts.pop(loc_idx) # removes the previously used topic | |
| try: | |
| context_sentence = get_topic_context(page_source) | |
| except Exception as e: | |
| context_sentence = "Context could not be found from webpage" | |
| links_texts = [] | |
| soup = BeautifulSoup(page_source, 'html.parser') | |
| links = soup.find_all('a') | |
| 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() | |
| if link_text and topic.lower() not in link_url.lower() and link_url not in used_links and link_text not in used_topics: | |
| if "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)) | |
| # st.write(f'links_texts length: {len(links_texts)}') | |
| if len(links_texts) == 0 and num_pages > 1: # if no links | |
| links_texts = prev_links_texts | |
| labels_list = [text for link, text in links_texts] | |
| # st.write(f'labels_list length: {len(labels_list)}') | |
| try: | |
| best_label, best_score, loc_idx = most_similar_sentence(target_topic = target_topic, labels_list = labels_list) | |
| except Exception as e: | |
| best_label, best_score, loc_idx = most_similar_sentence(target_topic = target_topic, labels_list = prev_links_texts) # previous page links without chosen error page | |
| st.write(f"\nPage: {num_pages}") | |
| st.write(f"Current topic: '{topic.title()}'") | |
| st.write(f"Current URL: 'https://en.wikipedia.org/wiki/{topic}'") | |
| st.write(f"Current Topic Context: '{context_sentence}'") | |
| st.write(f"Next topic: '{best_label.title()}'. Semantic similarity to '{target_topic.title()}': {round((best_score * 100), 2)}%") | |
| next_link, topic = links_texts[loc_idx] | |
| if target_topic.lower() == topic.lower() or calculate_jaro_similarity(target_topic.lower(), topic.lower()) > 0.9 or best_score > float(0.90): | |
| st.write("\n" + "-" * 150) | |
| st.write(f"\nFrom '{starting_topic.title()}', to '{target_topic.title()}' in {num_pages} pages, {round(time.time() - start_time, 2)} seconds!") | |
| st.write(f"Starting topic: '{starting_topic.title()}': {used_links[0].replace(' ', '_')}") | |
| st.write(f"Target topic: '{target_topic.title()}': '{used_links[-1].replace(' ', '_')}'\n") | |
| st.write("-" * 150) | |
| break | |
| if num_pages == limit: | |
| st.write("\n" + "-" * 150) | |
| st.write(f"\nUnfortunately, the model couldn't get from '{starting_topic.title()}', to '{target_topic.title()}' in {num_pages} pages or less.") | |
| st.write(f"In {round(time.time() - start_time, 2)} seconds, it got from '{starting_topic.title()}': '{used_links[0].replace(' ', '_')}', to '{used_topics[-1].title()}': '{used_links[-1].replace(' ', '_')}'") | |
| st.write(f"\nTry a different combination to see if it can do it!\n") | |
| st.write("-" * 150) | |
| break | |
| used_links.append(next_link) | |
| used_topics.append(topic) | |
| starting_topic = "soulja boy" | |
| target_topic = "game" | |
| play_wiki_game(starting_topic = starting_topic, target_topic = target_topic, limit = 100) | |
| # starting_topic = "soulja boy" | |
| # target_topic = "test" | |
| # play_wiki_game(starting_topic = starting_topic, target_topic = target_topic, limit = 50) |