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| #In streamlit and python edit this example and add tracking of the option selections by adding buttons for the three choice sets for options. Also save the values to text file and show full history after an option is recorded. import streamlit as st | |
| import spacy | |
| import wikipediaapi | |
| import wikipedia | |
| from wikipedia.exceptions import DisambiguationError | |
| from transformers import TFAutoModel, AutoTokenizer | |
| import numpy as np | |
| import pandas as pd | |
| import faiss | |
| import datetime | |
| import time | |
| st.markdown(""" | |
| Scene 1: The Enchanted Castle | |
| You arrive at the enchanted castle, surrounded by a forest of thorns. You have heard stories of a beautiful princess asleep within, waiting for someone to awaken her. | |
| Option 1: Try to make your way through the thorns. | |
| Option 2: Look for another way in. | |
| Sentiment: Feels like harsher trials after passive sleep. | |
| --- | |
| Scene 2: The Castle's Secrets | |
| If you made it past the thorns, you discover that the castle is full of hidden chambers, each containing a different trial. | |
| These trials are designed to test your limits, reveal your inner most desires, and help you understand the suffering of humankind. | |
| Option 1: Enter the first chamber. | |
| Option 2: Continue exploring the castle. | |
| Sentiment: Comedy ending in marriage. | |
| --- | |
| Scene 3: The Princess's Awakening | |
| After navigating the castle's trials, you finally reach the chamber where the princess lies sleeping. | |
| You are faced with the decision of how to awaken her, knowing that your actions will determine the nature of your relationship with her. | |
| Option 1: Awaken her with a gentle kiss. | |
| Option 2: Awaken her through a more assertive act like lifting her up. | |
| Sentiment: Heart forged awakening with different implications depending on context. | |
| """) | |
| try: | |
| nlp = spacy.load("en_core_web_sm") | |
| except: | |
| spacy.cli.download("en_core_web_sm") | |
| nlp = spacy.load("en_core_web_sm") | |
| wh_words = ['what', 'who', 'how', 'when', 'which'] | |
| def get_concepts(text): | |
| text = text.lower() | |
| doc = nlp(text) | |
| concepts = [] | |
| for chunk in doc.noun_chunks: | |
| if chunk.text not in wh_words: | |
| concepts.append(chunk.text) | |
| return concepts | |
| def get_passages(text, k=100): | |
| doc = nlp(text) | |
| passages = [] | |
| passage_len = 0 | |
| passage = "" | |
| sents = list(doc.sents) | |
| for i in range(len(sents)): | |
| sen = sents[i] | |
| passage_len += len(sen) | |
| if passage_len >= k: | |
| passages.append(passage) | |
| passage = sen.text | |
| passage_len = len(sen) | |
| continue | |
| elif i == (len(sents) - 1): | |
| passage += " " + sen.text | |
| passages.append(passage) | |
| passage = "" | |
| passage_len = 0 | |
| continue | |
| passage += " " + sen.text | |
| return passages | |
| def get_dicts_for_dpr(concepts, n_results=20, k=100): | |
| dicts = [] | |
| for concept in concepts: | |
| wikis = wikipedia.search(concept, results=n_results) | |
| st.write(f"{concept} No of Wikis: {len(wikis)}") | |
| for wiki in wikis: | |
| try: | |
| html_page = wikipedia.page(title=wiki, auto_suggest=False) | |
| except DisambiguationError: | |
| continue | |
| htmlResults = html_page.content | |
| passages = get_passages(htmlResults, k=k) | |
| for passage in passages: | |
| i_dicts = {} | |
| i_dicts['text'] = passage | |
| i_dicts['title'] = wiki | |
| dicts.append(i_dicts) | |
| return dicts | |
| passage_encoder = TFAutoModel.from_pretrained("nlpconnect/dpr-ctx_encoder_bert_uncased_L-2_H-128_A-2") | |
| query_encoder = TFAutoModel.from_pretrained("nlpconnect/dpr-question_encoder_bert_uncased_L-2_H-128_A-2") | |
| p_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/dpr-ctx_encoder_bert_uncased_L-2_H-128_A-2") | |
| q_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/dpr-question_encoder_bert_uncased_L-2_H-128_A-2") | |
| def get_title_text_combined(passage_dicts): | |
| res = [] | |
| for p in passage_dicts: | |
| res.append(tuple((p['title'], p['text']))) | |
| return res | |
| def extracted_passage_embeddings(processed_passages, max_length=156): | |
| passage_inputs = p_tokenizer.batch_encode_plus( | |
| processed_passages, | |
| add_special_tokens=True, | |
| truncation=True, | |
| padding="max_length", | |
| max_length=max_length, | |
| return_token_type_ids=True | |
| ) | |
| passage_embeddings = passage_encoder.predict([np.array(passage_inputs['input_ids']), np.array(passage_inputs['attention_mask']), | |
| np.array(passage_inputs['token_type_ids'])], | |
| batch_size=64, | |
| verbose=1) | |
| return passage_embeddings | |
| def extracted_query_embeddings(queries, max_length=64): | |
| query_inputs = q_tokenizer.batch_encode_plus( | |
| queries, | |
| add_special_tokens=True, | |
| truncation=True, | |
| padding="max_length", | |
| max_length=max_length, | |
| return_token_type_ids=True | |
| ) | |
| query_embeddings = query_encoder.predict([np.array(query_inputs['input_ids']), | |
| np.array(query_inputs['attention_mask']), | |
| np.array(query_inputs['token_type_ids'])], | |
| batch_size=1, | |
| verbose=1) | |
| return query_embeddings | |
| def get_pagetext(page): | |
| s = str(page).replace("/t","") | |
| return s | |
| def get_wiki_summary(search): | |
| wiki_wiki = wikipediaapi.Wikipedia('en') | |
| page = wiki_wiki.page(search) | |
| def get_wiki_summaryDF(search): | |
| wiki_wiki = wikipediaapi.Wikipedia('en') | |
| page = wiki_wiki.page(search) | |
| isExist = page.exists() | |
| if not isExist: | |
| return isExist, "Not found", "Not found", "Not found", "Not found" | |
| pageurl = page.fullurl | |
| pagetitle = page.title | |
| pagesummary = page.summary[0:60] | |
| pagetext = get_pagetext(page.text) | |
| backlinks = page.backlinks | |
| linklist = "" | |
| for link in backlinks.items(): | |
| pui = link[0] | |
| linklist += pui + " , " | |
| a=1 | |
| categories = page.categories | |
| categorylist = "" | |
| for category in categories.items(): | |
| pui = category[0] | |
| categorylist += pui + " , " | |
| a=1 | |
| links = page.links | |
| linklist2 = "" | |
| for link in links.items(): | |
| pui = link[0] | |
| linklist2 += pui + " , " | |
| a=1 | |
| sections = page.sections | |
| ex_dic = { | |
| 'Entity' : ["URL","Title","Summary", "Text", "Backlinks", "Links", "Categories"], | |
| 'Value': [pageurl, pagetitle, pagesummary, pagetext, linklist,linklist2, categorylist ] | |
| } | |
| df = pd.DataFrame(ex_dic) | |
| return df | |
| def save_message(name, message): | |
| now = datetime.datetime.now() | |
| timestamp = now.strftime("%Y-%m-%d %H:%M:%S") | |
| with open("chat.txt", "a") as f: | |
| f.write(f"{timestamp} - {name}: {message}\n") | |
| def press_release(): | |
| st.markdown("""🎉🎊 Breaking News! 📢📣 | |
| Introducing StreamlitWikipediaChat - the ultimate way to chat with Wikipedia and the whole world at the same time! 🌎📚👋 | |
| Are you tired of reading boring articles on Wikipedia? Do you want to have some fun while learning new things? Then StreamlitWikipediaChat is just the thing for you! 😃💻 | |
| With StreamlitWikipediaChat, you can ask Wikipedia anything you want and get instant responses! Whether you want to know the capital of Madagascar or how to make a delicious chocolate cake, Wikipedia has got you covered. 🍰🌍 | |
| But that's not all! You can also chat with other people from around the world who are using StreamlitWikipediaChat at the same time. It's like a virtual classroom where you can learn from and teach others. 🌐👨🏫👩🏫 | |
| And the best part? StreamlitWikipediaChat is super easy to use! All you have to do is type in your question and hit send. That's it! 🤯🙌 | |
| So, what are you waiting for? Join the fun and start chatting with Wikipedia and the world today! 😎🎉 | |
| StreamlitWikipediaChat - where learning meets fun! 🤓🎈""") | |
| def main(): | |
| st.title("Streamlit Chat") | |
| name = st.text_input("Enter your name") | |
| message = st.text_input("Enter a topic to share from Wikipedia") | |
| if st.button("Submit"): | |
| # wiki | |
| df = get_wiki_summaryDF(message) | |
| save_message(name, message) | |
| save_message(name, df) | |
| st.text("Message sent!") | |
| st.text("Chat history:") | |
| with open("chat.txt", "a+") as f: | |
| f.seek(0) | |
| chat_history = f.read() | |
| #st.text(chat_history) | |
| st.markdown(chat_history) | |
| countdown = st.empty() | |
| t = 60 | |
| while t: | |
| mins, secs = divmod(t, 60) | |
| countdown.text(f"Time remaining: {mins:02d}:{secs:02d}") | |
| time.sleep(1) | |
| t -= 1 | |
| if t == 0: | |
| countdown.text("Time's up!") | |
| with open("chat.txt", "a+") as f: | |
| f.seek(0) | |
| chat_history = f.read() | |
| #st.text(chat_history) | |
| st.markdown(chat_history) | |
| press_release() | |
| t = 60 | |
| if __name__ == "__main__": | |
| main() | |