backup.app.py

#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()