ai_vision.py

import streamlit as st

import pandas as pd
import time
from selenium import webdriver
from webdriver_manager.chrome import ChromeDriverManager
from selenium.webdriver.common.by import By
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.chrome.options import Options
import matplotlib.pyplot as plt
import os
import logging
import spacy
import re   #regular expression library to cleanup the text
import networkx as nx #for network analysis
from nltk.tokenize import word_tokenize, sent_tokenize
import nltk
import os
from spacy import displacy
import numpy as np
from spacy import displacy
import networkx as nx
import matplotlib.pyplot as plt

#AI vision page


#@st.cache_data
def load_data():
    NER = spacy.load('en_core_web_md')
    with open('pride.txt', 'r', encoding='utf8') as rf:
        text = rf.read()

    # Extract the desired text portion
    text = text[text.find("Chapter 61")+len("Chapter 61"):text.find("End of the Project Gutenberg EBook")].strip()
    text = re.sub(r'\n {6}', ' ', text)
    text = text.replace(".", "")

    return NER, text

# Load data and NER model
NER, text = load_data()

# Function to display NER visualization
def read_AI(NER, text):
    # st.title("Read 'Pride and Prejudice' in AI Vision")
    # st.write("##### Named Entity Recognition in Natural Language Processing (NLP)")
    # for _ in range(2):  # You can adjust the range for more or less space
    #     st.write("")  

    # Pagination
    #chapter_index = st.selectbox("Select Chapter", range(1, len(chapters) + 1)) - 1
    #selected_chapter = chapters[chapter_index]

    # Process the text with the NER model
    doc = NER(text[0:3000])  # Limiting to the first 2000 characters for demonstration
    #doc1 = NER(selected_chapter) 

    # Use displaCy to render the entities within the Streamlit app
    html = displacy.render(doc, style="ent")
    #html = displacy.render(doc1, style="ent")

    
    # Streamlit components to display HTML content
    st.write(html, unsafe_allow_html=True)