app.py

import streamlit as st
import requests
import nltk
from transformers import pipeline
from rake_nltk import Rake
from nltk.corpus import stopwords
from fuzzywuzzy import fuzz

st.title("Exploring Torch, Transformers, Rake, and Others analyzing Text")

# Define the options for the dropdown menu, Selecting a remote txt file already created to analyze the text
options = ['Apprecitation Letter', 'Regret Letter', 'Kindness Tale', 'Lost Melody Tale', 'Twitter Example 1', 'Twitter Example 2']

# Create a dropdown menu to select options
selected_option = st.selectbox("Select a preset option", options)

# Define URLs for different options
url_option1 = "https://raw.githubusercontent.com/peteciank/public_files/main/Transformers/Appreciation_Letter.txt"
url_option2 = "https://raw.githubusercontent.com/peteciank/public_files/main/Transformers/Regret_Letter.txt"
url_option3 = "https://raw.githubusercontent.com/peteciank/public_files/main/Transformers/Kindness_Tale.txt"
url_option4 = "https://raw.githubusercontent.com/peteciank/public_files/main/Transformers/Lost_Melody_Tale.txt"
url_option5 = "https://raw.githubusercontent.com/peteciank/public_files/main/Transformers/Twitter_Example_1.txt"
url_option6 = "https://raw.githubusercontent.com/peteciank/public_files/main/Transformers/Twitter_Example_2.txt"

# Function to fetch text content based on selected option
def fetch_text_content(selected_option):
    if selected_option == 'Apprecitation Letter':
        return requests.get(url_option1).text
    elif selected_option == 'Regret Letter':
        return requests.get(url_option2).text
    elif selected_option == 'Kindness Tale':
        return requests.get(url_option3).text
    elif selected_option == 'Lost Melody Tale':
        return requests.get(url_option4).text
    elif selected_option == 'Twitter Example 1':
        return requests.get(url_option5).text
    elif selected_option == 'Twitter Example 2':
        return requests.get(url_option6).text
    else:
        return ""

# Fetch text content based on selected option
text_content = fetch_text_content(selected_option)

# Display text content in a text area
jd = st.text_area("Text File Content", text_content)


# Download NLTK resources
nltk.download('punkt')
nltk.download('stopwords')

# Initialize pipeline for sentiment analysis
pipe_sent = pipeline('sentiment-analysis')
# Initialize pipeline for summarization
pipe_summ = pipeline("summarization", model="facebook/bart-large-cnn")

# Function to extract keywords and remove duplicates
def extract_keywords(text):
    r = Rake()
    r.extract_keywords_from_text(text)
    # Get all phrases scored
    phrases_with_scores = r.get_ranked_phrases_with_scores()
    # Filter out stopwords
    stop_words = set(stopwords.words('english'))
    keywords = []
    for score, phrase in phrases_with_scores:
        # Check if the phrase is not a stopword and add to the list
        if phrase.lower() not in stop_words:
            keywords.append((score, phrase))
    # Sort keywords by score in descending order
    keywords.sort(key=lambda x: x[0], reverse=True)
    # Remove duplicates and merge similar keywords
    unique_keywords = []
    seen_phrases = set()
    for score, phrase in keywords:
        if phrase not in seen_phrases:
            # Check if the phrase is similar to any of the seen phrases
            similar_phrases = [seen_phrase for seen_phrase in seen_phrases if fuzz.ratio(phrase, seen_phrase) > 70]
            if similar_phrases:
                # If similar phrases are found, merge them into one phrase
                merged_phrase = max([phrase] + similar_phrases, key=len)
                unique_keywords.append((score, merged_phrase))
            else:
                unique_keywords.append((score, phrase))
            seen_phrases.add(phrase)
    return unique_keywords[:10]  # Return only the first 10 keywords

text = st.text_area('Enter the text to analyze', jd)

if text:

    with st.expander("Sentiment Analysis", expanded=True):
        # Sentiment analysis
        out_sentiment = pipe_sent(text)
        # Display sentiment analysis result
        sentiment_score = out_sentiment[0]['score']
        sentiment_label = out_sentiment[0]['label']
        sentiment_emoji = '😊' if sentiment_label == 'POSITIVE' else '😞'
        sentiment_text = f"Sentiment Score: {sentiment_score}, Sentiment Label: {sentiment_label.capitalize()} {sentiment_emoji}"
        st.write(sentiment_text)
        st.write("✅ Completed")
        
    with st.expander("Summarization", expanded=True):        
        # Summarization
        out_summ = pipe_summ(text)
        summarized_text = out_summ[0]['summary_text']
        st.write(summarized_text)
        st.write("✅ Completed")

    with st.expander("Keywords Extraction", expanded=True):
        # Keyword extraction
        keywords = extract_keywords(text)
        keyword_list = [keyword[1] for keyword in keywords]
        st.write(keyword_list)
        st.write("✅ Completed")