Text_summarizer/TextAnalyzer.py at main · 0layiw0la/Text_summarizer · GitHub

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import nltk
from transformers import pipeline
import streamlit as st
import docx
from PyPDF2 import PdfReader
import streamlit as st
from textly import load_model, sentiment, summarize, extract_text_from_txt, extract_text_from_docx, extract_text_from_pdf

positive_style = "color: green; font-weight: bold;"
negative_style = "color: red; font-weight: bold;"

def main():
    st.title('Text Summarizer App')

    # Load the model when the page is loaded
    summarizer = load_model()

    user_input = st.text_area('Enter text here')
    uploaded_file = st.file_uploader("Choose a file", type=["txt", "docx", "pdf"])

    if uploaded_file is not None:
        file_extension = uploaded_file.name.split(".")[-1]

        if file_extension == "txt":
            text = extract_text_from_txt(uploaded_file)
        elif file_extension == "docx":
            text = extract_text_from_docx(uploaded_file)
        elif file_extension == "pdf":
            text = extract_text_from_pdf(uploaded_file)

    # Button to trigger summarization
    if st.button("Generate Summary"):
        if user_input:
            summary = summarize(user_input, summarizer, max_value=350, min_value=90)
            st.subheader("Generated Summary:")
            st.write(summary)
        elif uploaded_file is not None:
            summary = summarize(text, summarizer, max_value=350, min_value=80)
            st.write(summary)

    # Button to trigger sentiment analysis
    if st.button("Perform Sentiment Analysis"):
        sentiment_result = sentiment(user_input)
        if sentiment_result == 'Positive':
            style = positive_style
        elif sentiment_result == "Negative":
            style = negative_style
        else:
            style = ""

        st.subheader("Sentiment Analysis Result:")
        st.markdown(f"**Sentiment:** <span style='{style}'>{sentiment_result}</span>", unsafe_allow_html=True)

if __name__ == '__main__':
    main()
@st.cache_resource
def load_model():
    return pipeline('summarization')

def extract_text_from_txt(file):
    text = str(file.read())
    return text

def extract_text_from_docx(file):
    doc = docx.Document(file)
    full_text = ""
    for para in doc.paragraphs:
        full_text += para.text + "\n"
    return full_text

def extract_text_from_pdf(file):
    pdf_reader = PdfReader(file)
    full_text = ""
    for page_num in range(len(pdf_reader.pages)):
        page = pdf_reader.pages[page_num]
        full_text += page.extract_text() + "\n"
    return full_text

def summarize(text, summarizer, max_value=350, min_value=50):
    text = text.replace('.', '.<eos>')
    text = text.replace('?', '?<eos>')
    text = text.replace('!', '!<eos>')
    max_chunk = 600
    sentences = text.split('<eos>')
    current_chunk = 0
    chunks = []
    for sentence in sentences:
        if len(chunks) == current_chunk + 1:
            if len(chunks[current_chunk]) + len(sentence.split(' ')) < max_chunk:
                chunks[current_chunk].extend(sentence.split(' '))
            else:
                current_chunk += 1
                chunks.append(sentence.split(' '))
        else:
            chunks.append(sentence.split(' '))
    for chunk_id in range(len(chunks)):
        chunks[chunk_id] = ' '.join(chunks[chunk_id])
    summary = []
    for i in chunks:
        sumr = summarizer(i, max_length=max_value, min_length=min_value, do_sample=False)[0]['summary_text']
        summary.append(sumr)
    return ' '.join(summary)

def sentiment(text):
    nltk.download('vader_lexicon')
    from nltk.sentiment import SentimentIntensityAnalyzer
    text = str(text)
    sia = SentimentIntensityAnalyzer()
    sent = sia.polarity_scores(text)
    if sent['pos'] > 0.15 and (sent['pos'] > sent['neg']):
        sentiment_label = 'Positive'
    elif (sent['neg'] > 0.15) and (sent['neg'] > sent['pos']):
        sentiment_label = 'Negative'
    elif sent['neu'] > 0.8:
        sentiment_label = 'Neutral'
    else:
        sentiment_label = 'Neutral'
    return sentiment_label