import streamlit as st
from transformers import pipeline
from heapq import nlargest

# Function to extract text from SRT-formatted text
def extract_text_from_srt_text(srt_text):
    lines = srt_text.strip().split("\n\n")  # Split by empty lines to separate subtitles
    texts = [subtitle.split("\n")[2] for subtitle in lines if subtitle.strip()]  # Extract text from the third line of each subtitle
    return " ".join(texts)

# Function to generate summary from text
def generate_summary(text, summary_length):
    summarizer = pipeline("summarization")
    summary = summarizer(text, max_length=summary_length, min_length=30, do_sample=False)
    return summary[0]["summary_text"]

# Streamlit app
st.title("SRT Summarization")

# Text area for user to input SRT-formatted text
srt_text_input = st.text_area("Paste SRT-formatted text here:")

# Button to trigger summarization
if st.button("Summarize"):
    # Check if text area is not empty
    if srt_text_input.strip():
        # Extract text from SRT-formatted text
        text_to_summarize = extract_text_from_srt_text(srt_text_input)
        # Generate summary
        summary = generate_summary(text_to_summarize, 150)  # You can adjust the summary length as needed
        # Extract top 4 sentences
        sentences = text_to_summarize.split(". ")
        top_sentences = nlargest(4, sentences, key=len)
        top_subjects = "\n".join(top_sentences)
        # Display summary and top 4 subjects
        st.subheader("Summary:")
        st.write(summary)
        st.subheader("Top 4 Subjects:")
        st.write(top_subjects)
    else:
        st.warning("Please enter some SRT-formatted text.")