import streamlit as st
import io
import PyPDF2
from transformers import pipeline
from gtts import gTTS

# Function to extract text from a PDF
def extract_text_from_pdf(pdf_file):
    pdf_stream = io.BytesIO(pdf_file.read())
    pdf_reader = PyPDF2.PdfReader(pdf_stream)
    text = ""
    for page in pdf_reader.pages:
        text += page.extract_text() or ""  # Handle None for non-text pages
    return text

# Function to generate discussion points
def generate_discussion_points(text):
    summarizer = pipeline('summarization')
    summary = summarizer(text, max_length=600, min_length=300, do_sample=False)
    return summary[0]['summary_text']

# Function to convert text to speech
def text_to_speech(text):
    tts = gTTS(text=text, lang='en')
    tts.save("discussion_points.mp3")

# Streamlit app
st.title("PDF Analysis and Discussion Generator")
uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])

if uploaded_file is not None:
    # Extract text from the uploaded PDF
    text = extract_text_from_pdf(uploaded_file)
    st.subheader("Extracted Text")
    st.write(text)

    # Generate and display discussion points
    st.subheader("Generated Discussion Points")
    discussion_points = generate_discussion_points(text)
    st.write(discussion_points)

    # Convert discussion points to audio and play it
    text_to_speech(discussion_points)
    audio_file = open("discussion_points.mp3", "rb")
    audio_bytes = audio_file.read()
    st.audio(audio_bytes, format="audio/mp3")