import streamlit as st
import pdfplumber
from transformers import pipeline
from gtts import gTTS
import os

# Function to extract text from PDF
def extract_text_from_pdf(pdf_file):
    text = ""
    try:
        with pdfplumber.open(pdf_file) as pdf:
            for page in pdf.pages:
                text += page.extract_text() or ""
    except Exception as e:
        st.error(f"Error reading the PDF: {e}")
    return text

# Function to generate discussion points with adjustable summary length
def generate_discussion_points(text, user_prompt=None):
    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
    
    # Optionally include the user's prompt to guide the summary
    if user_prompt:
        text = user_prompt + " " + text

    # Adjust the max_length and min_length for a longer summary
    summary = summarizer(text, max_length=600, min_length=200, do_sample=False)
    return summary[0]['summary_text']

# Function to convert text to speech
def text_to_speech(text, output_file="output.mp3"):
    tts = gTTS(text)
    tts.save(output_file)
    return output_file

# Streamlit app starts here
st.title("📄 PDF briefing Points Generator")
st.write("Upload a PDF file to generate briefing points and listen to them. Optionally, provide a prompt to guide the summary.")

# File uploader
uploaded_file = st.file_uploader("Upload a PDF", type=["pdf"])

# Input for user-defined summary prompt
user_prompt = st.text_area("Enter a prompt to guide the summary (optional)", height=100)

if uploaded_file:
    # Extract text from uploaded PDF
    with st.spinner("Extracting text from the uploaded PDF..."):
        pdf_text = extract_text_from_pdf(uploaded_file)

    if pdf_text.strip():
        st.write("### Extracted Text")
        st.text_area("Extracted Text", pdf_text, height=200)

        # Generate discussion points
        with st.spinner("Generating discussion points..."):
            try:
                discussion_points = generate_discussion_points(pdf_text, user_prompt)
                st.write("### Discussion Points")
                st.text_area("Discussion Points", discussion_points, height=150)
            except Exception as e:
                st.error(f"Error during summarization: {e}")

            # Convert discussion points to audio
            with st.spinner("Converting discussion points to audio..."):
                try:
                    audio_file = text_to_speech(discussion_points)
                    st.success("Audio file generated successfully!")
                except Exception as e:
                    st.error(f"Error during text-to-speech conversion: {e}")
                    audio_file = None

            if audio_file:
                # Audio playback
                st.write("### Listen to the Discussion Points")
                audio_bytes = open(audio_file, "rb").read()
                st.audio(audio_bytes, format="audio/mp3")

                # Option to download the audio file
                st.download_button(
                    label="Download Audio",
                    data=audio_bytes,
                    file_name="discussion_points.mp3",
                    mime="audio/mp3"
                )
    else:
        st.error("No text was extracted from the uploaded PDF. Please try with another file.")
else:
    st.info("Upload a PDF file to start.")