app.py

import os
import openai
import streamlit as st
import io
from pydub import AudioSegment
from youtube_transcript_api import YouTubeTranscriptApi
import PyPDF2
from pptx import Presentation

# Set OpenAI API key
openai.api_key = os.getenv('OPENAI_API_KEY')

# Function to transcribe audio using OpenAI Whisper
def transcribe_audio(audio_file):
    try:
        # Load audio file
        audio = AudioSegment.from_file(audio_file)
        # Convert to WAV
        buffer = io.BytesIO()
        audio.export(buffer, format="wav")
        buffer.seek(0)
        # Set name attribute
        buffer.name = "audio.wav"
        # Transcribe audio
        response = openai.Audio.transcribe(
            "whisper-1",
            file=buffer,
            response_format="verbose_json"
        )
        return response
    except Exception as e:
        st.error(f"Error during transcription: {str(e)}")
        return None

# Function to extract text from PDF and split it into chunks
def extract_text_from_pdf(pdf_file):
    reader = PyPDF2.PdfReader(io.BytesIO(pdf_file.read()))
    text = ""
    for page in reader.pages:
        text += page.extract_text() + "\n"
    
    # Split text into chunks of approximately 1500 words each
    words = text.split()
    chunks = [" ".join(words[i:i + 1500]) for i in range(0, len(words), 1500)]
    return chunks

# Function to get YouTube transcript
def get_youtube_transcript(url):
    try:
        # Extract video ID from URL
        if "watch?v=" in url:
            video_id = url.split("watch?v=")[1].split("&")[0]
        elif "youtu.be/" in url:
            video_id = url.split("youtu.be/")[1].split("?")[0]
        else:
            st.error("Invalid YouTube URL.")
            return None
        # Fetch transcript
        transcript_list = YouTubeTranscriptApi.list_transcripts(video_id)
        # Choose a transcript
        transcript = transcript_list.find_transcript(['en'])
        # Fetch the actual transcript data
        transcript_data = transcript.fetch()
        # Combine transcript texts
        transcription_text = "\n".join([entry['text'] for entry in transcript_data])
        return transcription_text
    except Exception as e:
        st.error(f"Error fetching YouTube transcript: {str(e)}")
        return None

# Function to generate notes for each chunk
def generate_notes(text):
    prompt = f"Create comprehensive notes in bullet points from the following text:\n\n{text}"
    response = openai.ChatCompletion.create(
        model='gpt-3.5-turbo',
        messages=[{'role': 'user', 'content': prompt}],
        max_tokens=1000,
    )
    return response.choices[0].message.content.strip()

# Function to generate additional sections
def generate_section(title, text):
    prompt = f"Generate a section titled '{title}' with 3-6 sentences based on the following text:\n\n{text}"
    response = openai.ChatCompletion.create(
        model='gpt-3.5-turbo',
        messages=[{'role': 'user', 'content': prompt}],
        max_tokens=500,
    )
    return response.choices[0].message.content.strip()

# Function to create PowerPoint presentation
def create_presentation(summary, key_concepts, key_takeaways, case_studies, glossary, faqs):
    prs = Presentation()

    # Add title slide
    slide = prs.slides.add_slide(prs.slide_layouts[0])
    title = slide.shapes.title
    subtitle = slide.placeholders[1]
    title.text = "Lecture Notes"
    subtitle.text = "Generated by AI Notes Generation Bot"

    # Add slides for each section
    def add_slide(title, content):
        slide = prs.slides.add_slide(prs.slide_layouts[1])
        slide.shapes.title.text = title
        textbox = slide.placeholders[1]
        textbox.text = content

    add_slide("Summary", summary)
    add_slide("Key Concepts", key_concepts)
    add_slide("Key Takeaways", key_takeaways)
    add_slide("Case Studies/Examples", case_studies)
    add_slide("Glossary", glossary)
    add_slide("FAQs", faqs)

    # Save the presentation to a BytesIO object
    ppt_buffer = io.BytesIO()
    prs.save(ppt_buffer)
    ppt_buffer.seek(0)
    return ppt_buffer

# Main app
def main():
    st.set_page_config(layout="wide")

    # Add custom CSS for gradient background
    st.markdown("""
        <style>
        .stApp {
            background: linear-gradient(180deg, 
                rgba(64,224,208,0.7) 0%, 
                rgba(32,112,104,0.4) 35%, 
                rgba(0,0,0,0) 100%
            );
        }
        .css-1d391kg {
            background: none;
        }
        .stMarkdown {
            color: #ffffff;
        }
        .css-1y4p8pa {
            max-width: 100%;
            padding: 2rem;
        }
        div[data-testid="stSidebarContent"] {
            background-color: rgba(255,255,255,0.1);
        }
        .stTextArea textarea {
            background-color: #000000 !important;
            color: #ffffff !important;
        }
        .stButton button {
            background-color: #40E0D0;
            color: black;
        }
        .stButton button:hover {
            background-color: #48D1CC;
            color: black;
        }
        h1, h2, h3, h4, h5, h6 {
            color: white !important;
        }
        .css-184tjsw p {
            color: white !important;
        }
        .stTextInput input {
            color: white !important;
            background-color: rgba(0, 0, 0, 0.5) !important;
        }
        </style>
    """, unsafe_allow_html=True)

    st.markdown("<h1 style='text-align: center;'>AI Notes Generation Bot 🤖</h1>", unsafe_allow_html=True)

    # Left sidebar for upload options
    st.sidebar.header("Upload your file:")
    input_type = st.sidebar.selectbox("Select Input Type", ["Audio File", "PDF Document", "YouTube URL"])
    st.sidebar.markdown("### Steps to Use the Tool:")
    st.sidebar.markdown("1. Upload an audio file (25MB max), PDF, or YouTube URL for notes.")
    st.sidebar.markdown("2. Click on 'Generate Notes' to get AI-generated notes.")
    st.sidebar.markdown("3. Use the 'Download Presentation' button to save your notes locally.")
    # File uploader or URL input based on selected type
    audio_input = pdf_input = youtube_input = None
    if input_type == "Audio File":
        audio_input = st.sidebar.file_uploader("Upload audio file", type=["mp3", "wav"], key="audio", help="Supports mp3 and wav formats up to 25MB")
    elif input_type == "PDF Document":
        pdf_input = st.sidebar.file_uploader("Upload PDF document", type=["pdf"], key="pdf", help="")
    elif input_type == "YouTube URL":
        youtube_input = st.sidebar.text_input("Enter YouTube URL (must have subtitles enabled)", key="youtube")

    # Place Generate Notes button in the sidebar
    if st.sidebar.button("Generate Notes", key="generate_notes"):
        transcription_text = ""
        if input_type == "Audio File" and audio_input:
            transcription = transcribe_audio(audio_input)
            if transcription:
                transcription_text = "\n".join([seg['text'] for seg in transcription['segments']])
            else:
                st.error("Transcription failed.")
        elif input_type == "PDF Document" and pdf_input:
            chunks = extract_text_from_pdf(pdf_input)
            summaries = [generate_notes(chunk) for chunk in chunks]
            transcription_text = "\n".join(summaries)
        elif input_type == "YouTube URL" and youtube_input:
            transcription_text = get_youtube_transcript(youtube_input)
            if not transcription_text:
                st.error("Failed to retrieve YouTube transcript.")
        else:
            st.error("Please provide valid input.")

        if transcription_text:
            st.session_state['summary'] = transcription_text

    # Display generated notes
    if 'summary' in st.session_state:
        st.markdown("---")
        st.subheader("Generated Notes")
        summary = st.session_state['summary']
        
        st.markdown("### Summary:")
        st.markdown(f"<div style='background-color: black; color: white; padding: 10px; border-radius: 5px;'>" +
                    f"<p>{summary}</p>" +
                    "</div>", unsafe_allow_html=True)

        st.markdown("### Key Concepts:")
        key_concepts = generate_section("Key Concepts", summary)
        st.markdown(f"<div style='background-color: black; color: white; padding: 10px; border-radius: 5px;'>" +
                    f"<p>{key_concepts}</p>" +
                    "</div>", unsafe_allow_html=True)

        st.markdown("### Key Takeaways:")
        key_takeaways = generate_section("Key Takeaways", summary)
        st.markdown(f"<div style='background-color: black; color: white; padding: 10px; border-radius: 5px;'>" +
                    f"<p>{key_takeaways}</p>" +
                    "</div>", unsafe_allow_html=True)

        case_studies = generate_section("Case Studies/Examples", summary)
        st.markdown(f"<div style='background-color: black; color: white; padding: 10px; border-radius: 5px;'>"
                    f"<p>{case_studies}</p>"
                    "</div>", unsafe_allow_html=True)

        st.markdown("### Glossary:")
        glossary = generate_section("Glossary", summary)
        st.markdown(f"<div style='background-color: black; color: white; padding: 10px; border-radius: 5px;'>"
                    f"<p>{glossary}</p>"
                    "</div>", unsafe_allow_html=True)

        st.markdown("### FAQs:")
        faqs = generate_section("FAQs", summary)
        st.markdown(f"<div style='background-color: black; color: white; padding: 10px; border-radius: 5px;'>"
                    f"<p>{faqs}</p>"
                    "</div>", unsafe_allow_html=True)

        # Option to download the PowerPoint presentation
        ppt_buffer = create_presentation(summary, key_concepts, key_takeaways, case_studies, glossary, faqs)
        st.download_button(
            label="Download Presentation",
            data=ppt_buffer,
            file_name="Lecture_Notes_Presentation.pptx",
            mime="application/vnd.openxmlformats-officedocument.presentationml.presentation"
        )

if __name__ == "__main__":
    main()