import openai
import streamlit as st
from youtube_transcript_api import YouTubeTranscriptApi
from langchain.text_splitter import RecursiveCharacterTextSplitter

def get_transcript(video_id):
    transcript_list = YouTubeTranscriptApi.list_transcripts(video_id)

    # Try fetching the manual transcript
    try:
        transcript = transcript_list.find_manually_created_transcript()
        language_code = transcript.language_code  # Save the detected language
    except:
        # If no manual transcript is found, try fetching an auto-generated transcript in a supported language
        try:
            generated_transcripts = [trans for trans in transcript_list if trans.is_generated]
            transcript = generated_transcripts[0]
            language_code = transcript.language_code  # Save the detected language
        except:
            # If no auto-generated transcript is found, raise an exception
            raise Exception("No suitable transcript found.")

    full_transcript = " ".join([part['text'] for part in transcript.fetch()])
    return full_transcript, language_code  # Return both the transcript and detected language

def summarize_with_langchain_and_openai(transcript, language_code, openai_api_key, model_name='gpt-3.5-turbo'):
    # Set the OpenAI API key
    openai.api_key = openai_api_key

    # Split the document if it's too long
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=0)
    texts = text_splitter.split_text(transcript)
    text_to_summarize = " ".join(texts[:4])  # Adjust this as needed

    # Prepare the prompt for summarization
    system_prompt = 'I want you to act as a Life Coach that can create good summaries!'
    prompt = f'''Summarize the following text in {language_code}.
    Text: {text_to_summarize}

    Add a title to the summary in {language_code}. 
    Include an INTRODUCTION, BULLET POINTS if possible, and a CONCLUSION in {language_code}.'''

    # Start summarizing using OpenAI
    response = openai.ChatCompletion.create(
        model=model_name,
        messages=[
            {'role': 'system', 'content': system_prompt},
            {'role': 'user', 'content': prompt}
        ],
        temperature=1
    )

    return response['choices'][0]['message']['content']

def main():
    st.title('YouTube Video Summarizer')
    
    openai_api_key = st.text_input('Enter your OpenAI API Key:', type='password')
    video_id = st.text_input('Enter the YouTube Video ID:')
    
    if st.button('Start'):
        if openai_api_key and video_id:
            try:
                progress = st.progress(0)
                status_text = st.empty()

                status_text.text('Loading the transcript...')
                progress.progress(25)

                # Getting both the transcript and language_code
                transcript, language_code = get_transcript(video_id)

                status_text.text(f'Creating summary...')
                progress.progress(75)

                summary = summarize_with_langchain_and_openai(transcript, language_code, openai_api_key)

                status_text.text('Summary:')
                st.markdown(summary)
                progress.progress(100)
            except Exception as e:
                st.write(str(e))
        else:
            st.write('Please enter both your OpenAI API Key and YouTube Video ID.')

if __name__ == "__main__":
    main()