app.py

from llama_index import Document, GPTListIndex, GPTSimpleVectorIndex
import gradio as gr
import openai
import os
from pytube import YouTube


def download_yt_video(ytlink):

    try:

        yt = YouTube(ytlink)

        video = yt.streams.filter(only_audio=True).first()

        out_file = video.download(output_path="./")

        base, ext = os.path.splitext(out_file)
        new_file = base + '.mp3'

        os.rename(out_file, new_file)

        return new_file
    except Exception as e:
        return e


def get_transcript(filename):
    import requests
    import json

    headers = {
        'accept': 'application/json',
        'x-gladia-key': '70ad5f6e-31e6-4acf-8a15-89c166c4cc9f',
        # requests won't add a boundary if this header is set when you pass files=
        # 'Content-Type': 'multipart/form-data',
    }

    files = {
        'audio': (filename, open(filename, 'rb'), 'audio/mpeg'),
        'language': (None, 'english'),
        'language_behaviour': (None, 'manual'),
        'output_format': (None, 'json'),
    }

    response = requests.post(
        'https://api.gladia.io/audio/text/audio-transcription/', headers=headers, files=files)

    data = json.loads(response.text)

    result = ""
    for dict_ in data['prediction']:
        result = result + dict_['transcription'] + " "

    result = ' '.join(result.strip().split())

    with open(f"{filename[:-4]}.txt", "w") as f:
        f.write(result)

    return result


def createindex(url, openaikey):

    try:
        filename = download_yt_video(url)

        transcript = get_transcript(filename)

        os.remove(filename)

        # Store openai key in environment
        os.environ['OPENAI_API_KEY'] = openaikey

        # Create index
        index = GPTListIndex([Document(transcript)], chunk_size_limit=2500)

        index_filename = "index.json"
        index.save_to_disk(index_filename)

        return "Video processed. Now you can start querying."
    except Exception as e:
        return e


def videoques(query, openaikey):

    # Basic Checks
    if not query:
        return "Please enter your query."

    # Basic Checks
    if not openaikey:
        return "Please enter openaikey."

    # Store openai key in environment
    os.environ['OPENAI_API_KEY'] = openaikey

    index_name = "index.json"

    index = GPTListIndex.load_from_disk(index_name)

    # Query based on index
    response = index.query(query, mode="embedding", similarity_top_k=4)

    return response


def cleartext(query, output):
    """
    Function to clear text
    """
    return ["", ""]


with gr.Blocks() as demo:
    gr.Markdown(
        """
    <h1><center><b>VideoQues</center></h1>
    
    """)
    gr.Markdown(
        """
    VideoQues answers your queries on any youtube video.
    
    """)
    with gr.Row():
        with gr.Column():
            url = gr.Textbox(lines=1, label="Enter Youtube Video link.")
            openaikey = gr.Textbox(lines=1, label="Enter Your OpenAI key.")
            submit1_button = gr.Button("Submit")
            ans1_output = gr.Textbox(label="Status.")
            clear1_button = gr.Button("Clear")
        with gr.Column():
            query = gr.Textbox(lines=2, label="Enter Your Query.")
            submit2_button = gr.Button("Submit")
            ans2_output = gr.Textbox(label="Answer.")
            clear2_button = gr.Button("Clear")

    # Submit button for showing YT Video thumbnail.
    submit1_button.click(createindex, inputs=[
                         url, openaikey], outputs=[ans1_output])

    # Submit button for submitting query.
    submit2_button.click(videoques, inputs=[
                         query, openaikey], outputs=[ans2_output])

    # Clear button for clearing query and answer.
    clear1_button.click(cleartext, inputs=[
                        url, ans1_output], outputs=[url, ans1_output])

    # Clear button for clearing query and answer.
    clear2_button.click(cleartext, inputs=[query, ans2_output], outputs=[
                        query, ans2_output])

demo.launch(debug=True)