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import openai
import os
from langchain.document_loaders import TextLoader, YoutubeLoader
#pytube, gradio, langchain, openai
import gradio as gr
from youtube_transcript_api import YouTubeTranscriptApi
from langchain.indexes import VectorstoreIndexCreator
os.environ["OPENAI_API_KEY"] = "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"
def get_video_id(url):
video_id = None
if 'youtu.be' in url:
video_id = url.split('/')[-1]
else:
video_id = url.split('watch?v=')[-1]
return video_id
def get_captions(url):
try:
video_id = get_video_id(url)
transcript_list = YouTubeTranscriptApi.list_transcripts(video_id)
transcript = transcript_list.find_transcript(['en'])
captions = transcript.fetch()
formatted_captions = ''
for caption in captions:
formatted_captions += caption['text'] + ' '
return formatted_captions
except Exception as e:
print(e)
return "Error. Could not fetch captions."
def answer_question(youtube_url, user_question):
# You can implement your logic here to process the video, transcribe it, and answer the user question.
# For now, let's return the user question as output.
f= open("temp.txt","w+")
f.write(get_captions(user_question))
f.close()
loader = TextLoader("temp.txt")
index = VectorstoreIndexCreator().from_loaders([loader])
os.remove("temp.txt")
query = user_question
answer = index.query(query)
return answer
iface = gr.Interface(
fn=answer_question,
inputs=[
gr.inputs.Textbox(lines=1, placeholder="Enter YouTube URL here..."),
gr.inputs.Textbox(lines=1, placeholder="Enter your question here...")
],
outputs=gr.outputs.Textbox(),
title="YouTube Video Question Answering",
description="Enter a YouTube URL and a question related to the video content. The app will return the answer if answer exists in the video."
)
if __name__ == "__main__":
iface.launch()
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