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import gradio as gr
import torch
import os
from langchain.document_loaders import YoutubeLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceBgeEmbeddings
from langchain.chains import RetrievalQA
from langchain import HuggingFaceHub
from urllib.parse import urlparse, parse_qs
def extract_video_id(youtube_url):
try:
parsed_url = urlparse(youtube_url)
query_params = parse_qs(parsed_url.query)
video_id = query_params.get('v', [None])[0]
return video_id
except Exception as e:
return f"Error extracting video ID: {e}"
def process_video(youtube_url, question):
video_id = extract_video_id(youtube_url)
if not video_id:
return 'Invalid YouTube URL'
try:
# Initialize the YouTube Loader
loader = YoutubeLoader(video_id)
documents = loader.load()
# Process the documents
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
documents = text_splitter.split_documents(documents)
# Initialize Vector Store
model_name = "BAAI/bge-base-en"
encode_kwargs = {'normalize_embeddings': True}
vectordb = Chroma.from_documents(
documents,
embedding=HuggingFaceBgeEmbeddings(model_name=model_name,
model_kwargs={'device': 'cuda' if torch.cuda.is_available() else 'cpu'},
encode_kwargs=encode_kwargs)
)
# Setup the QA Chain
HUGGINGFACE_API_TOKEN = os.environ['HUGGINGFACE_API_TOKEN']
repo_id = "tiiuae/falcon-7b-instruct"
qa_chain = RetrievalQA.from_chain_type(
llm=HuggingFaceHub(huggingfacehub_api_token=HUGGINGFACE_API_TOKEN,
repo_id=repo_id,
model_kwargs={"temperature":0.1, "max_new_tokens":1000}),
retriever=vectordb.as_retriever(),
return_source_documents=False,
verbose=False
)
# Process the question
llm_response = qa_chain(question)
return llm_response['result']
except Exception as e:
return f"Error processing video: {e}"
iface = gr.Interface(
fn=process_video,
inputs=["text", "text"],
outputs="text",
title="YouTube Video AI Assistant",
description="Enter a YouTube URL and a question to get AI-generated answers based on the video."
)
if __name__ == "__main__":
iface.launch(share=True)
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