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Runtime error
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import torch, os, argparse, shutil, textwrap, time, streamlit as st
from langchain.document_loaders import YoutubeLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings, HuggingFaceBgeEmbeddings
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain import HuggingFaceHub
from transformers import pipeline
from deep_translator import GoogleTranslator
from langdetect import detect
from urllib.parse import urlparse, parse_qs
def typewriter(text, speed):
container = st.empty()
displayed_text = ''
for char in text:
displayed_text += char
container.markdown(displayed_text)
time.sleep(1 / speed)
def wrap_text_preserve_newlines(text, width=110):
lines = text.split('\n')
wrapped_lines = [textwrap.fill(line, width=width) for line in lines]
wrapped_text = '\n'.join(wrapped_lines)
return wrapped_text
def process_llm_response(llm_originalresponse2):
typewriter(llm_originalresponse2['result'], speed=40)
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:
print(f"Error extracting video ID: {e}")
return None
def chat():
HF_TOKEN = os.environ.get('HF_TOKEN', False)
model_name = "BAAI/bge-base-en"
encode_kwargs = {'normalize_embeddings': True}
st.title('YouTube ChatBot')
video_url = st.text_input('Insert video URL', placeholder='Format should be like: https://www.youtube.com/watch?v=pSLeYvld8Mk')
query = st.text_input("Ask any question about the video")
if st.button('Submit', type='primary'):
with st.spinner('Processing the video...'):
video_id = extract_video_id(video_url)
loader = YoutubeLoader(video_id)
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
documents = text_splitter.split_documents(documents)
vector_db = 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)
)
repo_id = "tiiuae/falcon-7b-instruct"
qa_chain = RetrievalQA.from_chain_type(
llm=HuggingFaceHub(
huggingfacehub_api_token=HF_TOKEN,
repo_id=repo_id,
model_kwargs={'temperature': 0.1, 'max_new_tokens': 1000},
),
retriever=vector_db.as_retriever(),
return_source_documents=False,
verbose=False
)
with st.spinner('Generating Answer...'):
llm_response = qa_chain(query)
process_llm_response(llm_response)
chat() |