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Runtime error
| 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() |