Spaces:
Running
Running
| import streamlit as st | |
| hidden_style = """ | |
| <style> | |
| #MainMenu {visibility: hidden;} | |
| footer {visibility: hidden;} | |
| </style> | |
| """ | |
| st.markdown(hidden_style, unsafe_allow_html=True) | |
| def basic_version(): | |
| import argparse | |
| import os | |
| import shutil | |
| import time | |
| import torch | |
| import textwrap | |
| from urllib.parse import urlparse, parse_qs | |
| from dotenv import load_dotenv | |
| from langdetect import detect | |
| from deep_translator import GoogleTranslator | |
| from transformers import pipeline | |
| import streamlit as st | |
| from langchain import HuggingFaceHub | |
| from langchain.chains import RetrievalQA | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.document_loaders import YoutubeLoader | |
| from langchain.embeddings import HuggingFaceBgeEmbeddings | |
| from langchain.embeddings import OpenAIEmbeddings | |
| from langchain.embeddings import HuggingFaceInstructEmbeddings | |
| from langchain.llms import OpenAI | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.vectorstores import Chroma | |
| load_dotenv() | |
| def text_writer(input_text: str, speed: float): | |
| container = st.empty() | |
| displayed_text = "" | |
| for char in input_text: | |
| displayed_text += char | |
| container.markdown(displayed_text) | |
| time.sleep(1/speed) | |
| def wrap_text_keep_newlines(input_text, width=110): | |
| lines = input_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_response(original_response): | |
| text_writer(original_response["result"], speed=40) | |
| def get_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 start_basic_version(): | |
| HUGGINGFACE_API_TOKEN = os.environ["HUGGINGFACE_API_TOKEN"] | |
| model_name = "BAAI/bge-base-en" | |
| encode_kwargs = {'normalize_embeddings': True} | |
| st.title('Chat with Youtube 🎬🤖') | |
| st.markdown(""" Using AI to interact with Youtube! """) | |
| video_url = st.text_input("Insert The video URL", placeholder="Format should be like: https://www.youtube.com/watch?v=pSLeYvld8Mk") | |
| query = st.text_input("Ask any question about the video",help="Suggested queries: Summarize the key points of this video - What is this video about - Ask about a specific thing in the video ") | |
| st.warning("⚠️ Please Keep in mind that the accuracy of the response relies on the :red[Video's quality] and the :red[prompt's Quality]. Occasionally, the response may not be entirely accurate. Consider using the response as a reference rather than a definitive answer.") | |
| if st.button("Submit Question", type="primary"): | |
| with st.spinner('Processing the Video...'): | |
| video_id = get_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) | |
| if os.path.exists('./data'): | |
| shutil.rmtree('./data') | |
| 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=HUGGINGFACE_API_TOKEN, | |
| repo_id=repo_id, | |
| model_kwargs={"temperature":0.2, "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_response(llm_response) | |
| start_basic_version() | |
| basic_version() | |
| st.sidebar.markdown("## Chat with Youtube using AI 🎬🤖") | |
| st.sidebar.markdown("""Built by <a href="https://github.com/Ahmet-Dedeler"> Ahmet </a> & <a href="https://github.com/arhaamwanii"> Arhaam </a> for MLH All in Open Source Hackathon.""", unsafe_allow_html=True) | |
| st.sidebar.markdown('<a href="https://github.com/Ahmet-Dedeler/Chat-With-Youtube_All-In-Hackathon"> Check out the project on GitHub <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/github/github-original.svg" alt="GitHub" width="30" height="30"></a>', unsafe_allow_html=True) |