Spaces:
Running
Running
File size: 5,082 Bytes
5b412f0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 |
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) |