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| import streamlit as st | |
| hide_streamlit_style = """ | |
| <style> | |
| #MainMenu {visibility: hidden;} | |
| footer {visibility: hidden;} | |
| </style> | |
| """ | |
| st.markdown(hide_streamlit_style, unsafe_allow_html=True) | |
| def paid_version(): | |
| import os | |
| import argparse | |
| import shutil | |
| from langchain.document_loaders import YoutubeLoader | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.vectorstores import Chroma | |
| from langchain.embeddings import OpenAIEmbeddings | |
| from langchain.chains import RetrievalQA | |
| from langchain.llms import OpenAI | |
| import streamlit as st | |
| from langchain.chat_models import ChatOpenAI | |
| 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: | |
| print(f"Error extracting video ID: {e}") | |
| return None | |
| def set_openAi_api_key(api_key: str): | |
| st.session_state["OPENAI_API_KEY"] = api_key | |
| os.environ['OPENAI_API_KEY'] = api_key | |
| def openai_api_insert_component(): | |
| with st.sidebar: | |
| st.markdown( | |
| """ | |
| ## Quick Guide 🚀 | |
| 1. Get started by adding your [OpenAI API key](https://platform.openai.com/account/api-keys) below🔑 | |
| 2. Easily input the video url | |
| 3. Engage with the content - ask questions, seek answers💬 | |
| """ | |
| ) | |
| api_key_input = st.text_input("Input your OpenAI API Key", | |
| type="password", | |
| placeholder="Format: sk-...", | |
| help="You can get your API key from https://platform.openai.com/account/api-keys.") | |
| if api_key_input == "" or api_key_input is None: | |
| st.sidebar.caption("👆 :red[Please set your OpenAI API Key here]") | |
| st.caption(":green[Your API is not stored anywhere. It is only used to generate answers to your questions.]") | |
| set_openAi_api_key(api_key_input) | |
| def launchpaidversion(): | |
| openai_api_insert_component() | |
| os.environ['OPENAI_API_KEY'] = st.session_state['OPENAI_API_KEY'] | |
| st.title('MKG: Your Chat with Youtube Assistant') | |
| videourl = st.text_input("Insert The video URL") | |
| query = st.text_input("Ask any question about the video") | |
| if st.button("Submit Question", type="primary"): | |
| video_id = extract_video_id(videourl) | |
| loader = YoutubeLoader(video_id) | |
| documents = loader.load() | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | |
| documents = text_splitter.split_documents(documents) | |
| shutil.rmtree('./data') | |
| vectordb = Chroma.from_documents( | |
| documents, | |
| embedding=OpenAIEmbeddings(), | |
| persist_directory='./data' | |
| ) | |
| vectordb.persist() | |
| qa_chain = RetrievalQA.from_chain_type( | |
| llm=ChatOpenAI(model_name='gpt-3.5-turbo'), | |
| retriever=vectordb.as_retriever(), | |
| return_source_documents=True, | |
| verbose=False | |
| ) | |
| response = qa_chain(query) | |
| st.write(response) | |
| launchpaidversion() | |
| def free_version(): | |
| import torch | |
| import os | |
| import argparse | |
| import shutil | |
| from langchain.document_loaders import YoutubeLoader | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.vectorstores import Chroma | |
| from langchain.embeddings import OpenAIEmbeddings | |
| from langchain.chains import RetrievalQA | |
| from langchain.llms import OpenAI | |
| import streamlit as st | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain import HuggingFaceHub | |
| from langchain.embeddings import HuggingFaceInstructEmbeddings | |
| from urllib.parse import urlparse, parse_qs | |
| from langchain.embeddings import HuggingFaceBgeEmbeddings | |
| from transformers import pipeline | |
| import textwrap | |
| import time | |
| from deep_translator import GoogleTranslator | |
| from langdetect import detect | |
| def typewriter(text: str, speed: float): | |
| 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): | |
| # Split the input text into lines based on newline characters | |
| lines = text.split('\n') | |
| # Wrap each line individually | |
| wrapped_lines = [textwrap.fill(line, width=width) for line in lines] | |
| # Join the wrapped lines back together using newline characters | |
| wrapped_text = '\n'.join(wrapped_lines) | |
| return wrapped_text | |
| def process_llm_response(llm_originalresponse2): | |
| #result_text = wrap_text_preserve_newlines(llm_originalresponse2["result"]) | |
| 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 set_openAi_api_key(api_key: str): | |
| st.session_state["OPENAI_API_KEY"] = api_key | |
| os.environ['OPENAI_API_KEY'] = api_key | |
| def openai_api_insert_component(): | |
| with st.sidebar: | |
| st.markdown( | |
| """ | |
| ## Quick Guide 🚀 | |
| 1. Get started by adding your [OpenAI API key](https://platform.openai.com/account/api-keys) below🔑 | |
| 2. Easily input the video url | |
| 3. Engage with the content - ask questions, seek answers💬 | |
| """ | |
| ) | |
| api_key_input = st.text_input("Input your OpenAI API Key", | |
| type="password", | |
| placeholder="Format: sk-...", | |
| help="You can get your API key from https://platform.openai.com/account/api-keys.") | |
| if api_key_input == "" or api_key_input is None: | |
| st.sidebar.caption("👆 :red[Please set your OpenAI API Key here]") | |
| st.caption(":green[Your API is not stored anywhere. It is only used to generate answers to your questions.]") | |
| set_openAi_api_key(api_key_input) | |
| def launchfreeversion(): | |
| HUGGINGFACE_API_TOKEN = os.environ['access_code'] | |
| model_name = "BAAI/bge-base-en" | |
| encode_kwargs = {'normalize_embeddings': True} | |
| st.title('MKG: Your Chat with Youtube Assistant') | |
| videourl = 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 = extract_video_id(videourl) | |
| loader = YoutubeLoader(video_id) | |
| documents = loader.load() | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100) | |
| documents = text_splitter.split_documents(documents) | |
| vectordb = Chroma.from_documents( | |
| documents, | |
| #embedding = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl", | |
| # model_kwargs={"device": "cuda"}) | |
| 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":0.1, "max_new_tokens":1000}), | |
| retriever=vectordb.as_retriever(), | |
| return_source_documents=False, | |
| verbose=False | |
| ) | |
| with st.spinner('Generating Answer...'): | |
| llm_response = qa_chain(query) | |
| #llm_originalresponse2=llm_response['result'] | |
| process_llm_response(llm_response) | |
| launchfreeversion() | |
| def intro(): | |
| st.markdown(""" | |
| # MKG: Your Chat with Youtube Assistant 🎬🤖 | |
| Welcome to MKG-Assistant, where AI meets Youtube! 🚀🔍 | |
| ## Base Models | |
| Q&A-Assistant is built on OpenAI's GPT 3.5 for the premium version and Falcon 7B instruct Model for the free version to enhance your websites browsing experience. Whether you're a student, researcher, or professional, we're here to simplify your interactions with the web. 💡📚 | |
| ## How to Get Started | |
| 1.Enter the Video URL. | |
| 2. Enter your API key.(Only if you chose the premium version. Key is not needed in the free version) | |
| 3. Ask questions using everyday language. | |
| 4. Get detailed, AI-generated answers. | |
| 5. Enjoy a smarter way to Interact with Youtube! | |
| ## It is Time to Dive in! | |
| """) | |
| page_names_to_funcs = { | |
| "Main Page": intro, | |
| "Open Source Edition (Free version)": free_version, | |
| "Premium edition (Requires Open AI API Key )": paid_version | |
| } | |
| #test | |
| demo_name = st.sidebar.selectbox("Choose a version", page_names_to_funcs.keys()) | |
| page_names_to_funcs[demo_name]() | |
| st.sidebar.markdown('<a href="https://www.linkedin.com/in/mohammed-khalil-ghali-11305119b/"> Connect on LinkedIn <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/linkedin/linkedin-original.svg" alt="LinkedIn" width="30" height="30"></a>', unsafe_allow_html=True) | |
| st.sidebar.markdown('<a href="https://github.com/khalil-ghali"> Check out my 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) |