import os import logging from typing import Any, List, Mapping, Optional from langchain.llms import HuggingFaceHub from gradio_client import Client from langchain.schema import Document from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.vectorstores import FAISS from langchain.embeddings.huggingface import HuggingFaceEmbeddings from langchain.callbacks.manager import CallbackManagerForLLMRun from langchain.llms.base import LLM from langchain.chains import RetrievalQA from langchain.prompts import PromptTemplate import streamlit as st from pytube import YouTube # import replicate DESCRIPTION = """
openai/whisper-large-v3 """ models = { "Llama2-70b": { "model_link": "https://huggingface.co/meta-llama/Llama-2-70b", "chat_link": "https://ysharma-explore-llamav2-with-tgi.hf.space/", }, "Llama2-13b": { "model_link": "https://huggingface.co/meta-llama/Llama-2-13b", "chat_link": "https://huggingface-projects-llama-2-13b-chat.hf.space/", } } DESCRIPTION = """ Welcome to the **YouTube Video Chatbot** powered by Llama-2 models. Here's what you can do: - **Transcribe & Understand**: Provide any YouTube video URL, and our system will transcribe it. Our advanced NLP model will then understand the content, ready to answer your questions. - **Ask Anything**: Based on the video's content, ask any question, and get instant, context-aware answers. To get started, simply paste a YouTube video URL and select a model in the sidebar, then start chatting with the model about the video's content. Enjoy the experience! """ st.title("YouTube Video Chatbot") st.markdown(DESCRIPTION) def get_video_title(youtube_url: str) -> str: yt = YouTube(youtube_url) embed_url = f"https://www.youtube.com/embed/{yt.video_id}" embed_html = f'' return yt.title, embed_html def transcribe_video(youtube_url: str, path: str) -> List[Document]: """ Transcribe a video and return its content as a Document. """ logging.info(f"Transcribing video: {youtube_url}") client = Client("https://sanchit-gandhi-whisper-jax.hf.space/") result = client.predict(youtube_url, "translate", True, fn_index=7) return [Document(page_content=result[1], metadata=dict(page=1))] # def predict(message: str, system_prompt: str = '', temperature: float = 0.7, max_new_tokens: int = 1024, # topp: float = 0.5, repetition_penalty: float = 1.2) -> Any: # """ # Predict a response using a client. # """ # client = Client("https://osanseviero-mistral-super-fast.hf.space/") # response = client.predict( # message, # temperature, # max_new_tokens, # topp, # repetition_penalty, # api_name="/chat" # ) # return response def predict(input, images = []): client = Client("https://roboflow-gemini.hf.space/--replicas/bkd57/") result = client.predict( None, images, 0.4, 2048, "", 32, 1, [[input,None]], api_name="/bot" ) return result[0][1] PATH = os.path.join(os.path.expanduser("~"), "Data") def initialize_session_state(): if "youtube_url" not in st.session_state: st.session_state.youtube_url = "" if "model_choice" not in st.session_state: st.session_state.model_choice = "Llama2-70b" if "setup_done" not in st.session_state: st.session_state.setup_done = False if "doneYoutubeurl" not in st.session_state: st.session_state.doneYoutubeurl = "" def sidebar(): with st.sidebar: st.markdown("# ๐ธ **Support our project**") st.markdown("This money would be used for paying for API and supporting our team.") st.markdown("[๐ Link](https://send.monobank.ua/jar/4mvqDivxmP)") st.markdown("") st.markdown("# Enter the YouTube Video URL below๐") st.session_state.youtube_url = st.text_input("YouTube Video URL:") model_choice = st.radio("Choose a Model:", list(models.keys())) st.session_state.model_choice = model_choice if st.session_state.youtube_url: # Get the video title video_title, embed_html = get_video_title(st.session_state.youtube_url) st.markdown(f"### {video_title}") # Embed the video st.markdown(embed_html, unsafe_allow_html=True) sidebar() initialize_session_state() text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0) embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-l6-v2") prompt = PromptTemplate( template="""Given the context about a video. Answer the user in a friendly and precise manner. Context: {context} Human: {question} AI:""", input_variables=["context", "question"] ) class LlamaLLM(LLM): """ Custom LLM class. """ @property def _llm_type(self) -> str: return "custom" def _call(self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None) -> str: model_link = models[st.session_state.model_choice]["chat_link"] response = predict(prompt) return response @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" return {} # Check if a new YouTube URL is provided if st.session_state.youtube_url != st.session_state.doneYoutubeurl: st.session_state.setup_done = False if st.session_state.youtube_url and not st.session_state.setup_done: with st.status("Transcribing video..."): data = transcribe_video(st.session_state.youtube_url, PATH) with st.status("Running Embeddings..."): docs = text_splitter.split_documents(data) docsearch = FAISS.from_documents(docs, embeddings) retriever = docsearch.as_retriever() retriever.search_kwargs["distance_metric"] = "cos" retriever.search_kwargs["k"] = 4 with st.status("Running RetrievalQA..."): llama_instance = LlamaLLM() st.session_state.qa = RetrievalQA.from_chain_type(llm=llama_instance, chain_type="stuff", retriever=retriever, chain_type_kwargs={"prompt": prompt}) st.session_state.doneYoutubeurl = st.session_state.youtube_url st.session_state.setup_done = True # Mark the setup as done for this URL if "messages" not in st.session_state: st.session_state.messages = [] for message in st.session_state.messages: with st.chat_message(message["role"], avatar=("๐งโ๐ป" if message["role"] == "human" else "๐ฆ")): st.markdown(message["content"]) textinput = st.chat_input("Ask anything about the video...") if prompt := textinput: st.chat_message("human", avatar="๐งโ๐ป").markdown(prompt) st.session_state.messages.append({"role": "human", "content": prompt}) with st.status("Requesting Client..."): video_title, _ = get_video_title(st.session_state.youtube_url) additional_context = f"Given the context about a video titled '{video_title}' available at '{st.session_state.youtube_url}'." response = st.session_state.qa.run(prompt + " " + additional_context) with st.chat_message("assistant", avatar="๐ฆ"): st.markdown(response) # Add assistant response to chat history st.session_state.messages.append({"role": "assistant", "content": response})