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| 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 = """ | |
| <div class="max-w-full overflow-auto"> | |
| <table> | |
| <thead> | |
| <tr> | |
| <th>Model</th> | |
| <th>Llama2</th> | |
| <th>Llama2-hf</th> | |
| <th>Llama2-chat</th> | |
| <th>Llama2-chat-hf</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr> | |
| <td>7B</td> | |
| <td><a rel="noopener nofollow" href="https://huggingface.co/meta-llama/Llama-2-7b">Link</a></td> | |
| <td><a rel="noopener nofollow" href="https://huggingface.co/meta-llama/Llama-2-7b-hf">Link</a></td> | |
| <td><a rel="noopener nofollow" href="https://huggingface.co/meta-llama/Llama-2-7b-chat">Link</a></td> | |
| <td><a rel="noopener nofollow" href="https://huggingface.co/meta-llama/Llama-2-7b-chat-hf">Link</a></td> | |
| </tr> | |
| <tr> | |
| <td>13B</td> | |
| <td><a rel="noopener nofollow" href="https://huggingface.co/meta-llama/Llama-2-13b">Link</a></td> | |
| <td><a rel="noopener nofollow" href="https://huggingface.co/meta-llama/Llama-2-13b-hf">Link</a></td> | |
| <td><a rel="noopener nofollow" href="https://huggingface.co/meta-llama/Llama-2-13b-chat">Link</a></td> | |
| <td><a rel="noopener nofollow" href="https://huggingface.co/meta-llama/Llama-2-13b-chat-hf">Link</a></td> | |
| </tr> | |
| <tr> | |
| <td>70B</td> | |
| <td><a rel="noopener nofollow" href="https://huggingface.co/meta-llama/Llama-2-70b">Link</a></td> | |
| <td><a rel="noopener nofollow" href="https://huggingface.co/meta-llama/Llama-2-70b-hf">Link</a></td> | |
| <td><a rel="noopener nofollow" href="https://huggingface.co/meta-llama/Llama-2-70b-chat">Link</a></td> | |
| <td><a rel="noopener nofollow" href="https://huggingface.co/meta-llama/Llama-2-70b-chat-hf">Link</a></td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| </div> | |
| 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'<iframe src="{embed_url}" frameborder="0" allowfullscreen></iframe>' | |
| 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-large-v2.hf.space/") | |
| result = client.predict( | |
| youtube_url, | |
| "transcribe", | |
| api_name="/predict_2" | |
| ) | |
| 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("# 💸 **Support our project**") | |
| st.markdown("This money would be used for paying for API and supporting our team.") | |
| st.markdown("[🔗 Link](https://send.monobank.ua/jar/4mvqDivxmP)") | |
| st.markdown("") | |
| st.markdown("# Enter the YouTube Video URL below🔗") | |
| 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. | |
| """ | |
| 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 | |
| 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=("🧑💻" if message["role"] == "human" else "🦙")): | |
| st.markdown(message["content"]) | |
| textinput = st.chat_input("Ask anything about the video...") | |
| if prompt := textinput: | |
| st.chat_message("human", avatar="🧑💻").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="🦙"): | |
| st.markdown(response) | |
| # Add assistant response to chat history | |
| st.session_state.messages.append({"role": "assistant", "content": response}) | |