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| import gradio as gr | |
| import shutil, openai, os | |
| from langchain.chains import RetrievalQA | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.document_loaders.blob_loaders.youtube_audio import YoutubeAudioLoader | |
| from langchain.document_loaders.generic import GenericLoader | |
| from langchain.document_loaders.parsers import OpenAIWhisperParser | |
| from langchain.embeddings.openai import OpenAIEmbeddings | |
| from langchain.prompts import PromptTemplate | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.vectorstores import Chroma | |
| from dotenv import load_dotenv, find_dotenv | |
| _ = load_dotenv(find_dotenv()) | |
| #openai.api_key = os.environ["OPENAI_API_KEY"] | |
| template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up | |
| an answer. Keep the answer as concise as possible. Always say "🔥 Thanks for using the app, Bernd Straehle." at the end of the answer. | |
| {context} Question: {question} Helpful Answer: """ | |
| QA_CHAIN_PROMPT = PromptTemplate(input_variables = ["context", "question"], template = template) | |
| YOUTUBE_DIR = "docs/youtube/" | |
| CHROMA_DIR = "docs/chroma/" | |
| MODEL_NAME = "gpt-4" | |
| def invoke(openai_api_key, youtube_url, process_video, prompt): | |
| openai.api_key = openai_api_key | |
| print(process_video) | |
| if (process_video): | |
| loader = GenericLoader(YoutubeAudioLoader([youtube_url], YOUTUBE_DIR), OpenAIWhisperParser()) | |
| docs = loader.load() | |
| shutil.rmtree(YOUTUBE_DIR) | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size = 1500, chunk_overlap = 150) | |
| splits = text_splitter.split_documents(docs) | |
| vector_db = Chroma.from_documents(documents = splits, embedding = OpenAIEmbeddings(), persist_directory = CHROMA_DIR) | |
| else: | |
| vector_db = Chroma(persist_directory = CHROMA_DIR, embedding_function = OpenAIEmbeddings()) | |
| llm = ChatOpenAI(model_name = MODEL_NAME, temperature = 0) | |
| qa_chain = RetrievalQA.from_chain_type(llm, retriever = vector_db.as_retriever(), return_source_documents = True, chain_type_kwargs = {"prompt": QA_CHAIN_PROMPT}) | |
| result = qa_chain({"query": prompt}) | |
| return result["result"] | |
| description = """<strong>Overview:</strong> The app demonstrates how to use a <strong>Large Language Model</strong> (LLM) with <strong>Retrieval Augmented Generation</strong> | |
| (RAG) on external data (YouTube videos in this case, but could be PDFs, URLs, databases, etc.).\n\n | |
| <strong>Instructions:</strong> Enter an OpenAI API key, YouTube URL, and prompt to perform semantic search, sentiment analysis, summarization, | |
| translation, etc. "Process Video" specifies whether or not to perform speech-to-text processing. To ask multiple questions related to the same video, | |
| typically set it to "True" the first time and then to "False". Note that persistence is not guaranteed in the Hugging Face free tier | |
| (the plan is to migrate to AWS S3). The example is a 3:12 min. video about GPT-4 and takes about 20 sec. to process. Try different prompts, for example | |
| "what is gpt-4, answer in german" or "write a poem about gpt-4".\n\n | |
| <strong>Technology:</strong> <a href='https://www.gradio.app/'>Gradio</a> UI using <a href='https://platform.openai.com/'>OpenAI</a> API | |
| via AI-first <a href='https://www.langchain.com/'>LangChain</a> toolkit with <a href='https://openai.com/research/whisper'>Whisper</a> (speech-to-text) | |
| and <a href='https://openai.com/research/gpt-4'>GPT-4</a> (LLM) foundation models as well as AI-native | |
| <a href='https://www.trychroma.com/'>Chroma</a> embedding database.""" | |
| gr.close_all() | |
| demo = gr.Interface(fn=invoke, | |
| inputs = [gr.Textbox(label = "OpenAI API Key", value = "sk-", lines = 1), gr.Textbox(label = "YouTube URL", value = "https://www.youtube.com/watch?v=--khbXchTeE", lines = 1), gr.Radio([True, False], label="Process Video", value = True), gr.Textbox(label = "Prompt", value = "what is gpt-4", lines = 1)], | |
| outputs = [gr.Textbox(label = "Completion", lines = 1)], | |
| title = "Generative AI - LLM & RAG", | |
| description = description) | |
| demo.launch() |