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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 "\n\nThanks, Bernd Straehle 🚀" at the end of the answer. | |
| {context} Question: {question} Helpful Answer: """ | |
| QA_CHAIN_PROMPT = PromptTemplate(input_variables = ["context", "question"], template = template) | |
| def invoke(openai_api_key, youtube_url, prompt): | |
| openai.api_key = openai_api_key | |
| youtube_dir = "docs/youtube/" | |
| loader = GenericLoader(YoutubeAudioLoader([youtube_url], youtube_dir), OpenAIWhisperParser()) | |
| docs = loader.load() | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size = 1500, chunk_overlap = 150) | |
| splits = text_splitter.split_documents(docs) | |
| chroma_dir = "docs/chroma/" | |
| vectordb = Chroma.from_documents(documents = splits, embedding = OpenAIEmbeddings(), persist_directory = chroma_dir) | |
| llm = ChatOpenAI(model_name = "gpt-4", temperature = 0) | |
| qa_chain = RetrievalQA.from_chain_type(llm, retriever = vectordb.as_retriever(), return_source_documents = True, chain_type_kwargs = {"prompt": QA_CHAIN_PROMPT}) | |
| result = qa_chain({"query": prompt}) | |
| shutil.rmtree(youtube_dir) | |
| shutil.rmtree(chroma_dir) | |
| return result["result"] | |
| description = """The app demonstrates how to use a <strong>Large Language Model</strong> (LLM) with <strong>Retrieval Augmented Generation</strong> (RAG) on external data. | |
| Enter an OpenAI API key, YouTube URL (external data), and prompt to search the video, analyse its sentiment, summarize it, and/or translate it, etc.\n\n | |
| Implementation: <a href='https://www.gradio.app/'>Gradio</a> UI using <a href='https://platform.openai.com/'>OpenAI</a> API | |
| via AI-first toolkit <a href='https://www.langchain.com/'>LangChain</a> with foundation models | |
| <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 use cases).""" | |
| gr.close_all() | |
| demo = gr.Interface(fn=invoke, | |
| inputs = [gr.Textbox(label = "OpenAI API Key", default = "sk-", lines = 1), gr.Textbox(label = "YouTube URL", default = "https://www.youtube.com/watch?v=3cQNkIrg-Tk", lines = 1), gr.Textbox(label = "Prompt", default = "What is the sentiment of the song?", lines = 1)], | |
| outputs = [gr.Textbox(label = "Completion", lines = 1)], | |
| title = "Generative AI - LLM & RAG", | |
| description = description) | |
| demo.launch() |