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import gradio as gr |
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import shutil, openai, os |
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from langchain.chains import RetrievalQA |
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from langchain.chat_models import ChatOpenAI |
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from langchain.document_loaders.blob_loaders.youtube_audio import YoutubeAudioLoader |
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from langchain.document_loaders.generic import GenericLoader |
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from langchain.document_loaders.parsers import OpenAIWhisperParser |
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from langchain.embeddings.openai import OpenAIEmbeddings |
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from langchain.prompts import PromptTemplate |
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from langchain.text_splitter import RecursiveCharacterTextSplitter |
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from langchain.vectorstores import Chroma |
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from dotenv import load_dotenv, find_dotenv |
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_ = load_dotenv(find_dotenv()) |
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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, |
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don't try to make up an answer. Use three sentences maximum. Keep the answer as concise as possible. Always say "Thanks, Bernd Straehle 🚀" |
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at the end of the answer. {context} Question: {question} Helpful Answer: """ |
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QA_CHAIN_PROMPT = PromptTemplate(input_variables = ["context", "question"], template = template) |
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def invoke(openai_api_key, youtube_url, prompt): |
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openai.api_key = openai_api_key |
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youtube_dir = "docs/youtube/" |
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loader = GenericLoader(YoutubeAudioLoader([youtube_url], youtube_dir), OpenAIWhisperParser()) |
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docs = loader.load() |
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text_splitter = RecursiveCharacterTextSplitter(chunk_size = 1500, chunk_overlap = 150) |
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splits = text_splitter.split_documents(docs) |
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chroma_dir = "docs/chroma/" |
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vectordb = Chroma.from_documents(documents = splits, embedding = OpenAIEmbeddings(), persist_directory = chroma_dir) |
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llm = ChatOpenAI(model_name = "gpt-4", temperature = 0) |
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qa_chain = RetrievalQA.from_chain_type(llm, retriever = vectordb.as_retriever(), return_source_documents = True, chain_type_kwargs = {"prompt": QA_CHAIN_PROMPT}) |
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result = qa_chain({"query": prompt}) |
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shutil.rmtree(youtube_dir) |
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shutil.rmtree(chroma_dir) |
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return result["result"] |
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description = """The app demonstrates how to use a <strong>Large Language Model</strong> (LLM) with <strong>Retrieval Augmented Generation</strong> (RAG) on external data. |
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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 |
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Implementation: <a href='https://www.gradio.app/'>Gradio</a> UI using <a href='https://platform.openai.com/'>OpenAI</a> API |
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via AI-first toolkit <a href='https://www.langchain.com/'>LangChain</a> with foundation models |
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<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).""" |
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gr.close_all() |
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demo = gr.Interface(fn=invoke, |
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inputs = [gr.Textbox(label = "OpenAI API Key", lines = 1), gr.Textbox(label = "YouTube URL", lines = 1), gr.Textbox(label = "Prompt", lines = 1)], |
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outputs = [gr.Textbox(label = "Completion", lines = 1)], |
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title = "Generative AI - LLM & RAG", |
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description = description) |
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demo.launch() |
