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import gradio as gr |
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import shutil, openai, os |
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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.document_loaders.blob_loaders.youtube_audio import YoutubeAudioLoader |
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from dotenv import load_dotenv, find_dotenv |
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_ = load_dotenv(find_dotenv()) |
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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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url = youtube_url |
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save_dir = "docs/youtube/" |
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loader = GenericLoader( |
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YoutubeAudioLoader([url], save_dir), |
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OpenAIWhisperParser() |
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) |
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docs = loader.load() |
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shutil.rmtree(save_dir) |
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retrieval = docs[0].page_content |
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return retrieval |
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description = """The app demonstrates how to use a Large Language Model (LLM) with Retrieval Augmented Generation (RAG) on external data.\n\n |
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Enter an OpenAI API key, YouTube URL, and prompt - search the video, analyse the sentiment, summarize it, translate it, etc.\n\n |
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<a href='https://www.gradio.app/'>Gradio</a> UI using <a href='https://platform.openai.com/'>OpenAI</a> API |
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with <a href='https://openai.com/research/whisper'>Whisper</a> foundation model (for speech to text generation) |
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and <a href='https://openai.com/research/gpt-4'>GPT-4</a> foundation model (for LLM use cases) |
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via AI-first toolkit <a href='https://www.langchain.com/'>LangChain</a>.""" |
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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 - RAG", |
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description = description) |
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demo.launch() |
