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import gradio as gr |
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import os |
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from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI |
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from langchain.document_loaders import YoutubeLoader |
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from langchain.text_splitter import RecursiveCharacterTextSplitter |
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from langchain.vectorstores import FAISS |
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from langchain.chains import LLMChain |
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from langchain.prompts.chat import ( |
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ChatPromptTemplate, |
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SystemMessagePromptTemplate, |
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HumanMessagePromptTemplate, |
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) |
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def create_db_from_video_url(video_url, api_key): |
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""" |
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Creates an Embedding of the Video and performs |
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""" |
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embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=api_key) |
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loader = YoutubeLoader.from_youtube_url(video_url) |
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transcripts = loader.load() |
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100) |
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docs = text_splitter.split_documents(transcripts) |
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db = FAISS.from_documents(docs, embedding=embeddings) |
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return db |
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def get_response(video, request): |
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""" |
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Usind Gemini Pro to get the response. It can handle upto 32k tokens. |
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""" |
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API_KEY = os.environ.get("API_Key") |
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db = create_db_from_video_url(video, API_KEY) |
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docs = db.similarity_search(query=request, k=5) |
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docs_content = " ".join([doc.page_content for doc in docs]) |
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chat = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=API_KEY, convert_system_message_to_human=True) |
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template = """ |
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You are an assistant that can answer questions about youtube videos based on |
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video transcripts: {docs} |
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Only use factual information from the transcript to answer the question. |
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If you don't have enough information to answer the question, say "I don't know". |
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Your Answers should be detailed. |
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""" |
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system_msg_prompt = SystemMessagePromptTemplate.from_template(template) |
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human_template = "Answer the following questions: {question}" |
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human_msg_prompt = HumanMessagePromptTemplate.from_template(human_template) |
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chat_prompt = ChatPromptTemplate.from_messages( |
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[system_msg_prompt, human_msg_prompt] |
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) |
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chain = LLMChain(llm=chat, prompt=chat_prompt) |
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response = chain.run(question=request, docs=docs_content) |
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return response |
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title = "YouTube Video Assistant 🧑💻" |
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description = "Answers to the Questions asked by the user on the specified YouTube video. (English Only)\nClick here to view [demo](https://cdn-uploads.huggingface.co/production/uploads/641aa7814577db917f70f8aa/vSEGALDIYsqdRM7t_49rp.mp4)." |
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article = "Other Projects: \t"\ |
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"💰 [Health Insurance Predictor](http://health-insurance-cost-predictor-k19.streamlit.app/) "\ |
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"📰 [Fake News Detector](https://fake-news-detector-k19.streamlit.app/) "\ |
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"🪶 [Birds Classifier](https://huggingface.co/spaces/Kathir0011/Birds_Classification)" |
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youtube_video_assistant = gr.Interface( |
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fn=get_response, |
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inputs=[gr.Text(label="Enter the Youtube Video URL:", placeholder="Example: https://www.youtube.com/watch?v=MnDudvCyWpc"), |
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gr.Text(label="Enter your Question", placeholder="Example: What's the video is about?")], |
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outputs=gr.TextArea(label="Answers using Gemini Pro:"), |
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title=title, |
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description=description, |
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article=article |
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) |
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youtube_video_assistant.launch() |
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