Delete pages/3_TalktoAVideo

pages/3_TalktoAVideo

@@ -1,83 +0,0 @@
-import gradio as gr
-import os
-
-from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI
-from langchain_community.document_loaders import YoutubeLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import FAISS
-from langchain.chains import LLMChain
-from langchain.prompts.chat import (
-    ChatPromptTemplate,
-    SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-)
-
-def create_db_from_video_url(video_url, api_key):
-    """
-    Creates an Embedding of the Video and performs 
-    """
-    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=api_key)
-
-    loader = YoutubeLoader.from_youtube_url(video_url)
-    transcripts = loader.load()
-    # cannot provide this directly to the model so we are splitting the transcripts into small chunks
-
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
-    docs = text_splitter.split_documents(transcripts)
-
-    db = FAISS.from_documents(docs, embedding=embeddings)
-    return db
-
-def get_response(video, request):
-    """
-    Usind Gemini Pro to get the response. It can handle upto 32k tokens.
-    """
-    API_KEY = os.environ.get("GOOGLE_API_KEY")
-    db = create_db_from_video_url(video, API_KEY)
-    docs = db.similarity_search(query=request, k=5)
-    docs_content = " ".join([doc.page_content for doc in docs])
-
-    chat = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=API_KEY, convert_system_message_to_human=True)
-
-    # creating a template for request
-    template = """
-    You are an assistant that can answer questions about youtube videos based on
-    video transcripts: {docs}
-    Only use factual information from the transcript to answer the question.
-    If you don't have enough information to answer the question, say "I don't know".
-    Your Answers should be detailed.
-    """
-
-    system_msg_prompt = SystemMessagePromptTemplate.from_template(template)
-
-    # human prompt
-    human_template = "Answer the following questions: {question}"
-    human_msg_prompt = HumanMessagePromptTemplate.from_template(human_template)
-
-    chat_prompt = ChatPromptTemplate.from_messages(
-        [system_msg_prompt, human_msg_prompt]
-    )
-
-    chain = LLMChain(llm=chat, prompt=chat_prompt)
-
-    response = chain.run(question=request, docs=docs_content)
-
-    return response
-
-# creating title, description for the web app
-title = "YouTube Video AI Assistant 🤖"
-description = "Answers to the Questions asked by the user on the specified YouTube video."
-
-
-# building the app
-youtube_video_assistant = gr.Interface(
-    fn=get_response,
-    inputs=[gr.Text(label="Enter the Youtube Video URL:", placeholder="Example: https://www.youtube.com/watch?v=MnDudvCyWpc"),
-            gr.Text(label="Enter your Question", placeholder="Example: What's the video is about?")],
-    outputs=gr.TextArea(label="Answers using....some secret llm 🤫😉:"),
-    title=title,
-    description=description
-)
-
-# launching the web app
-youtube_video_assistant.launch(share=True)