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app.py

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+import streamlit as st
+import time
+from main import *
+import textwrap
+from dotenv import load_dotenv
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.llms import HuggingFaceHub
+from langchain.document_loaders import YoutubeLoader
+from langchain.chains import LLMChain
+from langchain.prompts import (SystemMessagePromptTemplate,HumanMessagePromptTemplate,
+                               ChatPromptTemplate)
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+import textwrap
+from langchain_google_genai import GoogleGenerativeAI
+
+load_dotenv()
+
+st.header("Youtube Q&A")
+
+querry_input = st.chat_input()
+url_input = st.text_input("Your Youtube url")
+
+bot = st.chat_message("ai")
+bot.write("Hello!,\n enter the url in the allocated area and type the query.")
+
+if querry_input:
+    user = st.chat_message("user")
+    user.write(querry_input)
+    bot = st.chat_message("ai")
+    with bot.status("Patience and thee shalt knoweth thy answ'r") as status:
+        st.write("Creating Faiss database...")
+        time.sleep(1)
+        st.write("Generating asnwer for your querry....")
+
+        response,docs = app(url=url_input,querry=querry_input)
+        status.update(label= "Done!",state="complete")
+    bot = st.chat_message("ai")
+    bot.write(response)
+

main.py

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+# Prerequisite libraries
+
+from dotenv import load_dotenv
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.llms import HuggingFaceHub
+from langchain.document_loaders import YoutubeLoader
+from langchain.chains import LLMChain
+from langchain.prompts import (SystemMessagePromptTemplate,HumanMessagePromptTemplate,
+                               ChatPromptTemplate)
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+import textwrap
+from langchain_google_genai import GoogleGenerativeAI
+
+
+load_dotenv()
+
+def app(url,querry):
+    loader = YoutubeLoader.from_youtube_url(youtube_url=url)
+    transcript = loader.load()
+    embeddings = HuggingFaceEmbeddings()
+    rcts = RecursiveCharacterTextSplitter(chunk_size=400,chunk_overlap=20)
+    docs = rcts.split_documents(transcript)
+
+    db = FAISS.from_documents(docs,embeddings)
+
+    docs = db.similarity_search(querry, k=3)
+    docs_page_content = " ".join([d.page_content for d in docs])
+
+    template = """
+        hey you are a very helpful Ai assistant who is able to answer question about youtube videos based on the video's
+        transcript: {source}
+        Only use the factual imformation gathered from the transcript to answer the question also answer it in a very detailed manner more than 30 words.
+        If you feel that you dont have enough imformation to answer the question say "I dont have enough imformation in order to answer this question".
+        """
+
+    llm = GoogleGenerativeAI(model="models/text-bison-001")
+
+    system_msg_template = SystemMessagePromptTemplate.from_template(template)
+
+    human_template = "Answer the following question: {question}"
+    human_msg_template = HumanMessagePromptTemplate.from_template(human_template)
+
+    chat_prompt = ChatPromptTemplate.from_messages(
+        [system_msg_template,human_msg_template],
+    )
+
+
+    chain = LLMChain(llm = llm,prompt = chat_prompt)
+
+    responce = chain.run(question = querry,source = docs_page_content)
+    
+    return responce, docs