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import streamlit as st
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS, Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain.llms import OpenAI as OpenAI_llm
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain,RetrievalQA
from langchain.memory import ConversationBufferMemory
from langchain.document_loaders import PyPDFLoader, TextLoader, WebBaseLoader
from langchain.prompts.chat import ChatPromptTemplate,HumanMessagePromptTemplate,SystemMessagePromptTemplate
# from langchain.chains.qa_with_sources import load_qa_with_sources_chain,BaseCombineDocumentsChain
import os
import chromadb
import tempfile
import requests
import openai
from bs4 import BeautifulSoup
from urllib.parse import urlparse
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
def assistant(url):
question=st.text_input("Ask your Question")
if st.button("Submit",type="primary"):
ABS_PATH: str = os.path.dirname(os.path.abspath(__file__))
DB_DIR: str = os.path.join(ABS_PATH,"db")
loader=WebBaseLoader(url)
data=loader.load()
text_splitter = CharacterTextSplitter(separator='\n',
chunk_size=1000,chunk_overlap=0)
docs = text_splitter.split_documents(data)
openai_embeddings = OpenAIEmbeddings()
# client = chromadb.PersistentClient(path=DB_DIR)
vectordb = FAISS.from_documents(documents=docs,embedding=openai_embeddings)
# vectordb.persist()
retriever=vectordb.as_retriever()
llm=ChatOpenAI(model_name='gpt-3.5-turbo')
qa=RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)
response=qa(question)
st.write(response)
st.title('Chat with Website')
url=st.text_input('Enter Your URL here:')
if url:
assistant(url) |