Spaces:
Sleeping
Sleeping
File size: 1,941 Bytes
11fa0f1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
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) |