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"""Python file to serve as the frontend"""
import streamlit as st
from streamlit_chat import message
from langchain.chains import ConversationChain
from langchain.llms import OpenAI
import os
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Pinecone
from langchain.document_loaders import TextLoader
import pinecone
from langchain.document_loaders import TextLoader
import streamlit as st
# import pandas as pd
from constants import INDEX_NAME, NAMESPACE,PINECONE_ENV
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
from langchain.chains.question_answering import load_qa_chain
PINECONE_API_KEY= st.secrets["PINECONE_API_KEY"]
OPENAI_API_KEY = st.secrets["OPENAI_API_KEY"]
os.environ['OPENAI_API_KEY'] =OPENAI_API_KEY
# initialize pinecone
pinecone.init(
api_key=PINECONE_API_KEY, # find at app.pinecone.io
environment=PINECONE_ENV # next to api key in console
)
embeddings = OpenAIEmbeddings()
llm = OpenAI(temperature=0)
@st.cache_resource
def load_pinecone_existing_index(question):
pass
searchIndex = Pinecone.from_existing_index(index_name=INDEX_NAME,embedding = embeddings, namespace=NAMESPACE)
docsReturned = searchIndex.similarity_search(question, k=2)
return docsReturned
@st.cache_resource
def get_answer(question):
chain = load_qa_chain(llm, chain_type="stuff")
docs=load_pinecone_existing_index(question)
answer = chain.run(input_documents=docs, question=question)
return answer
# chain = load_qa_chain(llm, chain_type="stuff")
# answer = chain.run(input_documents=docs, question=QUERY)
# From here down is all the StreamLit UI.
st.set_page_config(page_title="Langchain Chat with PDF", page_icon=":robot:")
st.header("Chat with PDF Example")
if "generated" not in st.session_state:
st.session_state["generated"] = []
if "past" not in st.session_state:
st.session_state["past"] = []
def get_text():
input_text = st.text_input("You: ", "Hi,how are you.", key="input")
return input_text
user_input = get_text()
if user_input:
# output = chain.run(input=user_input)
output = get_answer(user_input)
st.session_state.past.append(user_input)
st.session_state.generated.append(output)
if st.session_state["generated"]:
for i in range(len(st.session_state["generated"]) - 1, -1, -1):
message(st.session_state["generated"][i], key=str(i))
message(st.session_state["past"][i], is_user=True, key=str(i) + "_user") |