app.py

import streamlit as st
import requests
import datetime
import os


from langchain.llms import HuggingFaceHub

from langchain.chains import RetrievalQA

# Embed and store
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceHubEmbeddings
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import RecursiveUrlLoader
from langchain.chains import RetrievalQA
from langchain.document_loaders import PyPDFLoader

os.environ["HUGGINGFACEHUB_API_TOKEN"] = "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"
embedding = HuggingFaceHubEmbeddings()

loader = PyPDFLoader("sample.pdf")
pages = loader.load_and_split()

text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
docs = text_splitter.split_documents(pages)

vectorstore = Chroma.from_documents(documents=docs, embedding=embedding)

memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) # to remember chat history include this

prompt_template = """Use the following pieces of context to answer the question at the end and provide the reasons. If you don't know the answer, just say that you don't know, don't try to make up an answer.
{context}
Question: {question}"""

PROMPT = PromptTemplate(
   template=prompt_template, input_variables=["context", "question"]
)
chain_type_kwargs = {"prompt": PROMPT}

llm = HuggingFaceHub(repo_id="google/flan-ul2", model_kwargs={"temperature":0.4, "max_new_tokens":250})

qachain=RetrievalQA.from_chain_type(llm, retriever=vectorstore.as_retriever(), chain_type_kwargs=chain_type_kwargs)
st.header("#CodeWars localGPT", divider='rainbow')

option = st.selectbox('What is your role?', ('Support', 'Sales'))

st.write('You selected', option)

prompt = st.chat_input("Say something to our #CodeWars bot...")
if(prompt):
    with st.chat_message(option):
         st.write(f"{datetime.datetime.now()} :red[{option}:] ", prompt)
    context = qachain({"query": prompt})
    
    st.write(f"{datetime.datetime.now()}", context)