app.py

import streamlit as st
import requests
import datetime

from langchain.llms import HuggingFaceHub
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler

from langchain.chains import RetrievalQA
from langchain.schema import LLMResult
from langchain.callbacks.base import BaseCallbackHandler

# 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 WebBaseLoader, RecursiveUrlLoader
from langchain.chains import RetrievalQA

embedding = HuggingFaceHubEmbeddings()

url = "https://us.community.samsung.com/t5/Get-Help/ct-p/get-help?page=1&tab=recent_topics"
loader = RecursiveUrlLoader(url=url, max_depth=1)
loaders = []

loaders.append(loader)

docs = []
for l in loaders:
   docs.extend(l.load())
text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000)
docs = text_splitter.split_documents(docs)




# text_splitter=RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
# all_splits = text_splitter.split_documents(data) 

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. 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=repo_id, model_kwargs={"temperature":0.1, "max_new_tokens":250})

qachain=RetrievalQA.from_chain_type(llm, retriever=vectorstore.as_retriever(), memory=memory, 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:
    st.write(f"{datetime.datetime.now()} :red[{option}:] ", prompt)

    response = requests.get("https://dummyjson.com/products/1").text
    
    st.write(f"{datetime.datetime.now()} :red[Bot:] ", response)


    context = [] # the context stores a conversation history, you can use this to make the model more context aware
while True:
    st.write(f"{datetime.datetime.now()} :red[{option}:] ", prompt)
    response = qachain({"query": prompt})
    
    st.write(f"{datetime.datetime.now()} :red[Bot:] ", context['result'])
    prompt = st.chat_input("Say something to our #CodeWars bot...")