Chat Bot

## download all the lib and create a donenv file where you can store the API key


import os
import streamlit as st
from streamlit_chat import message
from langchain.chains import ConversationalRetrievalChain
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.llms import CTransformers
from langchain.llms import Replicate
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.document_loaders import PyPDFLoader
from langchain.document_loaders import TextLoader
from langchain.document_loaders import Docx2txtLoader
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from dotenv import load_dotenv
import tempfile


load_dotenv()

def initialize_state():
  if " histroy " not in st.session_state:
    st.session_state['history'] = []
  if "generated" not in st.session_state:
    st.session_state['generated'] = ["Hello! I am a chatbot trained on your documents. Ask me anything!"]
  if "past" not in st.session_state:
    st.session_state['past'] = ["Hey!"]

def conversation_chat(query, chain, history):
  result = chain({"question": query, "chat_history": history})
  history.append((query, result["answer"]))
  return result["answer"]

def display_chat_history(chain):
  reply_container = st.container()
  container = st.container()
  with container:
    with st.form(key = "chat_form", clear_on_submit=True):
      user_input = st.text_input("Question:", placeholder="Type your question here", key="input")
      submit = st.form_submit_button(label="Submit", type="primary")
    if submit and user_input:
      with st.spinner("Generating response......"):
        output = conversation_chat(user_input, chain, st.session_state['history'])
        st.session_state['past'].append(user_input)
        st.session_state['generated'].append(output)

  if st.session_state['generated']:
    with reply_container:
      for i in range(len(st.session_state['generated'])):
        message(st.session_state['past'][i], is_user = True, key = str(i) + "_user", avatar_style = 'thumbs')
        message(st.session_state['generated'][i], key = str(i))

def create_conversational_chain(vector_store):
  load_dotenv()
  llm = Replicate(
    streaming = True,
    model = "meta/llama-2-70b-chat",
    callbacks = [StreamingStdOutCallbackHandler()],
    model_kwargs = { " temperature ": 0.5, "max_length": 500, "top_1": 1 }
  )

  memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
  chain = ConversationalRetrievalChain.from_llm(llm = llm, chain_type = "stuff", retriever = vector_store.as_retriever(search_kwargs = { "k": 2 }, memory=memory))

  return chain


def main():
   
   initialize_state()

   st.title ("📚Document AI Chatbot")
   st.sidebar.title("Upload documents")
   uploaded_files = st.sidebar.file_uploader("Upload your docs, pdf etc", accept_multiple_files=True)

   if uploaded_files:
      text = []
      for file in uploaded_files:
        file_extension = os.path.splitext(file.name)[1]
        with tempfile.NamedTemporaryFile(delete = False) as temp:
          temp.write(file.read())
          temp_path = temp.name

      loader = None
      if file_extension == ".pdf":
        loader = PyPDFLoader(temp_path)
      elif file_extension == ".docx" or file_extension == ".doc":
        loader = Docx2txtLoader(temp_path)
      elif file_extension == ".txt":
        loader = TextLoader(temp_path)

      if loader:
         text.extend(loader.load())
         os.remove(temp_path)
  
      text_splitter = CharacterTextSplitter(separator = "\n", chunk_size = 1000, chunk_overlap =  100, length_function = len)
      text_chunks = text_splitter.split_documents(text)

      # Embeddings 

      embeddings = HuggingFaceEmbeddings(model_name = "sentence-transformers/all-MiniLM-L6-v2", model_kwargs = { 'device': "cpu"})

      # Vector Store

      vector_store = FAISS.from_documents(text_chunks, embedding = embeddings)

      # Conversational Chain

      chain = create_conversational_chain(vector_store)

      display_chat_history(chain)


if __name__ == "__main__":
    main()