code: import streamlit as st
  import langchain
  from langchain_community.chat_models import ChatOllama
  from langchain.cache import InMemoryCache
  from dotenv import load_dotenv
  from langchain_community.embeddings import OllamaEmbeddings
  import os
  from PIL import Image
  from chroma_main import answer_no_retriever
  
  langchain.cache =  InMemoryCache()
  
  load_dotenv()
  
  CHROMA_DB = "./chroma_db"
  MODEL = os.getenv("MODEL", "llama2")
  
  OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434")
  llm = ChatOllama(base_url=OLLAMA_BASE_URL, model=MODEL, temperature=0.0)
  ollama_embeddings = OllamaEmbeddings(base_url=OLLAMA_BASE_URL, model="codellama")
  
  
  
  st.button("clear history", type="primary")    
  if st.button:
          st.session_state.messages = []
  
  
  if "messages" not in st.session_state:
      st.session_state.messages = []
  
  for message in st.session_state.messages:
      with st.chat_message(message["role"]):
          st.markdown(message["content"])
  
  
  if prompt := st.chat_input("What is your query?"):
      st.session_state.messages.append({"role": "user", "content": prompt})
      st.chat_message("user").markdown(prompt)    
      
      response = answer_no_retriever(prompt)
  
      with st.chat_message("assistant"):
          message_placeholder = st.empty()
          # message_placeholder.markdown(response + "▌")
          message_placeholder.markdown(response)
      st.session_state.messages.append({"role": "assistant", "content": response})
      
  
apis: langchain_community.embeddings.OllamaEmbeddings langchain_community.chat_models.ChatOllama langchain.cache.InMemoryCache
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