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import streamlit as st
import chatbot_bedrock as demo_chat
from transformers import AutoModelForCausalLM, AutoTokenizer
st.title("Hi, I am Chatbot Philio :mermaid:")
st.write("I am your hotel booking assistant for today.")
# tokenizer = AutoTokenizer.from_pretrained("KvrParaskevi/Hotel-Assistant-Attempt4-Llama-2-7b")
# [theme]
# base="light"
# primaryColor="#6b4bff"
tokenizer, model = demo_chat.load_model()
model_wrapper = demo_chat.HuggingFaceModelWrapper(model, tokenizer)
#Application
with st.container():
st.markdown('<div class="scrollable-div">', unsafe_allow_html=True)
#Langchain memory in session cache
if 'memory' not in st.session_state:
st.session_state.memory = demo_chat.demo_miny_memory(model)
#Check if chat history exists in this session
if 'chat_history' not in st.session_state:
st.session_state.chat_history = [ ] #Initialize chat history
if 'model' not in st.session_state:
st.write("Model added in state.")
st.session_state.model = model
#renders chat history
for message in st.session_state.chat_history:
with st.chat_message(message["role"]):
st.write(message["content"])
#Set up input text field
input_text = st.chat_input(placeholder="Here you can chat with Llamma 2 model.")
if input_text:
with st.chat_message("user"):
st.write(input_text)
st.session_state.chat_history.append({"role" : "user", "content" : input_text}) #append message to chat history
chat_response = demo_chat.demo_chain(input_text=input_text, memory=st.session_state.memory, model= model_wrapper)
first_answer = chat_response.split("Human")[0] #Because of Predict it prints the whole conversation.Here we seperate the first answer only.
with st.chat_message("assistant"):
st.write(first_answer)
st.session_state.chat_history.append({"role": "assistant", "content": first_answer})
st.markdown('</div>', unsafe_allow_html=True) |