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import os
import streamlit as st
import chatbot 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, model = demo_chat.load_model()
pipeline = demo_chat.load_pipeline()
scrollable_div_style = """
<style>
.scrollable-div {
height: 200px; /* Adjust the height as needed */
overflow-y: auto; /* Enable vertical scrolling */
padding: 5px;
border: 1px solid #ccc; /* Optional: adds a border around the div */
border-radius: 5px; /* Optional: rounds the corners of the border */
}
</style>
"""
def render_chat_history(chat_history):
#renders chat history
for message in chat_history:
if(message["role"]!= "system"):
with st.chat_message(message["role"]):
st.markdown(message["content"])
def generate_response(chat_history):
tokenized_chat = tokenizer.apply_chat_template(chat_history, tokenize=True, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(tokenized_chat, do_sample =True, max_new_tokens=50, temperature = 0.3, top_p = 0.85)
answer = tokenizer.decode(outputs[0][tokenized_chat.shape[1]:],skip_special_tokens=True)
final_answer = answer.split("<")[0]
return final_answer
#Application
#Langchain memory in session cache
if 'memory' not in st.session_state:
st.session_state.memory = demo_chat.demo_miny_memory(model)
system_content = "You are a friendly chatbot who always helps the user book a hotel room based on his/her needs.Based on the current social norms you wait for the user's response to your proposals."
#Check if chat history exists in this session
if 'chat_history' not in st.session_state:
st.session_state.chat_history = [
{
"role": "system",
"content": system_content,
},
{"role": "assistant", "content": "Hello, how can I help you today?"},
] #Initialize chat history
if 'model' not in st.session_state:
st.session_state.model = model
st.markdown('<div class="scrollable-div">', unsafe_allow_html=True) #add css style to container
render_chat_history(st.session_state.chat_history)
#Input field for chat interface
if input_text := st.chat_input(placeholder="Here you can chat with our hotel booking model."):
with st.chat_message("user"):
st.markdown(input_text)
st.session_state.chat_history.append({"role" : "user", "content" : input_text}) #append message to chat history
with st.spinner("Generating response..."):
first_answer = demo_chat.generate_from_pipeline(st.session_state.chat_history, pipeline)
with st.chat_message("assistant"):
st.markdown(first_answer)
st.session_state.chat_history.append({"role": "assistant", "content": first_answer})
st.markdown('</div>', unsafe_allow_html=True)
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