streamlit_app.py

import streamlit as st
from llama_index.core import VectorStoreIndex, ServiceContext, Document
from llama_index.llms.openai import OpenAI
import openai
from llama_index.core import SimpleDirectoryReader
import random
from button_response import button_responses


secrets= "secret.toml"
st.set_page_config(page_title="Chat with our Ai virtaul assistant", page_icon="🦙", layout="centered", initial_sidebar_state="auto", menu_items=None)
openai.api_key = st.secrets.openai_key
st.title("Chat with our virtaul assistant.")
         
if "messages" not in st.session_state.keys(): # Initialize the chat messages history
    st.session_state.messages = [
        {"role": "assistant", "content": "Welcome. I am here to help you with buying a mobile phone."}
    ]
    
# predefined_queries = [
#     "Can you tell me more about the camera specifications?",
#     "What are the available color options?",
#     "I'd like to know about the battery life of this phone.",
#     # Add more queries as needed
# ]

@st.cache_resource(show_spinner=False)
def load_data():
    with st.spinner(text="Processing please wait. This should take 1-2 minutes."):
        reader = SimpleDirectoryReader(input_dir="./data", recursive=True)
        docs = reader.load_data()
        service_context = ServiceContext.from_defaults(llm=OpenAI(model="gpt-3.5-turbo", temperature=0.5, system_prompt='''Act as a virtual shop assistant which try to sell the mobile phones to user.
                                                                  Also help assisting question to user after the response regarding the next step only when needed.
                                                                  only answer based on the data provided.
                                                                  if user does not specify speficic detail then consider giving the answer based on your current response.
                                                                  
                                                                  do not ask user for specific feature repeatedly for each product provide all the details when needed.
                                                                  
                                                                  ask user to visit the product page when they want to buy a product.
                                                                
                                                                  
                                                                  .'''))
        index = VectorStoreIndex.from_documents(docs, service_context=service_context)
        return index

index = load_data()

if "chat_engine" not in st.session_state.keys(): # Initialize the chat engine
        st.session_state.chat_engine = index.as_chat_engine(chat_mode="condense_question", verbose=True)

if prompt := st.chat_input("Your question"): # Prompt for user input and save to chat history
    st.session_state.messages.append({"role": "user", "content": prompt})

for message in st.session_state.messages: # Display the prior chat messages
    with st.chat_message(message["role"]):
        st.write(message["content"])

# If last message is not from assistant, generate a new response
if st.session_state.messages[-1]["role"] != "assistant":
    with st.chat_message("assistant"):
        with st.spinner("Thinking..."):
            response = st.session_state.chat_engine.chat(prompt)
            st.write(response.response)
            message = {"role": "assistant", "content": response.response}
            st.session_state.messages.append(message) # Add response to message history
            
button_responses(st.session_state.messages)
                      
# Check if the last message is from the assistant
if st.session_state.messages[-1]["role"] == "assistant" and len(st.session_state.messages) == 1:
    # Define button texts and their corresponding predefined queries
    button_queries = {
        "Check available Brands": '''We offer following brands:
        Samsung
        , realme
        , oppo
        , Apple
        , Nokia
        and many other brands. please specify which brand would you like check out''',        
    }
    
    # Display each button and handle its click event
    for button_text, predefined_query in button_queries.items():
        if st.button(button_text):
            with st.chat_message("user"):
                    st.write(button_text)
                    message = {"role": "user", "content": button_text}
                    st.session_state.messages.append(message)
            with st.chat_message("assistant"):
                with st.spinner("Thinking..."):
                    st.write(predefined_query)
                    message = {"role": "assistant", "content": predefined_query}
                    st.session_state.messages.append(message)