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import streamlit as st
from chatbot import RAGChatbot
import os
from dotenv import load_dotenv
import warnings
warnings.filterwarnings("ignore")
# Load environment variables from .env file
load_dotenv()
# Initialize chatbot and load session state for messages
@st.cache_resource
def initialize_chatbot():
chatbot = RAGChatbot(
pinecone_api_key=os.getenv('PINECONE_API_KEY'),
index_name='test',
)
# chatbot.ingest_data('./Data', empty=False)
return chatbot
chatbot = initialize_chatbot()
st.title("RAG Chatbot")
# Initialize session state for messages if it doesn't exist
if "messages" not in st.session_state:
st.session_state.messages = [
{"role": "assistant", "content": "Hi! I am Wagner, a highly intelligent and friendly AI assistant. I am developed to provide answers related to Daniel and Daniel's work"}
]
# Display chat history with icons for user and bot
def display_chat_messages():
for message in st.session_state.messages:
if message["role"] == "user":
with st.chat_message(message["role"], avatar="👤"): # User icon
st.markdown(message["content"])
elif message["role"] == "assistant":
with st.chat_message(message["role"], avatar="🤖"): # Bot icon
st.markdown(message["content"])
# Call the function to display past messages
display_chat_messages()
# Input prompt from the user, placed below the past messages
prompt = st.chat_input("Ask me anything!")
# If there's a prompt, send it to the chatbot and get the response
if prompt:
# Add user input to the message history
st.session_state.messages.append({"role": "user", "content": prompt})
# Display the user message immediately in the chat
with st.chat_message("user", avatar="👤"):
st.markdown(prompt)
# Get chatbot response
response, sources = chatbot.query_chatbot(prompt, k=15, rerank=True, past_messages=st.session_state.messages)
# Add chatbot response to the message history
st.session_state.messages.append({"role": "assistant", "content": response})
# Display the bot's response immediately after user input
with st.chat_message("assistant", avatar="🤖"):
st.markdown(response)
# Optionally display relevant documents with metadata
if prompt and sources:
st.subheader("Relevant Documents")
if type(sources) != str:
docs = sources
for i, doc in enumerate(docs):
st.write(f"**Document {i+1}:**")
st.json({"source": doc})
elif type(sources) == str and sources != 'None':
st.write(f"**Document {1}:**")
st.json({"source": sources}) |