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import os
import streamlit as st
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
StorageContext,
load_index_from_storage,
)
from dotenv import load_dotenv
import openai
# Load environment variables
load_dotenv()
# Set OpenAI API key
openai.api_key = os.environ['OPENAI_API_KEY']
# Define the storage directory
PERSIST_DIR = "./storage"
# Check if storage already exists and load or create the index
if not os.path.exists(PERSIST_DIR):
# Load the documents and create the index
documents = SimpleDirectoryReader(
"data",
exclude_hidden=False,
).load_data()
index = VectorStoreIndex.from_documents(documents)
# Store it for later
index.storage_context.persist(persist_dir=PERSIST_DIR)
else:
# Load the existing index
storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
index = load_index_from_storage(storage_context)
# Create a QueryEngine for Retrieval & Augmentation
query_engine = index.as_query_engine()
# Streamlit app
st.title("RAG-Based Homeopathic Chat Assistant")
def get_medical_llm_response(query):
# Generate response from the specialized medical LLM
response = openai.chat.completions.create(
model="gpt-3.5-turbo", # Assuming this is a more evolved model suited for medical queries
messages=[
{"role": "system", "content": "You are an expert in Homeopathic treatment with advanced training on medicine and diagnosis."},
{"role": "user", "content": query}
]
)
return response.choices[0].message.content.strip()
# Initialize session state for chat history
if 'messages' not in st.session_state:
st.session_state.messages = []
# Display chat messages from history on app rerun
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Get user input
user_query_prefix = "Suggest all possible diagnosis, remedies and medicines with potency & dosage for symptoms combining "
if user_input := st.chat_input("Enter the symptoms separated by comma"):
# Add user message to chat history
user_input = user_query_prefix + user_input
st.session_state.messages.append({"role": "user", "content": user_input})
with st.chat_message("user"):
st.markdown(user_input)
with st.spinner('Generating response...'):
# Get the RAG-based response
rag_response = query_engine.query(user_input).response
# Combine RAG response with LLM response
combined_query = f"Based on the following information, provide a comprehensive response:\n\n{rag_response}\n\nUser's query: {user_input}"
llm_response = get_medical_llm_response(combined_query)
# Add assistant message to chat history
st.session_state.messages.append({"role": "assistant", "content": llm_response})
with st.chat_message("assistant"):
st.markdown(llm_response)
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