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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) | |