Create app.py

app.py

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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.getenv("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("Conversational Medical 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
+if user_input := st.chat_input("Describe your symptoms or ask a medical question:"):
+    # Add user message to chat history
+    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)