Create app.py

app.py

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+import os
+import streamlit as st
+from dotenv import load_dotenv
+from AI_Risk_app import retrieval_augmented_qa_chain  # Importing the RAG chain
+
+
+# Load the .env file
+import streamlit as st
+import openai
+
+# Get OpenAI API key from Streamlit secrets
+openai.api_key = st.secrets["OPENAI_API_KEY"]
+
+# Load environment variables
+load_dotenv()
+
+# Set up the Streamlit interface
+st.title("AI Risk Advisory QA")
+
+# Get the user query
+user_query = st.text_input("Ask your question:")
+
+# Button to trigger the RAG process
+if st.button("Get Answer"):
+    if user_query:
+        # Pass user query through RAG chain
+        result = retrieval_augmented_qa_chain.invoke({"question": user_query})
+
+        # Extract response content from RAG result
+        response_content = result["response"].content
+
+        # Display the response content in the Streamlit app
+        st.write("**Answer:**")
+        st.write(response_content)
+    else:
+        st.write("Please enter a question.")