Update streamlit_app.py

streamlit_app.py

@@ -1,61 +1,61 @@
 # streamlit_app.py
-    import streamlit as st
-    import sys
-    import os
-
-    # *** Add these two lines at the very top ***
-    from dotenv import load_dotenv
-    load_dotenv() # Load variables from .env file
-
-    # Add the directory containing app.py to the Python path
-    # This assumes app.py is in the same directory as streamlit_app.py
-    sys.path.append(os.path.dirname(os.path.abspath(__file__)))
-
-    # Import your respond function and any necessary global variables from app.py
-    # Make sure app.py loads the model, tokenizer, etc. when imported
-    try:
-        from app import respond, model_id # Import your main function and model_id
-        # You might also need to import other things if respond relies on globals directly
-        # from app import model, tokenizer, embedder, nlp, data, descriptions, embeddings, ...
-        print("Successfully imported respond function from app.py")
-    except ImportError as e:
-        st.error(f"Error importing core logic from app.py: {e}")
-        st.stop() # Stop the app if the core logic can't be loaded
-
-    # Set Streamlit page config
-    st.set_page_config(page_title="Business Q&A Assistant")
-
-    st.title(f"Business Q&A Assistant with {model_id}")
-    st.write("Ask questions about the business (details from Google Sheet) or general knowledge (via search).")
-
-    # Initialize chat history in Streamlit's session state
-    # Session state persists across reruns for a single user session
-    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"])
-
-    # Accept user input
-    if prompt := st.chat_input("Your Question"):
-        # Add user message to chat history
-        st.session_state.messages.append({"role": "user", "content": prompt})
-        # Display user message in chat message container
-        with st.chat_message("user"):
-            st.markdown(prompt)
-
-        # Get the current chat history in the format your respond function expects
-        # Gradio's history is [(user, bot), (user, bot), ...]
-        # Streamlit's session state is a list of dicts [{"role": "user", "content": "..."}]
-        # We need to convert Streamlit's history format to Gradio's format for your respond function
-        gradio_chat_history = []
-        # Start from the second message if the first was from the system/initial state
-        # Or just iterate through pairs, skipping the latest user prompt for history pass
-        # The respond function expects history *before* the current turn
-        history_for_respond = []
-        # Iterate through messages, excluding the very last user prompt which is the current input
-        for i in range(len(st.session_state.messages) - 1):
-            if st.session_state.messages[i]["role"] == "user" and st.session_state.messages[i+1]["role"] == "assistant":
-                history_for_respond.append((st.session_state.messages[i]["content"], st.st
+import streamlit as st
+import sys
+import os
+
+# *** Add these two lines at the very top ***
+from dotenv import load_dotenv
+load_dotenv() # Load variables from .env file
+
+# Add the directory containing app.py to the Python path
+# This assumes app.py is in the same directory as streamlit_app.py
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+# Import your respond function and any necessary global variables from app.py
+# Make sure app.py loads the model, tokenizer, etc. when imported
+try:
+    from app import respond, model_id # Import your main function and model_id
+    # You might also need to import other things if respond relies on globals directly
+    # from app import model, tokenizer, embedder, nlp, data, descriptions, embeddings, ...
+    print("Successfully imported respond function from app.py")
+except ImportError as e:
+    st.error(f"Error importing core logic from app.py: {e}")
+    st.stop() # Stop the app if the core logic can't be loaded
+
+# Set Streamlit page config
+st.set_page_config(page_title="Business Q&A Assistant")
+
+st.title(f"Business Q&A Assistant with {model_id}")
+st.write("Ask questions about the business (details from Google Sheet) or general knowledge (via search).")
+
+# Initialize chat history in Streamlit's session state
+# Session state persists across reruns for a single user session
+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"])
+
+# Accept user input
+if prompt := st.chat_input("Your Question"):
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    # Display user message in chat message container
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    # Get the current chat history in the format your respond function expects
+    # Gradio's history is [(user, bot), (user, bot), ...]
+    # Streamlit's session state is a list of dicts [{"role": "user", "content": "..."}]
+    # We need to convert Streamlit's history format to Gradio's format for your respond function
+    gradio_chat_history = []
+    # Start from the second message if the first was from the system/initial state
+    # Or just iterate through pairs, skipping the latest user prompt for history pass
+    # The respond function expects history *before* the current turn
+    history_for_respond = []
+    # Iterate through messages, excluding the very last user prompt which is the current input
+    for i in range(len(st.session_state.messages) - 1):
+        if st.session_state.messages[i]["role"] == "user" and st.session_state.messages[i+1]["role"] == "assistant":
+            history_for_respond.append((st.session_state.messages[i]["content"], st.st