Update streamlit_app.py

streamlit_app.py

@@ -1,92 +1,61 @@
 # streamlit_app.py
-import streamlit as st
-import sys
-import os
-
-# 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
-    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.session_state.messages[i+1]["content"]))
-
-    # Call your respond function
-    with st.spinner("Thinking..."):
-        # Your respond function returns ("", updated_chat_history)
-        # We only need the updated chat history part
-        # We also need to pass the current user prompt as the first argument
-        # Ensure we are unpacking the result correctly into two variables
-        response_tuple = respond(prompt, history_for_respond)
-        # Check if the returned value is a tuple with at least two elements
-        if isinstance(response_tuple, tuple) and len(response_tuple) >= 2:
-            _, updated_gradio_history = response_tuple
-        else:
-            # Handle the case where the function did not return the expected tuple
-            print(f"Warning: respond function returned unexpected value: {response_tuple}")
-            updated_gradio_history = history_for_respond + [(prompt, "Sorry, I couldn't get a valid response.")]
-
-
-        # The respond function modifies and returns the history in Gradio format.
-        # The last entry in updated_gradio_history should be the current turn's (user_prompt, bot_response).
-        # We need to extract the bot_response part and add it to Streamlit's session state.
-
-        if updated_gradio_history and updated_gradio_history[-1][0] == prompt:
-            bot_response = updated_gradio_history[-1][1]
-        else:
-            # Fallback if the history structure is unexpected
-            bot_response = "Sorry, I couldn't get a response from the model."
-            print("Warning: respond function returned history in an unexpected format.")
-
-
-    # Add assistant response to chat history
-    st.session_state.messages.append({"role": "assistant", "content": bot_response})
-    # Display assistant response in chat message container
-    with st.chat_message("assistant"):
-        st.markdown(bot_response)
-
-# You might want a clear history button similar to Gradio's
-if st.button("Clear Chat History"):
-    st.session_state.messages = []
-    st.experimental_rerun() # Rerun the app to clear the display
+    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