Create streamlit_app.py

streamlit_app.py

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+# 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
+    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.session_state.messages[i+1]["content"]))
+        # Handle cases where the last turn was user, but no assistant response yet (e.g., previous session)
+        elif st.session_state.messages[i]["role"] == "user" and (i+1 == len(st.session_state.messages) or st.session_state.messages[i+1]["role"] == "user"):
+            # If the last message was user and there's no following assistant message,
+            # this user message is part of the current turn's input, not history.
+            # This part of the loop structure might need adjustment based on how history is built
+            pass # Skip the current user prompt
+
+
+    # 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
+        _, updated_gradio_history = respond(prompt, history_for_respond) # Call your core logic
+
+        # 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