pages/2_Consult.py

# /home/user/app/pages/2_Consult.py
import streamlit as st
from langchain_core.messages import HumanMessage, AIMessage # SystemMessage, ToolMessage removed if not used directly
from datetime import datetime
from typing import List, Optional
from sqlmodel import select # <--- IMPORT SELECT FOR SQLMODEL QUERIES

from config.settings import settings
from agent import get_agent_executor
from models import ChatMessage, ChatSession, User # User not directly used if ID is sufficient
from models.db import get_session_context
from services.logger import app_logger
from services.metrics import log_consultation_start

# --- Auth Check (same as before) ---
if not st.session_state.get("authenticated_user_id"):
    st.warning("Please log in to access the consultation page.")
    try:
        st.switch_page("app.py")
    except st.errors.StreamlitAPIException: # Catch specific error if needed
        st.info("Please navigate to the main login page.")
    st.stop()

authenticated_user_id = st.session_state.get("authenticated_user_id")
authenticated_username = st.session_state.get("authenticated_username", "User")
app_logger.info(f"User {authenticated_username} (ID: {authenticated_user_id}) accessed Consult page.")

# --- Initialize Agent (same as before) ---
try:
    agent_executor = get_agent_executor()
except Exception as e:
    st.error(f"Could not initialize AI Agent: {e}")
    app_logger.critical(f"AI Agent initialization failed: {e}", exc_info=True)
    st.stop()

# --- Helper Functions ---
@st.cache_data(ttl=60)
def load_chat_history_for_agent(session_id: int) -> List:
    messages = []
    app_logger.debug(f"Loading agent chat history for session_id: {session_id}")
    with get_session_context() as db: # db is a SQLModel Session
        # --- SQLMODEL QUERY ---
        statement = select(ChatMessage).where(ChatMessage.session_id == session_id).order_by(ChatMessage.timestamp)
        db_messages_results = db.exec(statement)
        db_messages = db_messages_results.all()
        # --------------------
        for msg in db_messages:
            if msg.role == "user":
                messages.append(HumanMessage(content=msg.content))
            elif msg.role == "assistant":
                messages.append(AIMessage(content=msg.content))
            # Add ToolMessage handling if you store and use them
            # elif msg.role == "tool" and hasattr(msg, 'tool_call_id') and msg.tool_call_id:
            #     messages.append(ToolMessage(content=msg.content, tool_call_id=str(msg.tool_call_id)))
    app_logger.debug(f"Loaded {len(messages)} messages for agent history for session {session_id}.")
    return messages

# save_chat_message_to_db remains the same as it's performing an insert, not a query.

# --- Page Logic ---
st.title("AI Consultation Room")
st.markdown(f"Interacting as: **{authenticated_username}**")

chat_session_id = st.session_state.get("current_chat_session_id")

if not chat_session_id:
    st.error("No active chat session ID found. Please try logging out and back in.")
    app_logger.error(f"User {authenticated_username} on Consult page with no current_chat_session_id.")
    st.stop()

agent_history_key = f"agent_chat_history_{chat_session_id}"
if agent_history_key not in st.session_state:
    st.session_state[agent_history_key] = load_chat_history_for_agent(chat_session_id)
    if not st.session_state[agent_history_key]:
        try:
            log_consultation_start(user_id=authenticated_user_id, session_id=chat_session_id)
        except Exception as e:
            app_logger.warning(f"Failed to log consultation start: {e}")
        initial_ai_message_content = "Hello! I am your AI Health Navigator. How can I assist you today?"
        st.session_state[agent_history_key].append(AIMessage(content=initial_ai_message_content))
        # save_chat_message_to_db(chat_session_id, "assistant", initial_ai_message_content) # save_chat_message_to_db defined elsewhere
        # Ensure save_chat_message_to_db is defined or called correctly. For this example, it's:
        with get_session_context() as db:
            chat_msg_obj = ChatMessage(session_id=chat_session_id, role="assistant", content=initial_ai_message_content)
            db.add(chat_msg_obj) # commit handled by context manager
        app_logger.info(f"Initialized new consultation for session {chat_session_id} with a greeting.")


# Display chat messages for UI
with st.container():
    with get_session_context() as db: # db is a SQLModel Session
        # --- SQLMODEL QUERY ---
        statement = select(ChatMessage).where(ChatMessage.session_id == chat_session_id).order_by(ChatMessage.timestamp)
        ui_messages_results = db.exec(statement)
        ui_messages = ui_messages_results.all()
        # --------------------
        for msg in ui_messages:
            avatar = "🧑‍⚕️" if msg.role == "assistant" else "👤"
            if msg.role == "tool": avatar = "🛠️"
            with st.chat_message(msg.role, avatar=avatar):
                st.markdown(msg.content)

# Chat input and AI response logic (remains largely the same as it calls agent_executor and save_chat_message_to_db)
# ... (rest of 2_Consult.py from the previous good version)
# For brevity, I'm omitting the chat input and AI response handling section as it primarily
# uses `agent_executor.invoke` and `save_chat_message_to_db`, which itself doesn't change syntax for inserts.

def save_chat_message_to_db(session_id: int, role: str, content: str, tool_call_id: Optional[str]=None, tool_name: Optional[str]=None):
    app_logger.debug(f"Saving message to DB for session {session_id}: Role={role}, Content snippet='{content[:50]}...'")
    with get_session_context() as db:
        chat_message = ChatMessage(
            session_id=session_id,
            role=role,
            content=content,
            timestamp=datetime.utcnow(),
            tool_call_id=tool_call_id,
            tool_name=tool_name
        )
        db.add(chat_message)
        # db.commit() # Handled by context manager
        app_logger.info(f"Message saved to DB for session {session_id}. Role: {role}.")

if prompt := st.chat_input("Ask the AI..."):
    with st.chat_message("user", avatar="👤"):
        st.markdown(prompt)
    save_chat_message_to_db(chat_session_id, "user", prompt)
    st.session_state[agent_history_key].append(HumanMessage(content=prompt))

    with st.chat_message("assistant", avatar="🧑‍⚕️"):
        with st.spinner("AI is thinking..."):
            try:
                response = agent_executor.invoke({
                    "input": prompt,
                    "chat_history": st.session_state[agent_history_key]
                })
                ai_response_content = response.get('output', "No output from AI.")
                if not isinstance(ai_response_content, str):
                    ai_response_content = str(ai_response_content)
                st.markdown(ai_response_content)
                save_chat_message_to_db(chat_session_id, "assistant", ai_response_content)
                st.session_state[agent_history_key].append(AIMessage(content=ai_response_content))
            except Exception as e:
                app_logger.error(f"Error during agent invocation for session {chat_session_id}: {e}", exc_info=True)
                error_message_user = f"Sorry, I encountered an error: {type(e).__name__}"
                st.error(error_message_user)
                save_chat_message_to_db(chat_session_id, "assistant", f"Error processing request: {type(e).__name__}")
                st.session_state[agent_history_key].append(AIMessage(content=f"Observed internal error: {type(e).__name__}"))