pages/2_Consult.py

# /home/user/app/pages/2_Consult.py
import streamlit as st
from langchain_core.messages import HumanMessage, AIMessage, SystemMessage # Ensure SystemMessage is imported
from datetime import datetime
from typing import List, Optional, Dict, Any
from sqlmodel import select

from config.settings import settings
from agent import get_agent_executor # This now returns the Gemini-based agent
from models import ChatMessage, ChatSession
from models.db import get_session_context
from services.logger import app_logger
from services.metrics import log_consultation_start

# --- Authentication Check ---
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:
        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 ---
try:
    agent_executor = get_agent_executor() # Gets the Gemini agent executor
    app_logger.info("Gemini-based agent executor initialized for Consult page.")
except ValueError as e: # Catch specific error from get_agent_executor if API key is missing
    st.error(f"AI Agent Initialization Error: {e}")
    app_logger.critical(f"Fatal: AI Agent could not be initialized in Consult page: {e}", exc_info=True)
    st.info("Please ensure the necessary API keys (e.g., Google API Key for Gemini) are configured in the application settings.")
    st.stop()
except Exception as e:
    st.error(f"An unexpected error occurred while initializing the AI Agent: {e}")
    app_logger.critical(f"Fatal: Unexpected AI Agent initialization error: {e}", exc_info=True)
    st.stop()


# --- Session State for Consult Page ---
if 'current_consult_patient_context' not in st.session_state:
    st.session_state.current_consult_patient_context = {}
if 'consult_context_submitted' not in st.session_state:
    st.session_state.consult_context_submitted = False

# --- Helper Functions ---
@st.cache_data(ttl=30, show_spinner=False)
def load_chat_history_for_agent(session_id: int) -> List: # List of LangChain messages
    messages = []
    app_logger.debug(f"Loading agent chat history for session_id: {session_id}")
    with get_session_context() as db:
        statement = select(ChatMessage).where(ChatMessage.session_id == session_id).order_by(ChatMessage.timestamp)
        db_messages = db.exec(statement).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))
            elif msg.role == "system": # Include system messages in agent history if they were saved
                messages.append(SystemMessage(content=msg.content))
    app_logger.debug(f"Loaded {len(messages)} messages for agent history for session {session_id}.")
    return messages

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}")
    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) # Commit handled by context manager
    app_logger.info(f"Message saved to DB for session {session_id}. Role: {role}.")

def update_chat_session_with_context_summary(session_id: int, context_summary: str):
    with get_session_context() as db:
        session_to_update = db.get(ChatSession, session_id)
        if session_to_update:
            session_to_update.patient_context_summary = context_summary
            db.add(session_to_update) # Stage for commit
            app_logger.info(f"Updated ChatSession {session_id} with patient context summary.")
        else:
            app_logger.error(f"Could not find ChatSession {session_id} to update with context summary.")

# --- Page Logic ---
st.title("AI Consultation Room")
st.markdown(f"Interacting as: **{authenticated_username}**")
st.info(f"{settings.MAIN_DISCLAIMER_SHORT} Remember to use only anonymized, simulated data.")

chat_session_id = st.session_state.get("current_chat_session_id")
if not chat_session_id:
    st.error("No active chat session ID. This can occur if a session wasn't properly created on login. Please try logging out and then logging back in. If the problem persists, contact support.")
    app_logger.error(f"User '{authenticated_username}' (ID: {authenticated_user_id}) on Consult page with NO current_chat_session_id.")
    st.stop()

# --- Patient Context Input Form ---
if not st.session_state.consult_context_submitted:
    st.subheader("Step 1: Provide Patient Context (Optional, Simulated Data Only)")
    with st.form(key="patient_context_form_consult"):
        st.markdown("**Reminder: Use only anonymized, simulated data for this demonstration.**")
        age = st.number_input("Patient Age (Simulated)", min_value=0, max_value=120, step=1, value=None) # Default to None
        gender_options = ["Not Specified", "Male", "Female", "Other"]
        gender = st.selectbox("Patient Gender (Simulated)", gender_options, index=0)
        chief_complaint = st.text_area("Chief Complaint / Reason for Consult (Simulated)", height=100, placeholder="e.g., Persistent cough for 2 weeks")
        key_history = st.text_area("Key Medical History (Simulated)", height=100, placeholder="e.g., Type 2 Diabetes, Hypertension, Asthma")
        current_meds = st.text_area("Current Medications (Simulated)", height=100, placeholder="e.g., Metformin 500mg BID, Lisinopril 10mg OD")
        submit_context_button = st.form_submit_button("Start Consult with this Context")

        if submit_context_button:
            context_dict = {
                "age": age if age is not None and age > 0 else None, # Store None if not specified
                "gender": gender if gender != "Not Specified" else None,
                "chief_complaint": chief_complaint.strip() or None,
                "key_medical_history": key_history.strip() or None,
                "current_medications": current_meds.strip() or None,
            }
            # Filter out None values for the summary string
            valid_context_parts = {k: v for k, v in context_dict.items() if v is not None}
            st.session_state.current_consult_patient_context = valid_context_parts # Store the filtered dict

            if valid_context_parts:
                context_summary_str_parts = [f"{k.replace('_', ' ').title()}: {v}" for k, v in valid_context_parts.items()]
                context_summary_for_db_and_agent = "; ".join(context_summary_str_parts)
            else:
                context_summary_for_db_and_agent = "No specific patient context provided for this session."
            
            update_chat_session_with_context_summary(chat_session_id, context_summary_for_db_and_agent)
            
            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] = []
            
            # Don't add patient context as a SystemMessage if it's passed as a variable to invoke
            # The agent's main system prompt will now include a placeholder for it.
            # However, we save it to DB for record keeping.
            if valid_context_parts: # Save a system message indicating context was provided
                 save_chat_message_to_db(chat_session_id, "system", f"Initial Patient Context Provided: {context_summary_for_db_and_agent}")

            st.session_state.consult_context_submitted = True
            app_logger.info(f"Patient context submitted for session {chat_session_id}: {context_summary_for_db_and_agent}")
            st.rerun()
    st.stop()

# --- Chat Interface (Shown after context is submitted or if skipped by some other logic not yet present) ---
st.subheader("Step 2: Interact with AI Health Navigator")
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]: # If history is empty
        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 metric: {e}")
        
        initial_ai_message_content = "Hello! I am your AI Health Navigator. How can I assist you today?"
        if st.session_state.get('current_consult_patient_context'):
            initial_ai_message_content += " I have noted the patient context you provided."
        
        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)
        app_logger.info(f"Initialized new consultation (session {chat_session_id}) with a greeting.")

# Display chat messages for UI
with st.container():
    with get_session_context() as db:
        stmt = select(ChatMessage).where(ChatMessage.session_id == chat_session_id).order_by(ChatMessage.timestamp)
        ui_messages = db.exec(stmt).all()
        for msg in ui_messages:
            if msg.role == "system": continue # Don't display system context messages directly
            avatar = "🧑‍⚕️" if msg.role == "assistant" else "👤"
            if msg.role == "tool": avatar = "🛠️" # Assuming you might log tool calls this way
            with st.chat_message(msg.role, avatar=avatar):
                st.markdown(msg.content) # Potentially enhance to show sources/confidence if agent provides

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:
                # Prepare patient context string for the agent, if any was provided
                patient_context_dict = st.session_state.get('current_consult_patient_context', {})
                if patient_context_dict:
                    context_parts_for_invoke = [f"{k.replace('_', ' ').title()}: {v}" for k, v in patient_context_dict.items()]
                    patient_context_str_for_invoke = "; ".join(context_parts_for_invoke)
                else:
                    patient_context_str_for_invoke = "No specific patient context was provided for this interaction."

                invoke_payload = {
                    "input": prompt,
                    "chat_history": st.session_state[agent_history_key],
                    "patient_context": patient_context_str_for_invoke # Pass to agent
                }
                app_logger.debug(f"Invoking agent with payload: {invoke_payload}")

                response = agent_executor.invoke(invoke_payload)
                
                ai_response_content = response.get('output', "I could not generate a valid response.")
                if not isinstance(ai_response_content, str): ai_response_content = str(ai_response_content)
                
                app_logger.info(f"Agent response for session {chat_session_id}: '{ai_response_content[:100]}...'")
                st.markdown(ai_response_content) # Display AI response
                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)
                # The user-facing error was: "Sorry, an error occurred: ValidationError. Please try again."
                # Let's try to be a bit more specific if we can, or keep it generic but log details.
                error_type_name = type(e).__name__ # e.g., "ValidationError", "APIError"
                user_friendly_error_message = f"Sorry, an error occurred ({error_type_name}). Please try rephrasing your query or contact support if the issue persists."
                st.error(user_friendly_error_message)
                
                # Save a representation of the error to DB for the assistant's turn
                db_error_message = f"System encountered an error: {error_type_name} while processing user query. Details logged."
                save_chat_message_to_db(chat_session_id, "assistant", db_error_message)
                # Add error representation to agent history so it's aware for next turn (optional)
                st.session_state[agent_history_key].append(AIMessage(content=f"Note to self: Encountered an error ({error_type_name}) on the previous turn."))