Update pages/2_Consult.py

pages/2_Consult.py

@@ -10,49 +10,45 @@ from agent import get_agent_executor # This now returns the OpenAI-based agent e
 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 # Assuming this function exists
+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") # Redirect to the main login page (app.py)
+        st.switch_page("app.py")
     except st.errors.StreamlitAPIException:
-        # This can happen if st.switch_page is called when not in a multipage app context (e.g. dev)
         st.info("Please navigate to the main login page.")
-    st.stop() # Halt script execution
+    st.stop()
 
 authenticated_user_id = st.session_state.get("authenticated_user_id")
-authenticated_username = st.session_state.get("authenticated_username", "User") # Default to "User"
+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 ---
-# This will now initialize the OpenAI agent via get_agent_executor() from agent.py
 try:
     agent_executor = get_agent_executor()
     app_logger.info("OpenAI-based agent executor initialized successfully for Consult page.")
-except ValueError as e: # Catch specific error from get_agent_executor if API key is missing
+except ValueError as e:
     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 OPENAI_API_KEY is correctly configured in the application settings (Hugging Face Secrets).")
     st.stop()
-except Exception as e: # Catch any other unexpected error during agent init
+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 ---
-# Using more descriptive key for the patient context dictionary
 if 'current_consult_patient_context_dict' not in st.session_state:
     st.session_state.current_consult_patient_context_dict = {}
 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, max_entries=10) # Cache agent history for current session
-def load_chat_history_for_agent(session_id: int) -> List[Any]: # List of LangChain BaseMessage
-    """Loads chat history from DB, formatted for LangChain agent (HumanMessage, AIMessage, SystemMessage)."""
+# --- Helper Functions (load_chat_history_for_agent, save_chat_message_to_db, update_chat_session_with_context_summary_in_db) ---
+# ... (These functions are identical to the previous full 2_Consult.py provided - no changes needed here for this specific error) ...
+@st.cache_data(ttl=30, show_spinner=False, max_entries=10)
+def load_chat_history_for_agent(session_id: int) -> List[Any]:
     messages = []
     app_logger.debug(f"Loading agent chat history from DB for session_id: {session_id}")
     try:
@@ -63,15 +59,13 @@ def load_chat_history_for_agent(session_id: int) -> List[Any]: # List of LangCha
                 if msg.role == "user": messages.append(HumanMessage(content=msg.content))
                 elif msg.role == "assistant": messages.append(AIMessage(content=msg.content))
                 elif msg.role == "system": messages.append(SystemMessage(content=msg.content))
-                # ToolMessages are usually part of agent_scratchpad, not general chat_history for invoke
         app_logger.debug(f"Loaded {len(messages)} LangChain messages for agent history (session {session_id}).")
     except Exception as e:
         app_logger.error(f"Error loading chat history for session {session_id}: {e}", exc_info=True)
-        st.toast(f"Error loading chat history: {e}", icon="⚠️") # Non-blocking error
+        st.toast(f"Error loading history: {e}", icon="⚠️")
     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):
-    """Saves a chat message to the database."""
     app_logger.debug(f"Saving message to DB for session {session_id}: Role='{role}', Content snippet='{content[:50]}...'")
     try:
         with get_session_context() as db:
@@ -79,176 +73,127 @@ def save_chat_message_to_db(session_id: int, role: str, content: str, tool_call_
                 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_obj) # Commit will be handled by the context manager
+            db.add(chat_message_obj)
         app_logger.info(f"Message (Role: {role}) saved to DB for session {session_id}.")
     except Exception as e:
         app_logger.error(f"Error saving chat message to DB for session {session_id}: {e}", exc_info=True)
         st.toast(f"Error saving message: {e}", icon="⚠️")
 
-
 def update_chat_session_with_context_summary_in_db(session_id: int, context_summary: str):
-    """Updates the ChatSession record with the patient context summary."""
     try:
         with get_session_context() as db:
-            session_to_update = db.get(ChatSession, session_id) # Fetch by primary key
+            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) # SQLModel handles add for updates if PK is present
+                db.add(session_to_update)
                 app_logger.info(f"Updated ChatSession {session_id} with patient context summary in DB.")
             else:
                 app_logger.error(f"Could not find ChatSession {session_id} in DB to update context summary.")
     except Exception as e:
         app_logger.error(f"Error updating chat session {session_id} context summary: {e}", exc_info=True)
-        st.toast(f"Error saving context summary: {e}", icon="⚠️")
-
+        st.toast(f"Error saving context: {e}", icon="⚠️")
 
 # --- Page Logic ---
 st.title("AI Consultation Room")
 st.markdown(f"Interacting as: **{authenticated_username}**")
-# Prominent disclaimer on the consult page itself
 st.warning(f"**Reminder & Disclaimer:** {settings.MAIN_DISCLAIMER_LONG} {settings.SIMULATION_DISCLAIMER}")
 
-
 chat_session_id = st.session_state.get("current_chat_session_id")
 if not chat_session_id:
-    st.error("Error: No active chat session ID found. This is unexpected after login. Please try logging out and logging back in. If the problem persists, please contact support.")
+    st.error("Error: No active chat session ID found. Please try logging out and back in.")
     app_logger.critical(f"User '{authenticated_username}' (ID: {authenticated_user_id}) on Consult page encountered MISSING 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, Use Simulated Data Only)")
-    with st.form(key="patient_context_form_consult_page_openai"): # Unique key
-        st.markdown("**Crucial Reminder: Use only anonymized, simulated data for this demonstration. Do NOT enter real PHI.**")
+    # ... (Form logic remains the same as previous full version of 2_Consult.py) ...
+    with st.form(key="patient_context_form_consult_page_openai_v2"): # Unique key
+        st.markdown("**Crucial Reminder: Use only anonymized, simulated data. Do NOT enter real PHI.**")
         age_in = st.number_input("Patient Age (Simulated)", min_value=0, max_value=120, step=1, value=None, help="Leave blank if not applicable.")
-        gender_options = ["Not Specified", "Male", "Female", "Other"]
-        gender_in = st.selectbox("Patient Gender (Simulated)", gender_options, index=0)
-        cc_in = st.text_area("Chief Complaint / Reason for Consult (Simulated)", height=100, placeholder="e.g., Persistent cough for 2 weeks, fatigue")
-        hist_in = st.text_area("Key Medical History (Simulated)", height=100, placeholder="e.g., Type 2 Diabetes (controlled), Hypertension (on medication), Asthma (mild intermittent)")
-        meds_in = st.text_area("Current Medications (Simulated)", height=100, placeholder="e.g., Metformin 500mg BID, Lisinopril 10mg OD, Salbutamol inhaler PRN")
+        gender_in = st.selectbox("Patient Gender (Simulated)", ["Not Specified", "Male", "Female", "Other"], index=0)
+        cc_in = st.text_area("Chief Complaint / Reason for Consult (Simulated)", height=100, placeholder="e.g., Persistent cough")
+        hist_in = st.text_area("Key Medical History (Simulated)", height=100, placeholder="e.g., Type 2 Diabetes")
+        meds_in = st.text_area("Current Medications (Simulated)", height=100, placeholder="e.g., Metformin")
         submit_context_btn = st.form_submit_button("Start Consult with this Context")
 
         if submit_context_btn:
-            raw_context = {
-                "Age": age_in, "Gender": gender_in, "Chief Complaint": cc_in,
-                "Key Medical History": hist_in, "Current Medications": meds_in,
-            }
-            # Filter out None/empty/"Not Specified" values for a cleaner context dictionary
-            filtered_context_dict = {
-                k: v for k, v in raw_context.items()
-                if v is not None and str(v).strip() and \
-                   (isinstance(v, str) and v.lower() != "not specified") and \
-                   (isinstance(v, int) and v > 0 or not isinstance(v, int)) # ensure age > 0 if int
-            }
+            raw_context = {"Age": age_in, "Gender": gender_in, "Chief Complaint": cc_in, "Key Medical History": hist_in, "Current Medications": meds_in}
+            filtered_context_dict = { k: v for k, v in raw_context.items() if v is not None and str(v).strip() and (isinstance(v, str) and v.lower() != "not specified") and (isinstance(v, int) and v > 0 or not isinstance(v, int))}
             st.session_state.current_consult_patient_context_dict = filtered_context_dict
-
-            if filtered_context_dict:
-                context_summary_str = "; ".join([f"{k}: {v}" for k, v in filtered_context_dict.items()])
-            else:
-                context_summary_str = "No specific patient context was provided for this session."
-            
+            context_summary_str = "; ".join([f"{k}: {v}" for k, v in filtered_context_dict.items()]) if filtered_context_dict else "No specific patient context was provided."
             update_chat_session_with_context_summary_in_db(chat_session_id, context_summary_str)
-            # Save a system message to DB indicating context was set for this session
             save_chat_message_to_db(chat_session_id, "system", f"Initial Patient Context Set: {context_summary_str}")
-            
             st.session_state.consult_context_submitted = True
             app_logger.info(f"Patient context submitted for session {chat_session_id}: {context_summary_str}")
-            st.rerun() # Rerun to hide form and proceed to chat interface
-    st.stop() # Don't proceed to chat interface until context form is handled
+            st.rerun()
+    st.stop()
 
-# --- Chat Interface (Shown after context is submitted) ---
+# --- Chat Interface ---
 st.subheader("Step 2: Interact with AI Health Navigator")
-agent_history_key = f"agent_chat_history_{chat_session_id}" # Session-specific key for agent's message history
+agent_history_key = f"agent_chat_history_{chat_session_id}"
 
-# Initialize or load agent's chat history (list of LangChain messages)
 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 after loading from DB
-        try:
-            log_consultation_start(user_id=authenticated_user_id, session_id=chat_session_id)
-        except Exception as e_metric:
-            app_logger.warning(f"Failed to log consultation start metric: {e_metric}")
-        
-        initial_ai_message_content = "Hello! I am your AI Health Navigator. How can I assist you today?"
-        patient_context_for_greeting = st.session_state.get('current_consult_patient_context_dict', {})
-        if patient_context_for_greeting: # Check if the dict itself is non-empty
-            initial_ai_message_content += " I have noted the patient context you provided."
-        
-        # Add initial AI message to agent's history and save to DB
-        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 (fetch fresh from DB for UI consistency)
-# This uses a scrollable container.
+    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_metric: app_logger.warning(f"Failed log_consultation_start: {e_metric}")
+        initial_ai_msg = "Hello! I am your AI Health Navigator. How can I assist you today?"
+        if st.session_state.get('current_consult_patient_context_dict'):
+            initial_ai_msg += " I have noted the patient context you provided."
+        st.session_state[agent_history_key].append(AIMessage(content=initial_ai_msg))
+        save_chat_message_to_db(chat_session_id, "assistant", initial_ai_msg)
+
 chat_display_container = st.container(height=450)
 with chat_display_container:
+    # ... (Chat message display loop - same as previous full version) ...
     with get_session_context() as db:
         stmt = select(ChatMessage).where(ChatMessage.session_id == chat_session_id).order_by(ChatMessage.timestamp)
         ui_messages_from_db = db.exec(stmt).all()
-        for msg in ui_messages_from_db:
-            if msg.role == "system": continue # Don't display raw system messages in chat UI
-            avatar_icon = "🧑‍⚕️" if msg.role == "assistant" else "👤"
-            if msg.role == "tool": avatar_icon = "🛠️" # Example if you log tool messages for UI
-            with st.chat_message(msg.role, avatar=avatar_icon):
-                st.markdown(msg.content) # Add source/confidence here if msg object from DB supports it
-
-# Chat input from user
+        for msg_db in ui_messages_from_db: # Renamed msg to msg_db to avoid conflict
+            if msg_db.role == "system": continue
+            avatar_icon = "🧑‍⚕️" if msg_db.role == "assistant" else "👤"
+            if msg_db.role == "tool": avatar_icon = "🛠️"
+            with st.chat_message(msg_db.role, avatar=avatar_icon):
+                st.markdown(msg_db.content)
+
 if user_prompt := st.chat_input("Ask the AI... (e.g., 'What is hypertension?')"):
-    # Display user message in UI immediately
-    with chat_display_container: # Also add to the container
-        with st.chat_message("user", avatar="👤"):
-            st.markdown(user_prompt)
-    
+    with chat_display_container:
+        with st.chat_message("user", avatar="👤"): st.markdown(user_prompt)
     save_chat_message_to_db(chat_session_id, "user", user_prompt)
     st.session_state[agent_history_key].append(HumanMessage(content=user_prompt))
 
-    # Get AI response
-    with chat_display_container: # Add AI response to the container
+    with chat_display_container:
         with st.chat_message("assistant", avatar="🧑‍⚕️"):
-            thinking_message = st.empty() # Placeholder for "AI is thinking..."
-            thinking_message.markdown("▌") # Simple animated cursor
-            
+            thinking_msg_placeholder = st.empty()
+            thinking_msg_placeholder.markdown("▌")
             try:
-                # Prepare patient context string for the agent
                 patient_context_dict = st.session_state.get('current_consult_patient_context_dict', {})
-                if patient_context_dict: # If there's any context
-                    context_parts_for_invoke = [f"{k}: {v}" for k, v in patient_context_dict.items()]
-                    patient_context_str_for_invoke = "; ".join(context_parts_for_invoke)
-                else: # If no context was provided or all fields were empty/default
-                    patient_context_str_for_invoke = "No specific patient context was provided by the user for this interaction."
-
-                # These are the keys expected by the OpenAI Functions Agent prompt
+                patient_context_str_for_invoke = "; ".join([f"{k}: {v}" for k,v in patient_context_dict.items()]) if patient_context_dict else "No specific patient context provided."
+                
                 invoke_payload = {
                     "input": user_prompt,
-                    "chat_history": st.session_state[agent_history_key], # List of BaseMessage
+                    "chat_history": st.session_state[agent_history_key],
                     "patient_context": patient_context_str_for_invoke
                 }
                 app_logger.debug(f"Invoking OpenAI agent with payload: {invoke_payload}")
-                
-                thinking_message.markdown("AI is thinking...") # Update spinner text
-
+                thinking_msg_placeholder.markdown("AI is thinking...")
                 response = agent_executor.invoke(invoke_payload)
-                
-                ai_response_content = response.get('output', "I could not generate a valid response at this time.")
+                ai_response_content = response.get('output', "Could not generate a response.")
                 if not isinstance(ai_response_content, str): ai_response_content = str(ai_response_content)
                 
                 app_logger.info(f"OpenAI Agent response for session {chat_session_id}: '{ai_response_content[:100]}...'")
-                thinking_message.empty() # Clear "thinking..." message
-                st.markdown(ai_response_content) # Display AI response
-                
+                thinking_msg_placeholder.empty()
+                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 OpenAI agent invocation for session {chat_session_id}: {e}", exc_info=True)
                 error_type_name = type(e).__name__
-                user_friendly_error = f"Sorry, an error occurred ({error_type_name}). Please try rephrasing your query or contact support if the issue persists."
-                thinking_message.empty() # Clear "thinking..." message
-                st.error(user_friendly_error) # Display error in the AI's bubble
-                
+                user_friendly_error = f"Sorry, an error occurred ({error_type_name}). Please try rephrasing or contact support."
+                thinking_msg_placeholder.empty()
+                st.error(user_friendly_error)
                 db_error_msg = f"System encountered an error: {error_type_name}. Details logged."
                 save_chat_message_to_db(chat_session_id, "assistant", db_error_msg)
-                st.session_state[agent_history_key].append(AIMessage(content=f"Note to self: Encountered error ({error_type_name})."))
-    
-    st.rerun() # Rerun to ensure the new messages are at the bottom of the scrollable container
+                st.session_state[agent_history_key].append(AIMessage(content=f"Note: Error ({error_type_name})."))
+    st.rerun()