Update pages/2_Consult.py

pages/2_Consult.py

@@ -1,6 +1,6 @@
 # /home/user/app/pages/2_Consult.py
 import streamlit as st
-from langchain_core.messages import HumanMessage, AIMessage, SystemMessage # Ensure SystemMessage is imported
+from langchain_core.messages import HumanMessage, AIMessage, SystemMessage # Ensure all are imported
 from datetime import datetime
 from typing import List, Optional, Dict, Any
 from sqlmodel import select
@@ -29,192 +29,173 @@ app_logger.info(f"User '{authenticated_username}' (ID: {authenticated_user_id})
 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
+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 necessary API keys (e.g., Google API Key for Gemini) are configured in the application settings.")
+    st.info("Please ensure API keys (e.g., Google API Key for Gemini) are configured.")
     st.stop()
-except Exception as e:
+except Exception as e: # Catch any other unexpected error during agent init
     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 'current_consult_patient_context_dict' not in st.session_state: # Renamed for clarity
+    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)
-def load_chat_history_for_agent(session_id: int) -> List: # List of LangChain messages
+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:
         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}.")
+            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))
+    app_logger.debug(f"Loaded {len(messages)} LangChain messages for agent history (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(
+        chat_message_obj = 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
+        db.add(chat_message_obj)
     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):
+def update_chat_session_with_context_summary_in_db(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.")
+            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} to update with context summary.")
+            app_logger.error(f"Could not find ChatSession {session_id} to update 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.")
+st.info(f"{settings.MAIN_DISCLAIMER_SHORT} Remember to use only anonymized, simulated data for this demonstration.")
 
 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.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()
 
 # --- 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"):
+    with st.form(key="patient_context_form_consult_page"):
         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,
+        age_in = st.number_input("Patient Age (Simulated)", min_value=0, max_value=120, step=1, value=None)
+        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 cleaner context dictionary
+            filtered_context_dict = {
+                k.replace('_', ' ').title(): v for k, v in raw_context.items()
+                if v is not None and str(v).strip() and str(v) != "Not Specified" and (isinstance(v, int) and v > 0 or isinstance(v, str)) # ensure age is >0
             }
-            # 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
+            st.session_state.current_consult_patient_context_dict = filtered_context_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)
+            if filtered_context_dict:
+                context_summary_str = "; ".join([f"{k}: {v}" for k, v in filtered_context_dict.items()])
             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)
+                context_summary_str = "No specific patient context was provided for this session."
             
-            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] = []
+            update_chat_session_with_context_summary_in_db(chat_session_id, context_summary_str)
+            save_chat_message_to_db(chat_session_id, "system", f"Initial Patient Context Provided: {context_summary_str}")
             
-            # 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}")
+            app_logger.info(f"Patient context submitted for session {chat_session_id}: {context_summary_str}")
             st.rerun()
     st.stop()
 
-# --- Chat Interface (Shown after context is submitted or if skipped by some other logic not yet present) ---
+# --- Chat Interface ---
 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
+    if not st.session_state[agent_history_key]: # If history is empty after loading
         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}")
+        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?"
-        if st.session_state.get('current_consult_patient_context'):
-            initial_ai_message_content += " I have noted the patient context you provided."
+        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'): # Check the renamed key
+            initial_ai_msg += " 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.")
+        st.session_state[agent_history_key].append(AIMessage(content=initial_ai_msg))
+        save_chat_message_to_db(chat_session_id, "assistant", initial_ai_msg)
 
-# Display chat messages for UI
-with st.container():
+# Display chat messages from DB for UI
+with st.container(height=400): # Fixed height container for chat, makes it scrollable
     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
+            if msg.role == "system": continue
             avatar = "🧑‍⚕️" if msg.role == "assistant" else "👤"
-            if msg.role == "tool": avatar = "🛠️" # Assuming you might log tool calls this way
+            if msg.role == "tool": avatar = "🛠️"
             with st.chat_message(msg.role, avatar=avatar):
-                st.markdown(msg.content) # Potentially enhance to show sources/confidence if agent provides
+                st.markdown(msg.content)
 
-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))
+# Chat input
+if user_prompt := st.chat_input("Ask the AI... (e.g., 'What is hypertension?')"):
+    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))
 
     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', {})
+                # Prepare patient context string for the agent
+                patient_context_dict = st.session_state.get('current_consult_patient_context_dict', {})
                 if patient_context_dict:
-                    context_parts_for_invoke = [f"{k.replace('_', ' ').title()}: {v}" for k, v in patient_context_dict.items()]
+                    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:
                     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
+                    "input": user_prompt,
+                    "chat_history": st.session_state[agent_history_key], # List of BaseMessage
+                    "patient_context": patient_context_str_for_invoke
                 }
                 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.")
+                ai_response_content = response.get('output', "I could not generate a valid response at this time.")
                 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
+                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)
-                # 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."))
+                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."
+                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: Encountered error ({error_type_name})."))
+    st.rerun() # Rerun to display the new messages immediately