agent.py

# agent.py
import requests
import json
import re
import os
import operator
import traceback
from functools import lru_cache

from langchain_groq import ChatGroq
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.messages import HumanMessage, SystemMessage, AIMessage, ToolMessage
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.tools import tool
from langgraph.prebuilt import ToolExecutor
from langgraph.graph import StateGraph, END

from typing import Optional, List, Dict, Any, TypedDict, Annotated

# --- Environment Variable Loading ---
UMLS_API_KEY = os.environ.get("UMLS_API_KEY")
GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
TAVILY_API_KEY = os.environ.get("TAVILY_API_KEY")

# --- Configuration & Constants ---
AGENT_MODEL_NAME = "llama3-70b-8192"
AGENT_TEMPERATURE = 0.1
MAX_SEARCH_RESULTS = 3

class ClinicalPrompts:
    SYSTEM_PROMPT = """
    You are SynapseAI, an expert AI clinical assistant... [SYSTEM PROMPT OMITTED FOR BREVITY]
    """

# --- API Constants & Helper Functions ---
UMLS_AUTH_ENDPOINT = "https://utslogin.nlm.nih.gov/cas/v1/api-key"; RXNORM_API_BASE = "https://rxnav.nlm.nih.gov/REST"; OPENFDA_API_BASE = "https://api.fda.gov/drug/label.json"
@lru_cache(maxsize=256)
def get_rxcui(drug_name: str) -> Optional[str]:
    # ... (Keep implementation) ...
    if not drug_name or not isinstance(drug_name, str): return None; drug_name = drug_name.strip();
    if not drug_name: return None; print(f"RxNorm Lookup for: '{drug_name}'");
    try:
        params = {"name": drug_name, "search": 1}; response = requests.get(f"{RXNORM_API_BASE}/rxcui.json", params=params, timeout=10); response.raise_for_status(); data = response.json();
        if data and "idGroup" in data and "rxnormId" in data["idGroup"]: rxcui = data["idGroup"]["rxnormId"][0]; print(f"  Found RxCUI: {rxcui} for '{drug_name}'"); return rxcui
        else:
            params = {"name": drug_name}; response = requests.get(f"{RXNORM_API_BASE}/drugs.json", params=params, timeout=10); response.raise_for_status(); data = response.json();
            if data and "drugGroup" in data and "conceptGroup" in data["drugGroup"]:
                for group in data["drugGroup"]["conceptGroup"]:
                    if group.get("tty") in ["SBD", "SCD", "GPCK", "BPCK", "IN", "MIN", "PIN"]:
                        if "conceptProperties" in group and group["conceptProperties"]: rxcui = group["conceptProperties"][0].get("rxcui");
                        if rxcui: print(f"  Found RxCUI (via /drugs): {rxcui} for '{drug_name}'"); return rxcui
        print(f"  RxCUI not found for '{drug_name}'."); return None
    except requests.exceptions.RequestException as e: print(f"  Error fetching RxCUI for '{drug_name}': {e}"); return None
    except json.JSONDecodeError as e: print(f"  Error decoding RxNorm JSON response for '{drug_name}': {e}"); return None
    except Exception as e: print(f"  Unexpected error in get_rxcui for '{drug_name}': {e}"); return None

@lru_cache(maxsize=128)
def get_openfda_label(rxcui: Optional[str] = None, drug_name: Optional[str] = None) -> Optional[dict]:
    # ... (Keep implementation) ...
    if not rxcui and not drug_name: return None; print(f"OpenFDA Label Lookup for: RXCUI={rxcui}, Name={drug_name}"); search_terms = []
    if rxcui: search_terms.append(f'spl_rxnorm_code:"{rxcui}" OR openfda.rxcui:"{rxcui}"')
    if drug_name: search_terms.append(f'(openfda.brand_name:"{drug_name.lower()}" OR openfda.generic_name:"{drug_name.lower()}")')
    search_query = " OR ".join(search_terms); params = {"search": search_query, "limit": 1};
    try:
        response = requests.get(OPENFDA_API_BASE, params=params, timeout=15); response.raise_for_status(); data = response.json();
        if data and "results" in data and data["results"]: print(f"  Found OpenFDA label for query: {search_query}"); return data["results"][0]
        print(f"  No OpenFDA label found for query: {search_query}"); return None
    except requests.exceptions.RequestException as e: print(f"  Error fetching OpenFDA label: {e}"); return None
    except json.JSONDecodeError as e: print(f"  Error decoding OpenFDA JSON response: {e}"); return None
    except Exception as e: print(f"  Unexpected error in get_openfda_label: {e}"); return None

def search_text_list(text_list: Optional[List[str]], search_terms: List[str]) -> List[str]:
    # ... (Keep implementation) ...
    found_snippets = [];
    if not text_list or not search_terms: return found_snippets; search_terms_lower = [str(term).lower() for term in search_terms if term];
    for text_item in text_list:
        if not isinstance(text_item, str): continue; text_item_lower = text_item.lower();
        for term in search_terms_lower:
            if term in text_item_lower:
                start_index = text_item_lower.find(term); snippet_start = max(0, start_index - 50); snippet_end = min(len(text_item), start_index + len(term) + 100); snippet = text_item[snippet_start:snippet_end];
                snippet = re.sub(f"({re.escape(term)})", r"**\1**", snippet, count=1, flags=re.IGNORECASE) # Highlight match
                found_snippets.append(f"...{snippet}...")
                break # Only report first match per text item
    return found_snippets


# --- Clinical Helper Functions ---
def parse_bp(bp_string: str) -> Optional[tuple[int, int]]:
    # ... (Keep implementation) ...
    if not isinstance(bp_string, str): return None; match = re.match(r"(\d{1,3})\s*/\s*(\d{1,3})", bp_string.strip());
    if match: return int(match.group(1)), int(match.group(2)); return None

def check_red_flags(patient_data: dict) -> List[str]:
    # ... (Keep implementation with multi-line ifs) ...
    flags = [];
    if not patient_data: return flags; symptoms = patient_data.get("hpi", {}).get("symptoms", []); vitals = patient_data.get("vitals", {}); history = patient_data.get("pmh", {}).get("conditions", ""); symptoms_lower = [str(s).lower() for s in symptoms if isinstance(s, str)];
    if "chest pain" in symptoms_lower: flags.append("Red Flag: Chest Pain reported.")
    if "shortness of breath" in symptoms_lower: flags.append("Red Flag: Shortness of Breath reported.")
    if "severe headache" in symptoms_lower: flags.append("Red Flag: Severe Headache reported.")
    if "sudden vision loss" in symptoms_lower: flags.append("Red Flag: Sudden Vision Loss reported.")
    if "weakness on one side" in symptoms_lower: flags.append("Red Flag: Unilateral Weakness reported (potential stroke).")
    if "hemoptysis" in symptoms_lower: flags.append("Red Flag: Hemoptysis (coughing up blood).")
    if "syncope" in symptoms_lower: flags.append("Red Flag: Syncope (fainting).")
    if vitals: temp = vitals.get("temp_c"); hr = vitals.get("hr_bpm"); rr = vitals.get("rr_rpm"); spo2 = vitals.get("spo2_percent"); bp_str = vitals.get("bp_mmhg");
    if temp is not None and temp >= 38.5: flags.append(f"Red Flag: Fever ({temp}°C)."); if hr is not None and hr >= 120: flags.append(f"Red Flag: Tachycardia ({hr} bpm)."); if hr is not None and hr <= 50: flags.append(f"Red Flag: Bradycardia ({hr} bpm)."); if rr is not None and rr >= 24: flags.append(f"Red Flag: Tachypnea ({rr} rpm)."); if spo2 is not None and spo2 <= 92: flags.append(f"Red Flag: Hypoxia ({spo2}%).");
    if bp_str: bp = parse_bp(bp_str);
    if bp:
        if bp[0] >= 180 or bp[1] >= 110: flags.append(f"Red Flag: Hypertensive Urgency/Emergency (BP: {bp_str} mmHg).");
        if bp[0] <= 90 or bp[1] <= 60: flags.append(f"Red Flag: Hypotension (BP: {bp_str} mmHg).");
    if history and isinstance(history, str): history_lower = history.lower();
    if "history of mi" in history_lower and "chest pain" in symptoms_lower: flags.append("Red Flag: History of MI with current Chest Pain.");
    if "history of dvt/pe" in history_lower and "shortness of breath" in symptoms_lower: flags.append("Red Flag: History of DVT/PE with current Shortness of Breath.");
    return list(set(flags))

# CORRECTED format_patient_data_for_prompt function indentation
def format_patient_data_for_prompt(data: dict) -> str:
    """Formats the patient dictionary into a readable string for the LLM."""
    if not data: return "No patient data provided."
    prompt_str = ""
    for key, value in data.items():
        section_title = key.replace('_', ' ').title()
        # Check if the value is a dictionary and has content
        if isinstance(value, dict) and value:
            has_content = any(sub_value for sub_value in value.values())
            if has_content:
                prompt_str += f"**{section_title}:**\n"
                for sub_key, sub_value in value.items():
                     if sub_value: # Only add if sub-value is truthy
                        prompt_str += f"  - {sub_key.replace('_', ' ').title()}: {sub_value}\n"
        # Check if the value is a non-empty list
        elif isinstance(value, list) and value: # <-- Correct indentation
             prompt_str += f"**{section_title}:** {', '.join(map(str, value))}\n"
        # Check if the value is truthy and not a dictionary (handles strings, numbers, etc.)
        elif value and not isinstance(value, dict): # <-- Correct indentation
             prompt_str += f"**{section_title}:** {value}\n"
    return prompt_str.strip()


# --- Tool Definitions ---
class LabOrderInput(BaseModel): test_name: str = Field(...); reason: str = Field(...); priority: str = Field("Routine")
class PrescriptionInput(BaseModel): medication_name: str = Field(...); dosage: str = Field(...); route: str = Field(...); frequency: str = Field(...); duration: str = Field("As directed"); reason: str = Field(...)
class InteractionCheckInput(BaseModel): potential_prescription: str = Field(...); current_medications: Optional[List[str]] = Field(None); allergies: Optional[List[str]] = Field(None)
class FlagRiskInput(BaseModel): risk_description: str = Field(...); urgency: str = Field("High")

@tool("order_lab_test", args_schema=LabOrderInput)
def order_lab_test(test_name: str, reason: str, priority: str = "Routine") -> str:
    print(f"Executing order_lab_test: {test_name}, Reason: {reason}, Priority: {priority}"); return json.dumps({"status": "success", "message": f"Lab Ordered: {test_name} ({priority})", "details": f"Reason: {reason}"})
@tool("prescribe_medication", args_schema=PrescriptionInput)
def prescribe_medication(medication_name: str, dosage: str, route: str, frequency: str, duration: str, reason: str) -> str:
    print(f"Executing prescribe_medication: {medication_name} {dosage}..."); return json.dumps({"status": "success", "message": f"Prescription Prepared: {medication_name} {dosage} {route} {frequency}", "details": f"Duration: {duration}. Reason: {reason}"})
@tool("check_drug_interactions", args_schema=InteractionCheckInput)
def check_drug_interactions(potential_prescription: str, current_medications: Optional[List[str]] = None, allergies: Optional[List[str]] = None) -> str:
    # ... (Keep the FULL implementation of the NEW check_drug_interactions using API helpers) ...
    print(f"\n--- Executing REAL check_drug_interactions ---"); print(f"Checking potential prescription: '{potential_prescription}'"); warnings = []; potential_med_lower = potential_prescription.lower().strip();
    current_meds_list = current_medications or []; allergies_list = allergies or []; current_med_names_lower = [];
    for med in current_meds_list: match = re.match(r"^\s*([a-zA-Z\-]+)", str(med));
    if match: current_med_names_lower.append(match.group(1).lower());
    allergies_lower = [str(a).lower().strip() for a in allergies_list if a]; print(f"  Against Current Meds (names): {current_med_names_lower}"); print(f"  Against Allergies: {allergies_lower}");
    print(f"  Step 1: Normalizing '{potential_prescription}'..."); potential_rxcui = get_rxcui(potential_prescription); potential_label = get_openfda_label(rxcui=potential_rxcui, drug_name=potential_prescription);
    if not potential_rxcui and not potential_label: warnings.append(f"INFO: Could not reliably identify '{potential_prescription}'. Checks may be incomplete.");
    print("  Step 2: Performing Allergy Check...");
    for allergy in allergies_lower:
        if allergy == potential_med_lower: warnings.append(f"CRITICAL ALLERGY (Name Match): Patient allergic to '{allergy}'. Potential prescription is '{potential_prescription}'.");
        elif allergy in ["penicillin", "pcns"] and potential_med_lower in ["amoxicillin", "ampicillin", "augmentin", "piperacillin"]: warnings.append(f"POTENTIAL CROSS-ALLERGY: Patient allergic to Penicillin. High risk with '{potential_prescription}'.");
        elif allergy == "sulfa" and potential_med_lower in ["sulfamethoxazole", "bactrim", "sulfasalazine"]: warnings.append(f"POTENTIAL CROSS-ALLERGY: Patient allergic to Sulfa. High risk with '{potential_prescription}'.");
        elif allergy in ["nsaids", "aspirin"] and potential_med_lower in ["ibuprofen", "naproxen", "ketorolac", "diclofenac"]: warnings.append(f"POTENTIAL CROSS-ALLERGY: Patient allergic to NSAIDs/Aspirin. Risk with '{potential_prescription}'.");
    if potential_label: contraindications = potential_label.get("contraindications"); warnings_section = potential_label.get("warnings_and_cautions") or potential_label.get("warnings");
    if contraindications: allergy_mentions_ci = search_text_list(contraindications, allergies_lower);
    if allergy_mentions_ci: warnings.append(f"ALLERGY RISK (Contraindication Found): Label for '{potential_prescription}' mentions contraindication potentially related to patient allergies: {'; '.join(allergy_mentions_ci)}");
    if warnings_section: allergy_mentions_warn = search_text_list(warnings_section, allergies_lower);
    if allergy_mentions_warn: warnings.append(f"ALLERGY RISK (Warning Found): Label for '{potential_prescription}' mentions warnings potentially related to patient allergies: {'; '.join(allergy_mentions_warn)}");
    print("  Step 3: Performing Drug-Drug Interaction Check...");
    if potential_rxcui or potential_label:
        for current_med_name in current_med_names_lower:
            if not current_med_name or current_med_name == potential_med_lower: continue; print(f"    Checking interaction between '{potential_prescription}' and '{current_med_name}'..."); current_rxcui = get_rxcui(current_med_name); current_label = get_openfda_label(rxcui=current_rxcui, drug_name=current_med_name); search_terms_for_current = [current_med_name];
            if current_rxcui: search_terms_for_current.append(current_rxcui); search_terms_for_potential = [potential_med_lower];
            if potential_rxcui: search_terms_for_potential.append(potential_rxcui); interaction_found_flag = False;
            if potential_label and potential_label.get("drug_interactions"): interaction_mentions = search_text_list(potential_label.get("drug_interactions"), search_terms_for_current);
            if interaction_mentions: warnings.append(f"Potential Interaction ({potential_prescription.capitalize()} Label): Mentions '{current_med_name.capitalize()}'. Snippets: {'; '.join(interaction_mentions)}"); interaction_found_flag = True;
            if current_label and current_label.get("drug_interactions") and not interaction_found_flag: interaction_mentions = search_text_list(current_label.get("drug_interactions"), search_terms_for_potential);
            if interaction_mentions: warnings.append(f"Potential Interaction ({current_med_name.capitalize()} Label): Mentions '{potential_prescription.capitalize()}'. Snippets: {'; '.join(interaction_mentions)}");
    else: warnings.append(f"INFO: Drug-drug interaction check skipped for '{potential_prescription}' as it could not be identified via RxNorm/OpenFDA.");
    final_warnings = list(set(warnings)); status = "warning" if any("CRITICAL" in w or "Interaction" in w or "RISK" in w for w in final_warnings) else "clear";
    if not final_warnings: status = "clear"; message = f"Interaction/Allergy check for '{potential_prescription}': {len(final_warnings)} potential issue(s) identified using RxNorm/OpenFDA." if final_warnings else f"No major interactions or allergy issues identified for '{potential_prescription}' based on RxNorm/OpenFDA lookup."; print(f"--- Interaction Check Complete ---");
    return json.dumps({"status": status, "message": message, "warnings": final_warnings})
@tool("flag_risk", args_schema=FlagRiskInput)
def flag_risk(risk_description: str, urgency: str) -> str:
    print(f"Executing flag_risk: {risk_description}, Urgency: {urgency}"); return json.dumps({"status": "flagged", "message": f"Risk '{risk_description}' flagged with {urgency} urgency."})
search_tool = TavilySearchResults(max_results=MAX_SEARCH_RESULTS, name="tavily_search_results")
all_tools = [order_lab_test, prescribe_medication, check_drug_interactions, flag_risk, search_tool]

# --- LangGraph State & Nodes ---
class AgentState(TypedDict): messages: Annotated[list[Any], operator.add]; patient_data: Optional[dict]; summary: Optional[str]; interaction_warnings: Optional[List[str]]
llm = ChatGroq(temperature=AGENT_TEMPERATURE, model=AGENT_MODEL_NAME); model_with_tools = llm.bind_tools(all_tools); tool_executor = ToolExecutor(all_tools)
def agent_node(state: AgentState):
    # ... (Keep implementation) ...
    print("\n---AGENT NODE---"); current_messages = state['messages'];
    if not current_messages or not isinstance(current_messages[0], SystemMessage): print("Prepending System Prompt."); current_messages = [SystemMessage(content=ClinicalPrompts.SYSTEM_PROMPT)] + current_messages;
    print(f"Invoking LLM with {len(current_messages)} messages.");
    try: response = model_with_tools.invoke(current_messages); print(f"Agent Raw Response Type: {type(response)}");
    if hasattr(response, 'tool_calls') and response.tool_calls: print(f"Agent Response Tool Calls: {response.tool_calls}"); else: print("Agent Response: No tool calls.");
    except Exception as e: print(f"ERROR in agent_node: {e}"); traceback.print_exc(); error_message = AIMessage(content=f"Error: {e}"); return {"messages": [error_message]};
    return {"messages": [response]}
def tool_node(state: AgentState):
    # ... (Keep implementation) ...
    print("\n---TOOL NODE---"); tool_messages = []; last_message = state['messages'][-1]; interaction_warnings_found = [];
    if not isinstance(last_message, AIMessage) or not getattr(last_message, 'tool_calls', None): print("Warning: Tool node called unexpectedly."); return {"messages": [], "interaction_warnings": None};
    tool_calls = last_message.tool_calls; print(f"Tool calls received: {json.dumps(tool_calls, indent=2)}"); prescriptions_requested = {}; interaction_checks_requested = {};
    for call in tool_calls: tool_name = call.get('name'); tool_args = call.get('args', {});
    if tool_name == 'prescribe_medication': med_name = tool_args.get('medication_name', '').lower();
    if med_name: prescriptions_requested[med_name] = call;
    elif tool_name == 'check_drug_interactions': potential_med = tool_args.get('potential_prescription', '').lower();
    if potential_med: interaction_checks_requested[potential_med] = call;
    valid_tool_calls_for_execution = []; blocked_ids = set();
    for med_name, prescribe_call in prescriptions_requested.items():
        if med_name not in interaction_checks_requested: print(f"**SAFETY VIOLATION (Agent): Prescribe '{med_name}' blocked - no interaction check requested.**"); error_msg = ToolMessage(content=json.dumps({"status": "error", "message": f"Interaction check needed for '{med_name}'."}), tool_call_id=prescribe_call['id'], name=prescribe_call['name']); tool_messages.append(error_msg); blocked_ids.add(prescribe_call['id']);
    valid_tool_calls_for_execution = [call for call in tool_calls if call['id'] not in blocked_ids];
    patient_data = state.get("patient_data", {}); patient_meds_full = patient_data.get("medications", {}).get("current", []); patient_allergies = patient_data.get("allergies", []);
    for call in valid_tool_calls_for_execution:
        if call['name'] == 'check_drug_interactions':
            if 'args' not in call: call['args'] = {}; call['args']['current_medications'] = patient_meds_full; call['args']['allergies'] = patient_allergies; print(f"Augmented interaction check args for call ID {call['id']}");
    if valid_tool_calls_for_execution: print(f"Attempting execution: {[c['name'] for c in valid_tool_calls_for_execution]}");
    try: responses = tool_executor.batch(valid_tool_calls_for_execution, return_exceptions=True);
    for call, resp in zip(valid_tool_calls_for_execution, responses): tool_call_id = call['id']; tool_name = call['name'];
    if isinstance(resp, Exception): error_type = type(resp).__name__; error_str = str(resp); print(f"ERROR executing tool '{tool_name}': {error_type} - {error_str}"); traceback.print_exc(); error_content = json.dumps({"status": "error", "message": f"Failed: {error_type} - {error_str}"}); tool_messages.append(ToolMessage(content=error_content, tool_call_id=tool_call_id, name=tool_name));
    # ... Specific error check ...
    else:
        print(f"Tool '{tool_name}' executed."); content_str = str(resp); tool_messages.append(ToolMessage(content=content_str, tool_call_id=tool_call_id, name=tool_name));
        if tool_name == "check_drug_interactions": # Extract warnings
            try: result_data = json.loads(content_str);
            if result_data.get("status") == "warning" and result_data.get("warnings"): print(f"  Interaction check returned warnings: {result_data['warnings']}"); interaction_warnings_found.extend(result_data["warnings"]);
            except Exception as e: print(f"  Error processing interaction check result: {e}");
    except Exception as e: # Outer exception handling...
         print(f"CRITICAL TOOL NODE ERROR: {e}"); traceback.print_exc(); error_content = json.dumps({"status": "error", "message": f"Internal error: {e}"}); processed_ids = {msg.tool_call_id for msg in tool_messages}; [tool_messages.append(ToolMessage(content=error_content, tool_call_id=call['id'], name=call['name'])) for call in valid_tool_calls_for_execution if call['id'] not in processed_ids];
    print(f"Returning {len(tool_messages)} tool messages. Warnings: {bool(interaction_warnings_found)}")
    return {"messages": tool_messages, "interaction_warnings": interaction_warnings_found or None} # Return messages AND warnings

def reflection_node(state: AgentState):
    # ... (Keep implementation) ...
    print("\n---REFLECTION NODE---")
    interaction_warnings = state.get("interaction_warnings")
    if not interaction_warnings: print("Warning: Reflection node called without warnings."); return {"messages": [], "interaction_warnings": None};
    print(f"Reviewing interaction warnings: {interaction_warnings}"); triggering_ai_message = None; relevant_tool_call_ids = set();
    for msg in reversed(state['messages']):
        if isinstance(msg, ToolMessage) and msg.name == "check_drug_interactions": relevant_tool_call_ids.add(msg.tool_call_id);
        if isinstance(msg, AIMessage) and msg.tool_calls:
            if any(tc['id'] in relevant_tool_call_ids for tc in msg.tool_calls): triggering_ai_message = msg; break;
    if not triggering_ai_message: print("Error: Could not find triggering AI message for reflection."); return {"messages": [AIMessage(content="Internal Error: Reflection context missing.")], "interaction_warnings": None};
    original_plan_proposal_context = triggering_ai_message.content;
    reflection_prompt_text = f"""You are SynapseAI, performing a critical safety review... [PROMPT OMITTED FOR BREVITY]""" # Use full prompt
    reflection_messages = [SystemMessage(content="Perform focused safety review based on interaction warnings."), HumanMessage(content=reflection_prompt_text)];
    print("Invoking LLM for reflection...");
    try: reflection_response = llm.invoke(reflection_messages); print(f"Reflection Response: {reflection_response.content}"); final_ai_message = AIMessage(content=reflection_response.content);
    except Exception as e: print(f"ERROR during reflection: {e}"); traceback.print_exc(); final_ai_message = AIMessage(content=f"Error during safety reflection: {e}");
    return {"messages": [final_ai_message], "interaction_warnings": None} # Return reflection response, clear warnings

# --- Graph Routing Logic ---
def should_continue(state: AgentState) -> str:
    # ... (Keep implementation) ...
    print("\n---ROUTING DECISION (Agent Output)---"); last_message = state['messages'][-1] if state['messages'] else None;
    if not isinstance(last_message, AIMessage): return "end_conversation_turn";
    if "Sorry, an internal error occurred" in last_message.content: return "end_conversation_turn";
    if getattr(last_message, 'tool_calls', None): return "continue_tools"; else: return "end_conversation_turn";
def after_tools_router(state: AgentState) -> str:
    # ... (Keep implementation) ...
    print("\n---ROUTING DECISION (After Tools)---");
    if state.get("interaction_warnings"): print("Routing: Warnings found -> Reflection"); return "reflect_on_warnings";
    else: print("Routing: No warnings -> Agent"); return "continue_to_agent";

# --- ClinicalAgent Class ---
class ClinicalAgent:
    def __init__(self):
        # ... (Keep graph compilation) ...
        workflow = StateGraph(AgentState); workflow.add_node("agent", agent_node); workflow.add_node("tools", tool_node); workflow.add_node("reflection", reflection_node)
        workflow.set_entry_point("agent"); workflow.add_conditional_edges("agent", should_continue, {"continue_tools": "tools", "end_conversation_turn": END})
        workflow.add_conditional_edges("tools", after_tools_router, {"reflect_on_warnings": "reflection", "continue_to_agent": "agent"})
        workflow.add_edge("reflection", "agent"); self.graph_app = workflow.compile(); print("ClinicalAgent initialized and LangGraph compiled.")
    def invoke_turn(self, state: Dict) -> Dict:
        # ... (Keep implementation) ...
        print(f"Invoking graph with state keys: {state.keys()}");
        try: final_state = self.graph_app.invoke(state, {"recursion_limit": 15}); final_state.setdefault('summary', state.get('summary')); final_state.setdefault('interaction_warnings', None); return final_state
        except Exception as e: print(f"CRITICAL ERROR during graph invocation: {type(e).__name__} - {e}"); traceback.print_exc(); error_msg = AIMessage(content=f"Sorry, error occurred: {e}"); return {"messages": state.get('messages', []) + [error_msg], "patient_data": state.get('patient_data'), "summary": state.get('summary'), "interaction_warnings": None}