app.py

# -*- coding: utf-8 -*-
import streamlit as st
import requests
import json
import re
import os
import operator
import traceback
from functools import lru_cache
from dotenv import load_dotenv

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.prompts import ChatPromptTemplate # Not explicitly used
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 & Validation ---
load_dotenv()
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")
missing_keys = []
if not UMLS_API_KEY: missing_keys.append("UMLS_API_KEY")
if not GROQ_API_KEY: missing_keys.append("GROQ_API_KEY")
if not TAVILY_API_KEY: missing_keys.append("TAVILY_API_KEY")
if missing_keys: st.error(f"Missing API Key(s): {', '.join(missing_keys)}."); st.stop()

# --- Configuration & Constants ---
class ClinicalAppSettings: APP_TITLE = "SynapseAI (UMLS/FDA Integrated)"; PAGE_LAYOUT = "wide"; MODEL_NAME = "llama3-70b-8192"; TEMPERATURE = 0.1; MAX_SEARCH_RESULTS = 3
class ClinicalPrompts: SYSTEM_PROMPT = """
    You are SynapseAI, an expert AI clinical assistant engaged in an interactive consultation... [SYSTEM PROMPT REMAINS THE SAME - OMITTED FOR BREVITY]
    """

# --- API Helper Functions (get_rxcui, get_openfda_label, search_text_list) ---
# ... (Keep these functions exactly as they were in the previous 'full code' response) ...
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]:
    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]:
    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]:
    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 = snippet.replace(term, f"**{term}**", 1); found_snippets.append(f"...{snippet}..."); break
    return found_snippets


# --- Other Helper Functions (parse_bp, check_red_flags, format_patient_data_for_prompt) ---
# ... (Keep these functions exactly as they were) ...
def parse_bp(bp_string: str) -> Optional[tuple[int, int]]:
    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]:
    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))
def format_patient_data_for_prompt(data: dict) -> str:
    if not data: return "No patient data provided."; prompt_str = "";
    for key, value in data.items(): section_title = key.replace('_', ' ').title();
    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: prompt_str += f"  - {sub_key.replace('_', ' ').title()}: {sub_value}\n"
    elif isinstance(value, list) and value: prompt_str += f"**{section_title}:** {', '.join(map(str, value))}\n"
    elif value and not isinstance(value, dict): 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 for '{potential_prescription}' ---");
    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}"); st.error(f"🚨 **{urgency.upper()} RISK FLAGGED by AI:** {risk_description}", icon="🚨"); return json.dumps({"status": "flagged", "message": f"Risk '{risk_description}' flagged with {urgency} urgency."})
search_tool = TavilySearchResults(max_results=ClinicalAppSettings.MAX_SEARCH_RESULTS, name="tavily_search_results")

# --- LangGraph Setup ---
class AgentState(TypedDict): messages: Annotated[list[Any], operator.add]; patient_data: Optional[dict]
tools = [order_lab_test, prescribe_medication, check_drug_interactions, flag_risk, search_tool]
tool_executor = ToolExecutor(tools)
model = ChatGroq(temperature=ClinicalAppSettings.TEMPERATURE, model=ClinicalAppSettings.MODEL_NAME)
model_with_tools = model.bind_tools(tools)

# --- Graph Nodes (agent_node, tool_node) ---
# ... (Keep agent_node and tool_node functions exactly as they were in the last 'full code' response) ...
def agent_node(state: AgentState):
    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):
    print("\n---TOOL NODE---"); tool_messages = []; last_message = state['messages'][-1];
    if not isinstance(last_message, AIMessage) or not getattr(last_message, 'tool_calls', None): print("Warning: Tool node called unexpectedly."); return {"messages": []};
    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: st.error(f"**Safety Violation:** AI tried to prescribe '{med_name}' without check."); 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(); st.error(f"Error: {error_type}"); 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));
    if isinstance(resp, AttributeError) and "'dict' object has no attribute 'tool'" in error_str: print("\n *** DETECTED SPECIFIC ATTRIBUTE ERROR *** \n");
    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));
    except Exception as e: print(f"CRITICAL TOOL NODE ERROR: {e}"); traceback.print_exc(); st.error(f"Critical error: {e}"); 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."); return {"messages": tool_messages}

# --- Graph Edges (Routing Logic) ---
def should_continue(state: AgentState) -> str:
    print("\n---ROUTING DECISION---"); 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";

# --- Graph Definition & Compilation ---
workflow = StateGraph(AgentState); workflow.add_node("agent", agent_node); workflow.add_node("tools", tool_node)
workflow.set_entry_point("agent"); workflow.add_conditional_edges("agent", should_continue, {"continue_tools": "tools", "end_conversation_turn": END})
workflow.add_edge("tools", "agent"); app = workflow.compile(); print("LangGraph compiled successfully.")

# --- Streamlit UI ---
def main():
    st.set_page_config(page_title=ClinicalAppSettings.APP_TITLE, layout=ClinicalAppSettings.PAGE_LAYOUT)
    st.title(f"🩺 {ClinicalAppSettings.APP_TITLE}")
    st.caption(f"Interactive Assistant | LangGraph/Groq/Tavily/UMLS/OpenFDA | Model: {ClinicalAppSettings.MODEL_NAME}")
    if "messages" not in st.session_state: st.session_state.messages = []
    if "patient_data" not in st.session_state: st.session_state.patient_data = None
    if "graph_app" not in st.session_state: st.session_state.graph_app = app

    # --- Patient Data Input Sidebar ---
    with st.sidebar:
        st.header("📄 Patient Intake Form")
        # Input fields... (Using shorter versions for brevity, assume full fields are here)
        st.subheader("Demographics"); age = st.number_input("Age", 0, 120, 55); sex = st.selectbox("Sex", ["Male", "Female", "Other"])
        st.subheader("HPI"); chief_complaint = st.text_input("Chief Complaint", "Chest pain"); hpi_details = st.text_area("HPI Details", "55 y/o male...", height=100); symptoms = st.multiselect("Symptoms", ["Nausea", "Diaphoresis", "SOB", "Dizziness"], default=["Nausea", "Diaphoresis"])
        st.subheader("History"); pmh = st.text_area("PMH", "HTN, HLD, DM2, History of MI"); psh = st.text_area("PSH", "Appendectomy")
        st.subheader("Meds & Allergies"); current_meds_str = st.text_area("Current Meds", "Lisinopril 10mg daily\nMetformin 1000mg BID"); allergies_str = st.text_area("Allergies", "Penicillin (rash)")
        st.subheader("Social/Family"); social_history = st.text_area("SH", "Smoker"); family_history = st.text_area("FHx", "Father MI")
        st.subheader("Vitals & Exam"); col1, col2 = st.columns(2);
        with col1: temp_c = st.number_input("Temp C", 35.0, 42.0, 36.8, format="%.1f"); hr_bpm = st.number_input("HR", 30, 250, 95); rr_rpm = st.number_input("RR", 5, 50, 18)
        with col2: bp_mmhg = st.text_input("BP", "155/90"); spo2_percent = st.number_input("SpO2", 70, 100, 96); pain_scale = st.slider("Pain", 0, 10, 8)
        exam_notes = st.text_area("Exam Notes", "Awake, alert...", height=50)

        if st.button("Start/Update Consultation"):
            current_meds_list = [med.strip() for med in current_meds_str.split('\n') if med.strip()]
            current_med_names_only = [];
            for med in current_meds_list: match = re.match(r"^\s*([a-zA-Z\-]+)", med);
            if match: current_med_names_only.append(match.group(1).lower())
            allergies_list = []
            for a in allergies_str.split(','): cleaned_allergy = a.strip();
            if cleaned_allergy: match = re.match(r"^\s*([a-zA-Z\-\s/]+)(?:\s*\(.*\))?", cleaned_allergy); name_part = match.group(1).strip().lower() if match else cleaned_allergy.lower(); allergies_list.append(name_part)
            st.session_state.patient_data = { "demographics": {"age": age, "sex": sex}, "hpi": {"chief_complaint": chief_complaint, "details": hpi_details, "symptoms": symptoms}, "pmh": {"conditions": pmh}, "psh": {"procedures": psh}, "medications": {"current": current_meds_list, "names_only": current_med_names_only}, "allergies": allergies_list, "social_history": {"details": social_history}, "family_history": {"details": family_history}, "vitals": { "temp_c": temp_c, "hr_bpm": hr_bpm, "bp_mmhg": bp_mmhg, "rr_rpm": rr_rpm, "spo2_percent": spo2_percent, "pain_scale": pain_scale}, "exam_findings": {"notes": exam_notes} }
            red_flags = check_red_flags(st.session_state.patient_data); st.sidebar.markdown("---");
            if red_flags: st.sidebar.warning("**Initial Red Flags:**"); [st.sidebar.warning(f"- {flag.replace('Red Flag: ','')}") for flag in red_flags]
            else: st.sidebar.success("No immediate red flags.")
            initial_prompt = "Initiate consultation. Review patient data and begin analysis."
            st.session_state.messages = [HumanMessage(content=initial_prompt)]; st.success("Patient data loaded/updated.")

    # --- Main Chat Interface Area ---
    st.header("💬 Clinical Consultation")
    # Display loop - SyntaxError Fixed
    for msg in st.session_state.messages:
        if isinstance(msg, HumanMessage):
            with st.chat_message("user"): st.markdown(msg.content) # No key
        elif isinstance(msg, AIMessage):
            with st.chat_message("assistant"): # No key
                ai_content = msg.content; structured_output = None
                try: # JSON Parsing logic...
                    json_match = re.search(r"```json\s*(\{.*?\})\s*```", ai_content, re.DOTALL | re.IGNORECASE)
                    if json_match: json_str = json_match.group(1); prefix = ai_content[:json_match.start()].strip(); suffix = ai_content[json_match.end():].strip();
                    if prefix: st.markdown(prefix); structured_output = json.loads(json_str);
                    if suffix: st.markdown(suffix)
                    elif ai_content.strip().startswith("{") and ai_content.strip().endswith("}"): structured_output = json.loads(ai_content); ai_content = ""
                    else: st.markdown(ai_content)
                except Exception as e: st.markdown(ai_content); print(f"Error parsing/displaying AI JSON: {e}")
                if structured_output and isinstance(structured_output, dict): # Structured JSON display logic...
                    st.divider(); st.subheader("📊 AI Analysis & Recommendations")
                    cols = st.columns(2);
                    with cols[0]: st.markdown("**Assessment:**"); st.markdown(f"> {structured_output.get('assessment', 'N/A')}"); st.markdown("**Differential Diagnosis:**"); ddx = structured_output.get('differential_diagnosis', []);
                    if ddx: [st.expander(f"{'🥇🥈🥉'[('High','Medium','Low').index(item.get('likelihood','Low')[0])] if item.get('likelihood','?')[0] in 'HML' else '?'} {item.get('diagnosis', 'Unknown')} ({item.get('likelihood','?')})").write(f"**Rationale:** {item.get('rationale', 'N/A')}") for item in ddx]
                    else: st.info("No DDx provided."); st.markdown("**Risk Assessment:**"); risk = structured_output.get('risk_assessment', {}); flags=risk.get('identified_red_flags',[]); concerns=risk.get("immediate_concerns",[]); comps=risk.get("potential_complications",[])
                    if flags: st.warning(f"**Flags:** {', '.join(flags)}"); if concerns: st.warning(f"**Concerns:** {', '.join(concerns)}"); if comps: st.info(f"**Potential Complications:** {', '.join(comps)}");
                    if not flags and not concerns: st.success("No major risks highlighted.")
                    with cols[1]: st.markdown("**Recommended Plan:**"); plan = structured_output.get('recommended_plan', {});
                    for section in ["investigations","therapeutics","consultations","patient_education"]: st.markdown(f"_{section.replace('_',' ').capitalize()}:_"); items = plan.get(section); [st.markdown(f"- {item}") for item in items] if items and isinstance(items, list) else (st.markdown(f"- {items}") if items else st.markdown("_None_")); st.markdown("")
                    st.markdown("**Rationale & Guideline Check:**"); st.markdown(f"> {structured_output.get('rationale_summary', 'N/A')}"); interaction_summary = structured_output.get("interaction_check_summary", "");
                    if interaction_summary: st.markdown("**Interaction Check Summary:**"); st.markdown(f"> {interaction_summary}"); st.divider()

                # CORRECTED Tool Call Display Block
                if getattr(msg, 'tool_calls', None):
                     with st.expander("🛠️ AI requested actions", expanded=False):
                         if msg.tool_calls: # Check if list is not empty
                            for tc in msg.tool_calls:
                                try:
                                    # Properly indented try block content
                                    st.code(f"Action: {tc.get('name', 'Unknown Tool')}\nArgs: {json.dumps(tc.get('args', {}), indent=2)}", language="json")
                                except Exception as display_e:
                                    # Properly indented except block content
                                    st.error(f"Could not display tool call arguments properly: {display_e}", icon="⚠️")
                                    # Provide a fallback display
                                    st.code(f"Action: {tc.get('name', 'Unknown Tool')}\nRaw Args: {tc.get('args')}") # Show raw args if JSON fails
                         else:
                            st.caption("_No actions requested in this turn._")
        elif isinstance(msg, ToolMessage):
            tool_name_display = getattr(msg, 'name', 'tool_execution')
            with st.chat_message(tool_name_display, avatar="🛠️"): # No key
                try: # Tool message display logic...
                    tool_data = json.loads(msg.content); status = tool_data.get("status", "info"); message = tool_data.get("message", msg.content); details = tool_data.get("details"); warnings = tool_data.get("warnings");
                    if status == "success" or status == "clear" or status == "flagged": st.success(f"{message}", icon="✅" if status != "flagged" else "🚨")
                    elif status == "warning": st.warning(f"{message}", icon="⚠️");
                    if warnings and isinstance(warnings, list): st.caption("Details:"); [st.caption(f"- {warn}") for warn in warnings]
                    else: st.error(f"{message}", icon="❌")
                    if details: st.caption(f"Details: {details}")
                except json.JSONDecodeError: st.info(f"{msg.content}")
                except Exception as e: st.error(f"Error displaying tool message: {e}", icon="❌"); st.caption(f"Raw content: {msg.content}")

    # --- Chat Input Logic ---
    if prompt := st.chat_input("Your message or follow-up query..."):
        if not st.session_state.patient_data: st.warning("Please load patient data first."); st.stop()
        user_message = HumanMessage(content=prompt); st.session_state.messages.append(user_message)
        with st.chat_message("user"): st.markdown(prompt)
        current_state = AgentState(messages=st.session_state.messages, patient_data=st.session_state.patient_data)
        with st.spinner("SynapseAI is thinking..."):
            try:
                final_state = st.session_state.graph_app.invoke(current_state, {"recursion_limit": 15})
                st.session_state.messages = final_state['messages']
            except Exception as e: print(f"CRITICAL ERROR: {e}"); traceback.print_exc(); st.error(f"Error: {e}")
        st.rerun()

    # Disclaimer
    st.markdown("---"); st.warning("**Disclaimer:** SynapseAI is for demonstration...")

if __name__ == "__main__":
    main()