Update agent.py

agent.py

@@ -1,191 +1,347 @@
-# app.py
-import streamlit as st
+# agent.py
+import requests
 import json
 import re
 import os
-from dotenv import load_dotenv
+import operator
+import traceback
+from functools import lru_cache
 
-# Import agent logic and message types
-from agent import ClinicalAgent, AgentState, check_red_flags # Import necessary components
-from langchain_core.messages import HumanMessage, AIMessage, ToolMessage
+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
 
-# --- Environment Variable Loading & Validation ---
-load_dotenv()
-# Check keys required by agent.py are present before initializing the agent
+from typing import Optional, List, Dict, Any, TypedDict, Annotated
+
+# --- Environment Variable Loading ---
+# Keys are primarily used here, but checked in app.py for UI feedback
 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 required API Key(s): {', '.join(missing_keys)}. Please set them in Hugging Face Space Secrets or environment variables.")
-    st.stop()
-
-# --- App Configuration ---
-class ClinicalAppSettings:
-    APP_TITLE = "SynapseAI (UMLS/FDA Integrated)"
-    PAGE_LAYOUT = "wide"
-    # Model name is now primarily defined in agent.py, but can keep for display
-    MODEL_NAME_DISPLAY = "Llama3-70b (via Groq)"
-
-
-# --- 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_DISPLAY}")
-
-    # Initialize session state
-    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
-    # Summary state for future memory enhancement
-    if "summary" not in st.session_state: st.session_state.summary = None
-    # Initialize the agent instance only once
-    if "agent" not in st.session_state:
-        try:
-            st.session_state.agent = ClinicalAgent()
-            print("ClinicalAgent successfully initialized in Streamlit session state.")
-        except Exception as e:
-            st.error(f"Failed to initialize Clinical Agent: {e}. Check API keys and dependencies.")
-            print(f"ERROR Initializing ClinicalAgent: {e}")
-            traceback.print_exc()
-            st.stop()
-
-
-    # --- Patient Data Input Sidebar ---
-    with st.sidebar:
-        st.header("📄 Patient Intake Form")
-        # Input fields... (Assume full fields as before)
-        st.subheader("Demographics"); age = st.number_input("Age", 0, 120, 55, key="sb_age"); sex = st.selectbox("Sex", ["Male", "Female", "Other"], key="sb_sex")
-        st.subheader("HPI"); chief_complaint = st.text_input("Chief Complaint", "Chest pain", key="sb_cc"); hpi_details = st.text_area("HPI Details", "55 y/o male...", height=100, key="sb_hpi"); symptoms = st.multiselect("Symptoms", ["Nausea", "Diaphoresis", "SOB", "Dizziness", "Severe Headache", "Syncope", "Hemoptysis"], default=["Nausea", "Diaphoresis"], key="sb_sym")
-        st.subheader("History"); pmh = st.text_area("PMH", "HTN, HLD, DM2, History of MI", key="sb_pmh"); psh = st.text_area("PSH", "Appendectomy", key="sb_psh")
-        st.subheader("Meds & Allergies"); current_meds_str = st.text_area("Current Meds", "Lisinopril 10mg daily\nMetformin 1000mg BID\nWarfarin 5mg daily", key="sb_meds"); allergies_str = st.text_area("Allergies", "Penicillin (rash), Aspirin", key="sb_allergies")
-        st.subheader("Social/Family"); social_history = st.text_area("SH", "Smoker", key="sb_sh"); family_history = st.text_area("FHx", "Father MI", key="sb_fhx")
-        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", key="sb_temp"); hr_bpm = st.number_input("HR", 30, 250, 95, key="sb_hr"); rr_rpm = st.number_input("RR", 5, 50, 18, key="sb_rr")
-        with col2: bp_mmhg = st.text_input("BP", "155/90", key="sb_bp"); spo2_percent = st.number_input("SpO2", 70, 100, 96, key="sb_spo2"); pain_scale = st.slider("Pain", 0, 10, 8, key="sb_pain")
-        exam_notes = st.text_area("Exam Notes", "Awake, alert...", height=50, key="sb_exam")
-
-        if st.button("Start/Update Consultation", key="sb_start"):
-            # Compile data...
-            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)
-            # Update patient data in session state
-            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} }
-            # Call check_red_flags from agent module
-            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.")
-            # Reset conversation and summary on new intake
-            initial_prompt = "Initiate consultation. Review patient data and begin analysis."
-            st.session_state.messages = [HumanMessage(content=initial_prompt)]
-            st.session_state.summary = None # Reset summary
-            st.success("Patient data loaded/updated.")
-            # Rerun might be needed if the main area should clear or update based on new data
-            st.rerun()
-
-    # --- Main Chat Interface Area ---
-    st.header("💬 Clinical Consultation")
-    # Display loop - Uses messages from st.session_state
-    for msg in st.session_state.messages: # Removed enumerate and key
-        if isinstance(msg, HumanMessage):
-            with st.chat_message("user"): st.markdown(msg.content)
-        elif isinstance(msg, AIMessage):
-            with st.chat_message("assistant"):
-                # ... (Keep the detailed AI message display logic, including JSON parsing) ...
-                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) # Display non-JSON 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")
-                    # ... (Keep detailed JSON display logic for assessment, ddx, plan, etc.) ...
-                    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()
-
-                # Tool Call Display
-                if getattr(msg, 'tool_calls', None):
-                     with st.expander("🛠️ AI requested actions", expanded=False):
-                         if msg.tool_calls:
-                            for tc in msg.tool_calls:
-                                try: st.code(f"Action: {tc.get('name', 'Unknown Tool')}\nArgs: {json.dumps(tc.get('args', {}), indent=2)}", language="json")
-                                except Exception as display_e: st.error(f"Could not display tool call args: {display_e}", icon="⚠️"); st.code(f"Action: {tc.get('name', 'Unknown Tool')}\nRaw Args: {tc.get('args')}")
-                         else: st.caption("_No actions requested._")
-        elif isinstance(msg, ToolMessage):
-            tool_name_display = getattr(msg, 'name', 'tool_execution')
-            with st.chat_message(tool_name_display, avatar="🛠️"):
-                # ... (Keep ToolMessage display logic) ...
-                try:
-                    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="❌") # Assume error if not known status
-                    if details: st.caption(f"Details: {details}")
-                except json.JSONDecodeError: st.info(f"{msg.content}") # Display raw if not JSON
-                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()
-        if not st.session_state.agent: st.error("Agent not initialized. Check logs."); st.stop() # Add check for agent
-
-        # Append user message and display immediately
-        user_message = HumanMessage(content=prompt)
-        st.session_state.messages.append(user_message)
-        with st.chat_message("user"): st.markdown(prompt)
-
-        # Prepare state for the agent, including existing messages and patient data
-        current_state_dict = {
-            "messages": st.session_state.messages,
-            "patient_data": st.session_state.patient_data,
-            "summary": st.session_state.get("summary"), # Include summary if implemented
-            "interaction_warnings": None # Always start turn with no pending warnings
+
+# --- Configuration & Constants ---
+AGENT_MODEL_NAME = "llama3-70b-8192"
+AGENT_TEMPERATURE = 0.1
+MAX_SEARCH_RESULTS = 3
+
+class ClinicalPrompts:
+    # The comprehensive system prompt defining agent behavior
+    SYSTEM_PROMPT = """
+    You are SynapseAI, an expert AI clinical assistant engaged in an interactive consultation.
+    Your goal is to support healthcare professionals by analyzing patient data, providing differential diagnoses, suggesting evidence-based management plans, and identifying risks according to current standards of care.
+
+    **Core Directives for this Conversation:**
+    1.  **Analyze Sequentially:** Process information turn-by-turn. Base your responses on the *entire* conversation history.
+    2.  **Seek Clarity:** If the provided information is insufficient or ambiguous for a safe assessment, CLEARLY STATE what specific additional information or clarification is needed. Do NOT guess or make unsafe assumptions.
+    3.  **Structured Assessment (When Ready):** When you have sufficient information and have performed necessary checks (like interactions, guideline searches), provide a comprehensive assessment using the following JSON structure. Output this JSON structure as the primary content of your response when you are providing the full analysis. Do NOT output incomplete JSON. If you need to ask a question or perform a tool call first, do that instead of outputting this structure.
+        ```json
+        {
+          "assessment": "Concise summary of the patient's presentation and key findings based on the conversation.",
+          "differential_diagnosis": [
+            {"diagnosis": "Primary Diagnosis", "likelihood": "High/Medium/Low", "rationale": "Supporting evidence from conversation..."},
+            {"diagnosis": "Alternative Diagnosis 1", "likelihood": "Medium/Low", "rationale": "Supporting/Refuting evidence..."},
+            {"diagnosis": "Alternative Diagnosis 2", "likelihood": "Low", "rationale": "Why it's less likely but considered..."}
+          ],
+          "risk_assessment": {
+            "identified_red_flags": ["List any triggered red flags based on input and analysis"],
+            "immediate_concerns": ["Specific urgent issues requiring attention (e.g., sepsis risk, ACS rule-out)"],
+            "potential_complications": ["Possible future issues based on presentation"]
+          },
+          "recommended_plan": {
+            "investigations": ["List specific lab tests or imaging required. Use 'order_lab_test' tool."],
+            "therapeutics": ["Suggest specific treatments or prescriptions. Use 'prescribe_medication' tool. MUST check interactions first using 'check_drug_interactions'."],
+            "consultations": ["Recommend specialist consultations if needed."],
+            "patient_education": ["Key points for patient communication."]
+          },
+          "rationale_summary": "Justification for assessment/plan. **Crucially, if relevant (e.g., ACS, sepsis, common infections), use 'tavily_search_results' to find and cite current clinical practice guidelines (e.g., 'latest ACC/AHA chest pain guidelines 202X', 'Surviving Sepsis Campaign guidelines') supporting your recommendations.** Include summary of guideline findings here.",
+          "interaction_check_summary": "Summary of findings from 'check_drug_interactions' if performed."
         }
+        ```
+    4.  **Safety First - Interactions:** BEFORE suggesting a new prescription via `prescribe_medication`, you MUST FIRST use `check_drug_interactions` in a preceding or concurrent tool call. Report the findings from the interaction check. If significant interactions exist, modify the plan or state the contraindication clearly.
+    5.  **Safety First - Red Flags:** Use the `flag_risk` tool IMMEDIATELY if critical red flags requiring urgent action are identified at any point in the conversation.
+    6.  **Tool Use:** Employ tools (`order_lab_test`, `prescribe_medication`, `check_drug_interactions`, `flag_risk`, `tavily_search_results`) logically within the conversational flow. Wait for tool results before proceeding if the result is needed for the next step (e.g., wait for interaction check before confirming prescription in the structured JSON).
+    7.  **Evidence & Guidelines:** Actively use `tavily_search_results` not just for general knowledge, but specifically to query for and incorporate **current clinical practice guidelines** relevant to the patient's presentation (e.g., chest pain, shortness of breath, suspected infection). Summarize findings in the `rationale_summary` when providing the structured output.
+    8.  **Conciseness & Flow:** Be medically accurate and concise. Use standard terminology. Respond naturally in conversation (asking questions, acknowledging info) until ready for the full structured JSON output.
+    """
+
+# --- 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]:
+    """Uses RxNorm API to find the RxCUI for a given drug name."""
+    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: # Try direct lookup first
+        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: # Fallback to /drugs search
+            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]:
+    """Fetches drug label information from OpenFDA using RxCUI or drug name."""
+    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]:
+    """ Case-insensitive search for any search_term within a list of text strings. Returns snippets. """
+    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]]:
+    """Parses BP string like '120/80' into (systolic, diastolic) integers."""
+    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]:
+    """Checks patient data against predefined red flags."""
+    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)]
+
+    # Symptom Flags
+    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).")
 
-        # Invoke the agent's graph for one turn
-        with st.spinner("SynapseAI is thinking..."):
-            try:
-                # Call the agent instance's method
-                final_state = st.session_state.agent.invoke_turn(current_state_dict)
+    # Vital Sign Flags
+    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).")
 
-                # Update Streamlit session state from the returned agent state
-                st.session_state.messages = final_state.get('messages', [])
-                st.session_state.summary = final_state.get('summary') # Update summary if implemented
+    # History Flags
+    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.")
 
-            except Exception as e:
-                print(f"CRITICAL ERROR during agent invocation: {type(e).__name__} - {e}")
-                traceback.print_exc()
-                st.error(f"An error occurred during processing: {e}", icon="❌")
-                # Optionally append an error message to the chat display
-                # st.session_state.messages.append(AIMessage(content=f"Error processing request: {e}"))
+    return list(set(flags)) # Unique flags
 
-        # Rerun Streamlit script to update the chat display
-        st.rerun()
+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();
+    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()
 
-    # Disclaimer
-    st.markdown("---"); st.warning("**Disclaimer:** SynapseAI is for demonstration...")
 
-if __name__ == "__main__":
-    main()
+# --- 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:
+    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}"); # UI part in app.py
+    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):
+    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]} # Only return messages
+
+def tool_node(state: AgentState):
+    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):
+    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...
+    Previous Context:\n{original_plan_proposal_context}\n---\nInteraction Warnings:\n```json\n{json.dumps(interaction_warnings, indent=2)}\n```\n**CRITICAL REFLECTION STEP:** Analyze warnings, decide if revision is needed, respond ONLY about therapeutics revision based on these warnings."""
+    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:
+    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:
+    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):
+        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:
+        """Invokes the LangGraph app for one turn."""
+        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')) # Ensure keys exist
+            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, a critical error occurred during processing: {e}");
+            return {"messages": state.get('messages', []) + [error_msg], "patient_data": state.get('patient_data'), "summary": state.get('summary'), "interaction_warnings": None}