Update agent.py

agent.py

@@ -1,296 +1,308 @@
-# agent.py
-import requests
+# app.py
+import streamlit as st
 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.pydantic_v1 import BaseModel, Field
-from langchain_core.tools import tool
-from langgraph.prebuilt import ToolExecutor
-from langgraph.graph import StateGraph, END
+# Import agent logic and message types from agent.py
+try:
+    from agent import ClinicalAgent, AgentState, check_red_flags
+    from langchain_core.messages import HumanMessage, AIMessage, ToolMessage
+except ImportError as e:
+    st.error(f"Failed to import from agent.py: {e}. Make sure agent.py is in the same directory.")
+    st.stop()
 
-from typing import Optional, List, Dict, Any, TypedDict, Annotated
-
-# --- Environment Variable Loading ---
+# --- Environment Variable Loading & Validation ---
+load_dotenv()
+# Check keys required by agent.py are present before initializing the agent
 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_DISPLAY = "Llama3-70b (via Groq)"  # Defined in agent.py
+
+# --- 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
+    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... (Using shorter versions for brevity, assume full fields are here)
+        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"
+        )
 
-# --- Configuration & Constants ---
-AGENT_MODEL_NAME = "llama3-70b-8192"
-AGENT_TEMPERATURE = 0.1
-MAX_SEARCH_RESULTS = 3
+        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")
 
-class ClinicalPrompts:
-    SYSTEM_PROMPT = """
-    You are SynapseAI, an expert AI clinical assistant engaged in an interactive consultation... [SYSTEM PROMPT OMITTED FOR BREVITY]
-    """
+        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")
 
-# --- API Constants & Helper Functions ---
-# ... (Keep get_rxcui, get_openfda_label, search_text_list implementations) ...
-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 = 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
+        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")
 
-# --- Clinical Helper Functions ---
-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
+        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=68, key="sb_exam")
 
-# CORRECTED check_red_flags function (again)
-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)]
+        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())
 
-    # Symptom Flags (Separate lines)
-    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).")
+            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)
 
-    # 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")
+            # 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}
+            }
 
-        # CORRECTED Vital Sign Checks - Separate Lines
-        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).")
+            # 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:**")
+                for flag in red_flags:
+                    st.sidebar.warning(f"- {flag.replace('Red Flag: ', '')}")
+            else:
+                st.sidebar.success("No immediate red flags.")
 
-    # 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.")
+            # 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()
 
-    return list(set(flags)) # Unique flags
+    # --- Main Chat Interface Area ---
+    st.header("💬 Clinical Consultation")
+    # Display loop
+    for msg in st.session_state.messages:
+        if isinstance(msg, HumanMessage):
+            with st.chat_message("user"):
+                st.markdown(msg.content)
+        elif isinstance(msg, AIMessage):
+            with st.chat_message("assistant"):
+                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")
+                    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:
+                            for item in ddx:
+                                likelihood = item.get('likelihood', 'Low')
+                                medal = ('🥇' if likelihood.startswith('H') else '🥈' if likelihood.startswith('M') else '🥉')
+                                expander_title = f"{medal} {item.get('diagnosis', 'Unknown')} ({likelihood})"
+                                with st.expander(expander_title):
+                                    st.write(f"**Rationale:** {item.get('rationale', 'N/A')}")
+                        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)
+                            if items and isinstance(items, list):
+                                for it in items:
+                                    st.markdown(f"- {it}")
+                            elif items:
+                                st.markdown(f"- {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()
 
-def format_patient_data_for_prompt(data: dict) -> str:
-    # ... (Keep Corrected Implementation) ...
-    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 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._")
+    
+    # --- 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 'agent' not in st.session_state or not st.session_state.agent:
+            st.error("Agent not initialized. Check logs.")
+            st.stop()
 
+        # 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)
 
-# --- Tool Definitions ---
-# ... (Keep Pydantic Models and Tool Function implementations as before) ...
-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")
+        # Prepare state for the agent
+        current_state_dict = {
+            "messages": st.session_state.messages,
+            "patient_data": st.session_state.patient_data,
+            "summary": st.session_state.get("summary"),
+            "interaction_warnings": None  # Start clean
+        }
 
-@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}"); 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]
+        # Invoke the agent's graph for one turn
+        with st.spinner("SynapseAI is processing..."):
+            try:
+                final_state = st.session_state.agent.invoke_turn(current_state_dict)
+                st.session_state.messages = final_state.get('messages', [])
+                st.session_state.summary = final_state.get('summary')
+            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="❌")
+                st.session_state.messages.append(AIMessage(content=f"Error during processing: {e}"))
 
-# --- 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));
-    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));
-        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
+        st.rerun()
 
-# --- 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";
+    # Disclaimer
+    st.markdown("---")
+    st.warning("**Disclaimer:** SynapseAI is for demonstration...")
 
-# --- 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}
+if __name__ == "__main__":
+    main()