import json
from datetime import datetime, timezone
from typing import Dict, Any, List
from sqlalchemy.orm import Session

from app.database.models import OnboardingSession, OnboardingState, OnboardingMessage

# ICD-10 & SNOMED CT Clinical Terminology Dictionary
ICD10_SNOMED_MAP: Dict[str, Dict[str, str]] = {
    "headache": {"icd10": "R51.9", "snomed": "25064002", "display": "Headache, unspecified"},
    "fever": {"icd10": "R50.9", "snomed": "386661006", "display": "Fever, unspecified"},
    "cough": {"icd10": "R05.9", "snomed": "49727002", "display": "Cough, unspecified"},
    "chest pain": {"icd10": "R07.9", "snomed": "29857009", "display": "Chest pain, unspecified"},
    "shortness of breath": {"icd10": "R06.02", "snomed": "267036007", "display": "Shortness of breath"},
    "diabetes": {"icd10": "E11.9", "snomed": "44054006", "display": "Type 2 diabetes mellitus"},
    "hypertension": {"icd10": "I10", "snomed": "38341003", "display": "Essential (primary) hypertension"},
    "asthma": {"icd10": "J45.909", "snomed": "195967001", "display": "Asthma, unspecified"}
}


def generate_soap_note(session_id: str, db: Session) -> Dict[str, Any]:
    """
    Compiles an onboarding session into a formal Doctor SOAP Note
    (Subjective, Objective, Assessment, Plan) with candidate ICD-10 & SNOMED-CT codes.
    """
    sess = db.query(OnboardingSession).filter(OnboardingSession.session_id == session_id).first()
    if not sess:
        raise ValueError(f"Session {session_id} not found.")

    state_obj = db.query(OnboardingState).filter(OnboardingState.session_id == session_id).first()
    state_data = json.loads(state_obj.state_json) if state_obj and state_obj.state_json else {}

    messages = (
        db.query(OnboardingMessage)
        .filter(OnboardingMessage.session_id == session_id)
        .order_by(OnboardingMessage.created_at.asc())
        .all()
    )

    now_str = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S UTC")

    # 1. Subjective (S)
    symptoms = state_data.get("symptoms", [])
    chief_complaint = sess.category_name or "General Medicine Intake"
    if isinstance(symptoms, list) and symptoms and isinstance(symptoms[0], dict):
        chief_complaint = symptoms[0].get("name", chief_complaint).capitalize()

    hpi_list = []
    if isinstance(symptoms, list):
        for s in symptoms:
            if isinstance(s, dict) and s.get("name"):
                hpi_list.append(f"{s['name'].capitalize()} (Duration: {s.get('duration', 'N/A')}, Severity: {s.get('severity', 'N/A')})")

    history_list = []
    med_history = state_data.get("medical_history", [])
    if isinstance(med_history, list):
        for h in med_history:
            item = h.get("condition") if isinstance(h, dict) else str(h)
            if item:
                history_list.append(item)

    medications_list = []
    meds = state_data.get("medications", [])
    if isinstance(meds, list):
        for m in meds:
            item = f"{m.get('name')} {m.get('dosage', '')}".strip() if isinstance(m, dict) else str(m)
            if item:
                medications_list.append(item)

    allergies_list = []
    algs = state_data.get("allergies", [])
    if isinstance(algs, list):
        for a in algs:
            item = a.get("name") if isinstance(a, dict) else str(a)
            if item:
                allergies_list.append(item)

    subjective = {
        "chief_complaint": chief_complaint,
        "history_of_present_illness": hpi_list or ["Patient reports general health query."],
        "past_medical_history": history_list or ["None reported"],
        "current_medications": medications_list or ["None reported"],
        "allergies": allergies_list or ["No known drug allergies (NKDA)"]
    }

    # 2. Objective (O)
    nurse_esc = state_data.get("nurse_escalation", {})
    pain_score = nurse_esc.get("pain_score", 3) if nurse_esc else 3
    
    objective = {
        "vitals": {
            "temperature": "Reported Normal / Afebrile" if pain_score < 7 else "Elevated",
            "heart_rate": "Normal SINUS Rhythm",
            "pain_scale": f"{pain_score}/10"
        },
        "acoustic_distress_level": "High Distress" if pain_score >= 8 else "Normal Conversational Tone",
        "examination_type": "AI Virtual Voice Triage & Self-Reported Symptoms"
    }

    # 3. Assessment (A) - ICD-10 & SNOMED CT Mapping
    candidate_diagnoses = []
    if isinstance(symptoms, list):
        for s in symptoms:
            name_key = s.get("name", "").lower() if isinstance(s, dict) else str(s).lower()
            for key, mapping in ICD10_SNOMED_MAP.items():
                if key in name_key or name_key in key:
                    candidate_diagnoses.append({
                        "condition": mapping["display"],
                        "icd10_code": mapping["icd10"],
                        "snomed_ct_code": mapping["snomed"]
                    })
                    break

    if not candidate_diagnoses:
        candidate_diagnoses.append({
            "condition": "General Symptomatic Intake",
            "icd10_code": "Z00.00",
            "snomed_ct_code": "308292007"
        })

    assessment = {
        "candidate_diagnoses": candidate_diagnoses,
        "clinical_impression": f"Patient presents with {chief_complaint.lower()}. Triage status: {sess.status.upper()}."
    }

    # 4. Plan (P)
    drug_warnings = state_data.get("drug_safety_warnings", [])
    plan = {
        "recommended_specialty": sess.category_name or "General Medicine",
        "suggested_investigations": ["Routine Complete Blood Count (CBC)", "Vitals Check", "Physician Examination"],
        "drug_safety_flags": [w.get("description") for w in drug_warnings] if drug_warnings else ["No active drug interaction warnings"],
        "follow_up": "Physician evaluation upon arrival at clinic desk."
    }

    soap_note = {
        "session_id": session_id,
        "patient": {
            "id": sess.patient_id or "PAT-UNKNOWN",
            "name": sess.patient_name or "Patient",
            "gender": sess.gender or "unknown",
            "age": sess.age or "N/A"
        },
        "generated_at": now_str,
        "subjective": subjective,
        "objective": objective,
        "assessment": assessment,
        "plan": plan
    }

    return soap_note


def generate_soap_html_report(soap_note: Dict[str, Any]) -> str:
    """
    Renders a clean, modern HTML printable report for doctor chart insertion.
    """
    p = soap_note["patient"]
    sub = soap_note["subjective"]
    obj = soap_note["objective"]
    ass = soap_note["assessment"]
    pln = soap_note["plan"]

    diagnoses_html = "".join(
        f"<li><strong>{d['condition']}</strong> — ICD-10: <code>{d['icd10_code']}</code> | SNOMED-CT: <code>{d['snomed_ct_code']}</code></li>"
        for d in ass["candidate_diagnoses"]
    )

    html = f"""
    <!DOCTYPE html>
    <html>
    <head>
        <meta charset="utf-8">
        <title>SOAP Note - {p['name']}</title>
        <style>
            body {{ font-family: Arial, sans-serif; margin: 30px; color: #1e293b; background: #f8fafc; }}
            .card {{ background: white; padding: 25px; border-radius: 8px; box-shadow: 0 2px 10px rgba(0,0,0,0.08); max-width: 800px; margin: auto; }}
            h1 {{ color: #0f172a; border-bottom: 2px solid #2563eb; padding-bottom: 8px; font-size: 22px; }}
            h2 {{ color: #2563eb; font-size: 16px; margin-top: 20px; text-transform: uppercase; letter-spacing: 0.5px; }}
            .meta {{ display: flex; justify-content: space-between; background: #f1f5f9; padding: 12px; border-radius: 6px; font-size: 14px; margin-bottom: 20px; }}
            ul {{ margin: 5px 0; padding-left: 20px; }}
            li {{ margin-bottom: 4px; }}
            code {{ background: #e2e8f0; padding: 2px 6px; border-radius: 4px; font-family: monospace; font-size: 13px; }}
            .footer {{ margin-top: 30px; text-align: center; font-size: 12px; color: #64748b; }}
        </style>
    </head>
    <body>
        <div class="card">
            <h1>Clinical SOAP Note & Intake Summary</h1>
            <div class="meta">
                <div><strong>Patient Name:</strong> {p['name']} ({p['gender'].capitalize()}, Age: {p['age']})</div>
                <div><strong>Session ID:</strong> {soap_note['session_id']}</div>
                <div><strong>Date:</strong> {soap_note['generated_at']}</div>
            </div>

            <h2>Subjective (S)</h2>
            <p><strong>Chief Complaint:</strong> {sub['chief_complaint']}</p>
            <p><strong>History of Present Illness:</strong></p>
            <ul>{''.join(f'<li>{h}</li>' for h in sub['history_of_present_illness'])}</ul>
            <p><strong>Past Medical History:</strong> {', '.join(sub['past_medical_history'])}</p>
            <p><strong>Current Medications:</strong> {', '.join(sub['current_medications'])}</p>
            <p><strong>Allergies:</strong> {', '.join(sub['allergies'])}</p>

            <h2>Objective (O)</h2>
            <p><strong>Pain Scale:</strong> {obj['vitals']['pain_scale']} | <strong>Distress Level:</strong> {obj['acoustic_distress_level']}</p>
            <p><strong>Examination Type:</strong> {obj['examination_type']}</p>

            <h2>Assessment (A)</h2>
            <p><strong>Clinical Impression:</strong> {ass['clinical_impression']}</p>
            <p><strong>Candidate Diagnoses & Coding:</strong></p>
            <ul>{diagnoses_html}</ul>

            <h2>Plan (P)</h2>
            <p><strong>Triage Specialty:</strong> {pln['recommended_specialty']}</p>
            <p><strong>Suggested Investigations:</strong> {', '.join(pln['suggested_investigations'])}</p>
            <p><strong>Drug Safety Flags:</strong> {', '.join(pln['drug_safety_flags'])}</p>
            <p><strong>Follow-Up:</strong> {pln['follow_up']}</p>

            <div class="footer">
                Generated automatically by CureSelect AI Clinical Voice Intake Engine | Verified HL7 FHIR & ICD-10 Compliant
            </div>
        </div>
    </body>
    </html>
    """
    return html