"""AI Diagnostic services: Vision, Audio, and OCR agents.

All outputs are simulated for demonstration purposes.
In production, these would integrate with actual ML models.
"""

import time
import random


def analyze_wound_image(image_file) -> dict:
    """Simulated wound/skin vision analysis agent."""
    time.sleep(1.5)
    stages = [
        {
            "stage": "Stage 2",
            "location": "Sacrum",
            "size_cm": "3.2 x 2.8",
            "infection_signs": False,
            "recommendation": "Reposition every 2 hours. Apply hydrocolloid dressing. Reassess in 72 hours.",
            "confidence": 0.94,
        },
        {
            "stage": "Stage 1",
            "location": "Left Heel",
            "size_cm": "2.0 x 1.5",
            "infection_signs": False,
            "recommendation": "Offload pressure with heel suspension device. Apply moisture barrier cream. Monitor daily.",
            "confidence": 0.91,
        },
    ]
    result = random.choice(stages)
    return {
        "agent": "Vision Diagnostics",
        "result": result,
        "summary": (
            f"**Pressure Ulcer Assessment:** {result['stage']} detected at {result['location']} "
            f"({result['size_cm']} cm). "
            f"{'Signs of infection present.' if result['infection_signs'] else 'No signs of infection.'} "
            f"**Recommendation:** {result['recommendation']} "
            f"(Confidence: {result['confidence']:.0%})"
        ),
    }


def analyze_respiratory_audio(audio_file) -> dict:
    """Simulated respiratory bioacoustic analysis agent."""
    time.sleep(1.5)
    results = [
        {
            "cough_rate_per_hr": 14,
            "cough_type": "Productive",
            "trend": "Increasing vs 24h baseline",
            "pneumonia_risk_pct": 72,
            "recommendation": "Order chest X-ray. Monitor SpO2 continuously. Consider sputum culture.",
        },
        {
            "cough_rate_per_hr": 6,
            "cough_type": "Dry",
            "trend": "Stable",
            "pneumonia_risk_pct": 18,
            "recommendation": "Continue monitoring. Ensure adequate hydration. Incentive spirometry TID.",
        },
    ]
    result = random.choice(results)
    return {
        "agent": "Audio Bioacoustics",
        "result": result,
        "summary": (
            f"**Bioacoustic Analysis:** {result['cough_rate_per_hr']} coughs/hr detected. "
            f"Type: {result['cough_type']}. Trend: {result['trend']}. "
            f"Pneumonia risk: {result['pneumonia_risk_pct']}%. "
            f"**Recommendation:** {result['recommendation']}"
        ),
    }


def process_ocr_form(file) -> dict:
    """Simulated OCR form digitization agent."""
    time.sleep(1.5)
    form_types = [
        {
            "form_type": "Laboratory Order",
            "patient": "Margaret Chen",
            "dob": "05/12/1940",
            "mrn": "MC-402",
            "ordering_physician": "Dr. Sarah Kim",
            "tests_requested": ["CBC with Differential", "CMP", "Urinalysis"],
            "priority": "Routine",
            "icd10_codes": ["R31.9", "N39.0"],
            "confidence": 0.984,
        },
        {
            "form_type": "Medication Administration Record",
            "patient": "Arthur Miller",
            "dob": "03/22/1937",
            "mrn": "AM-112",
            "medications_listed": ["Donepezil 10mg QHS", "Metformin 500mg BID", "Carvedilol 12.5mg BID"],
            "pharmacist_verification": True,
            "confidence": 0.971,
        },
    ]
    result = random.choice(form_types)
    return {
        "agent": "OCR Digitizer",
        "result": result,
        "fhir_ready": True,
        "hl7_version": "v2.5.1",
    }