analysis.py

from typing import Optional, Tuple, List
from config import agent, patients_collection, analysis_collection, alerts_collection, logger
from models import RiskLevel
from utils import structure_medical_response, compute_file_content_hash, compute_patient_data_hash, serialize_patient
from datetime import datetime
import asyncio
import json
import re

async def create_alert(patient_id: str, risk_data: dict):
    alert_doc = {
        "patient_id": patient_id,
        "type": "suicide_risk",
        "level": risk_data["level"],
        "score": risk_data["score"],
        "factors": risk_data["factors"],
        "timestamp": datetime.utcnow(),
        "acknowledged": False
    }
    await alerts_collection.insert_one(alert_doc)
    logger.warning(f"⚠️ Created suicide risk alert for patient {patient_id}")

async def analyze_patient_report(patient_id: Optional[str], report_content: str, file_type: str, file_content: bytes):
    identifier = patient_id if patient_id else compute_file_content_hash(file_content)
    report_data = {"identifier": identifier, "content": report_content, "file_type": file_type}
    report_hash = compute_patient_data_hash(report_data)
    logger.info(f"🧾 Analyzing report for identifier: {identifier}")

    existing_analysis = await analysis_collection.find_one({"identifier": identifier, "report_hash": report_hash})
    if existing_analysis:
        logger.info(f"✅ No changes in report data for {identifier}, skipping analysis")
        return existing_analysis

    prompt = (
        "You are a clinical decision support AI. Analyze the following patient report:\n"
        "1. Summarize the patient's medical history.\n"
        "2. Identify risks or red flags (including mental health and suicide risk).\n"
        "3. Highlight missed diagnoses or treatments.\n"
        "4. Suggest next clinical steps.\n"
        f"\nPatient Report ({file_type}):\n{'-'*40}\n{report_content[:10000]}"
    )

    raw_response = agent.chat(
        message=prompt,
        history=[],
        temperature=0.7,
        max_new_tokens=1024
    )
    structured_response = structure_medical_response(raw_response)

    risk_level, risk_score, risk_factors = detect_suicide_risk(raw_response)
    suicide_risk = {
        "level": risk_level.value,
        "score": risk_score,
        "factors": risk_factors
    }

    analysis_doc = {
        "identifier": identifier,
        "patient_id": patient_id,
        "timestamp": datetime.utcnow(),
        "summary": structured_response,
        "suicide_risk": suicide_risk,
        "raw": raw_response,
        "report_hash": report_hash,
        "file_type": file_type
    }

    await analysis_collection.update_one(
        {"identifier": identifier, "report_hash": report_hash},
        {"$set": analysis_doc},
        upsert=True
    )

    if patient_id and risk_level in [RiskLevel.MODERATE, RiskLevel.HIGH, RiskLevel.SEVERE]:
        await create_alert(patient_id, suicide_risk)

    logger.info(f"✅ Stored analysis for identifier {identifier}")
    return analysis_doc

async def analyze_patient(patient: dict):
    try:
        serialized = serialize_patient(patient)
        patient_id = serialized.get("fhir_id")
        patient_hash = compute_patient_data_hash(serialized)
        logger.info(f"🧾 Analyzing patient: {patient_id}")

        existing_analysis = await analysis_collection.find_one({"patient_id": patient_id})
        if existing_analysis and existing_analysis.get("data_hash") == patient_hash:
            logger.info(f"✅ No changes in patient data for {patient_id}, skipping analysis")
            return

        doc = json.dumps(serialized, indent=2)
        message = (
            "You are a clinical decision support AI.\n\n"
            "Given the patient document below:\n"
            "1. Summarize the patient's medical history.\n"
            "2. Identify risks or red flags (including mental health and suicide risk).\n"
            "3. Highlight missed diagnoses or treatments.\n"
            "4. Suggest next clinical steps.\n"
            f"\nPatient Document:\n{'-'*40}\n{doc[:10000]}"
        )

        raw = agent.chat(message=message, history=[], temperature=0.7, max_new_tokens=1024)
        structured = structure_medical_response(raw)
        
        risk_level, risk_score, risk_factors = detect_suicide_risk(raw)
        suicide_risk = {
            "level": risk_level.value,
            "score": risk_score,
            "factors": risk_factors
        }
        
        analysis_doc = {
            "identifier": patient_id,
            "patient_id": patient_id,
            "timestamp": datetime.utcnow(),
            "summary": structured,
            "suicide_risk": suicide_risk,
            "raw": raw,
            "data_hash": patient_hash
        }
        
        await analysis_collection.update_one(
            {"identifier": patient_id},
            {"$set": analysis_doc},
            upsert=True
        )
        
        if risk_level in [RiskLevel.MODERATE, RiskLevel.HIGH, RiskLevel.SEVERE]:
            await create_alert(patient_id, suicide_risk)
            
        logger.info(f"✅ Stored analysis for patient {patient_id}")

    except Exception as e:
        logger.error(f"Error analyzing patient: {e}")

def detect_suicide_risk(text: str) -> Tuple[RiskLevel, float, List[str]]:
    suicide_keywords = [
        'suicide', 'suicidal', 'kill myself', 'end my life', 
        'want to die', 'self-harm', 'self harm', 'hopeless',
        'no reason to live', 'plan to die'
    ]
    explicit_mentions = [kw for kw in suicide_keywords if kw in text.lower()]
    if not explicit_mentions:
        return RiskLevel.NONE, 0.0, []
    
    assessment_prompt = (
        "Assess the suicide risk level based on this text. "
        "Consider frequency, specificity, and severity of statements. "
        "Respond with JSON format: {\"risk_level\": \"low/moderate/high/severe\", "
        "\"risk_score\": 0-1, \"factors\": [\"list of risk factors\"]}\n\n"
        f"Text to assess:\n{text}"
    )
    
    try:
        response = agent.chat(
            message=assessment_prompt,
            history=[],
            temperature=0.2,
            max_new_tokens=256
        )
        json_match = re.search(r'\{.*\}', response, re.DOTALL)
        if json_match:
            assessment = json.loads(json_match.group())
            return (
                RiskLevel(assessment.get("risk_level", "none").lower()),
                float(assessment.get("risk_score", 0)),
                assessment.get("factors", [])
            )
    except Exception as e:
        logger.error(f"Error in suicide risk assessment: {e}")
    
    risk_score = min(0.1 * len(explicit_mentions), 0.9)
    if risk_score > 0.7:
        return RiskLevel.HIGH, risk_score, explicit_mentions
    elif risk_score > 0.4:
        return RiskLevel.MODERATE, risk_score, explicit_mentions
    return RiskLevel.LOW, risk_score, explicit_mentions