app.py

import os
import sys
import json
import logging
import re
import hashlib
from datetime import datetime
from typing import List, Dict, Optional, Tuple
from enum import Enum

from fastapi import FastAPI, HTTPException
from fastapi.responses import StreamingResponse
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
import asyncio
from fastapi import Query
from bson import ObjectId
from txagent.txagent import TxAgent
from db.mongo import get_mongo_client

# Logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger("TxAgentAPI")

# App
app = FastAPI(title="TxAgent API", version="2.2.1")  # Version for hash-based analysis

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"], allow_credentials=True,
    allow_methods=["*"], allow_headers=["*"]
)

# Pydantic
class ChatRequest(BaseModel):
    message: str
    temperature: float = 0.7
    max_new_tokens: int = 512
    history: Optional[List[Dict]] = None
    format: Optional[str] = "clean"

# Enums
class RiskLevel(str, Enum):
    NONE = "none"
    LOW = "low"
    MODERATE = "moderate"
    HIGH = "high"
    SEVERE = "severe"

# Globals
agent = None
patients_collection = None
analysis_collection = None
alerts_collection = None

# Helpers
def clean_text_response(text: str) -> str:
    text = re.sub(r'\n\s*\n', '\n\n', text)
    text = re.sub(r'[ ]+', ' ', text)
    return text.replace("**", "").replace("__", "").strip()

def extract_section(text: str, heading: str) -> str:
    try:
        pattern = rf"{re.escape(heading)}:\s*\n(.*?)(?=\n[A-Z][^\n]*:|\Z)"
        match = re.search(pattern, text, re.DOTALL | re.IGNORECASE)
        return match.group(1).strip() if match else ""
    except Exception as e:
        logger.error(f"Section extraction failed for heading '{heading}': {e}")
        return ""

def structure_medical_response(text: str) -> Dict:
    """Improved version that handles both markdown and plain text formats"""
    def extract_improved(text: str, heading: str) -> str:
        patterns = [
            rf"{re.escape(heading)}:\s*\n(.*?)(?=\n\s*\n|\Z)",
            rf"\*\*{re.escape(heading)}\*\*:\s*\n(.*?)(?=\n\s*\n|\Z)",
            rf"{re.escape(heading)}[\s\-]+(.*?)(?=\n\s*\n|\Z)",
            rf"\n{re.escape(heading)}\s*\n(.*?)(?=\n\s*\n|\Z)"
        ]
        
        for pattern in patterns:
            match = re.search(pattern, text, re.DOTALL | re.IGNORECASE)
            if match:
                content = match.group(1).strip()
                content = re.sub(r'^\s*[\-\*]\s*', '', content, flags=re.MULTILINE)
                return content
        return ""

    text = text.replace('**', '').replace('__', '')
    
    return {
        "summary": extract_improved(text, "Summary of Patient's Medical History") or 
                  extract_improved(text, "Summarize the patient's medical history"),
        "risks": extract_improved(text, "Identify Risks or Red Flags") or 
                extract_improved(text, "Risks or Red Flags"),
        "missed_issues": extract_improved(text, "Missed Diagnoses or Treatments") or 
                       extract_improved(text, "What the doctor might have missed"),
        "recommendations": extract_improved(text, "Suggest Next Clinical Steps") or 
                         extract_improved(text, "Suggested Clinical Actions")
    }

def detect_suicide_risk(text: str) -> Tuple[RiskLevel, float, List[str]]:
    """Analyze text for suicide risk factors and return assessment"""
    suicide_keywords = [
        'suicide', 'suicidal', 'kill myself', 'end my life', 
        'want to die', 'self-harm', 'self harm', 'hopeless',
        'no reason to live', 'plan to die'
    ]
    
    # Check for explicit mentions
    explicit_mentions = [kw for kw in suicide_keywords if kw in text.lower()]
    
    if not explicit_mentions:
        return RiskLevel.NONE, 0.0, []
    
    # If found, ask AI for detailed assessment
    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,  # Lower temp for more deterministic responses
            max_new_tokens=256
        )
        
        # Extract JSON from response
        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}")
    
    # Fallback if JSON parsing fails
    risk_score = min(0.1 * len(explicit_mentions), 0.9)  # Cap at 0.9 for fallback
    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

async def create_alert(patient_id: str, risk_data: dict):
    """Create an alert document in the database"""
    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}")

def serialize_patient(patient: dict) -> dict:
    patient_copy = patient.copy()
    if "_id" in patient_copy:
        patient_copy["_id"] = str(patient_copy["_id"])
    return patient_copy

def compute_patient_data_hash(patient: dict) -> str:
    """Compute SHA-256 hash of patient data."""
    serialized = json.dumps(patient, sort_keys=True)  # Sort keys for consistent hashing
    return hashlib.sha256(serialized.encode()).hexdigest()

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}")

        # Check if analysis exists and hash matches
        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  # Skip analysis if data hasn't changed

        # Main clinical analysis
        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)
        
        # Suicide risk assessment
        risk_level, risk_score, risk_factors = detect_suicide_risk(raw)
        suicide_risk = {
            "level": risk_level.value,
            "score": risk_score,
            "factors": risk_factors
        }
        
        # Store analysis with data hash
        analysis_doc = {
            "patient_id": patient_id,
            "timestamp": datetime.utcnow(),
            "summary": structured,
            "suicide_risk": suicide_risk,
            "raw": raw,
            "data_hash": patient_hash  # Store the hash
        }
        
        await analysis_collection.update_one(
            {"patient_id": patient_id},
            {"$set": analysis_doc},
            upsert=True
        )
        
        # Create alert if risk is above threshold
        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}")

async def analyze_all_patients():
    patients = await patients_collection.find({}).to_list(length=None)
    for patient in patients:
        await analyze_patient(patient)
        await asyncio.sleep(0.1)

@app.on_event("startup")
async def startup_event():
    global agent, patients_collection, analysis_collection, alerts_collection

    agent = TxAgent(
        model_name="mims-harvard/TxAgent-T1-Llama-3.1-8B",
        rag_model_name="mims-harvard/ToolRAG-T1-GTE-Qwen2-1.5B",
        enable_finish=True,
        enable_rag=False,
        force_finish=True,
        enable_checker=True,
        step_rag_num=4,
        seed=42
    )
    agent.chat_prompt = (
        "You are a clinical assistant AI. Analyze the patient's data and provide clear clinical recommendations."
    )
    agent.init_model()
    logger.info("✅ TxAgent initialized")

    db = get_mongo_client()["cps_db"]
    patients_collection = db["patients"]
    analysis_collection = db["patient_analysis_results"]
    alerts_collection = db["clinical_alerts"]
    logger.info("📡 Connected to MongoDB")

    asyncio.create_task(analyze_all_patients())

@app.get("/status")
async def status():
    return {
        "status": "running",
        "timestamp": datetime.utcnow().isoformat(),
        "version": "2.2.1"
    }

@app.get("/patients/analysis-results")
async def get_patient_analysis_results(name: Optional[str] = Query(None)):
    try:
        query = {}

        # If a name filter is provided, we search the patients collection first
        if name:
            name_regex = re.compile(name, re.IGNORECASE)
            matching_patients = await patients_collection.find({"full_name": name_regex}).to_list(length=None)
            patient_ids = [p["fhir_id"] for p in matching_patients if "fhir_id" in p]
            if not patient_ids:
                return []
            query = {"patient_id": {"$in": patient_ids}}

        # Find analysis results based on patient_ids (or all if no filter)
        analyses = await analysis_collection.find(query).sort("timestamp", -1).to_list(length=100)

        # Attach full_name to each analysis result
        enriched_results = []
        for analysis in analyses:
            patient = await patients_collection.find_one({"fhir_id": analysis["patient_id"]})
            if patient:
                analysis["full_name"] = patient.get("full_name", "Unknown")
            analysis["_id"] = str(analysis["_id"])
            enriched_results.append(analysis)

        return enriched_results

    except Exception as e:
        logger.error(f"Error fetching analysis results: {e}")
        raise HTTPException(status_code=500, detail="Failed to retrieve analysis results")

@app.post("/chat-stream")
async def chat_stream_endpoint(request: ChatRequest):
    async def token_stream():
        try:
            conversation = [{"role": "system", "content": agent.chat_prompt}]
            if request.history:
                conversation.extend(request.history)
            conversation.append({"role": "user", "content": request.message})

            input_ids = agent.tokenizer.apply_chat_template(
                conversation, add_generation_prompt=True, return_tensors="pt"
            ).to(agent.device)

            output = agent.model.generate(
                input_ids,
                do_sample=True,
                temperature=request.temperature,
                max_new_tokens=request.max_new_tokens,
                pad_token_id=agent.tokenizer.eos_token_id,
                return_dict_in_generate=True
            )

            text = agent.tokenizer.decode(output["sequences"][0][input_ids.shape[1]:], skip_special_tokens=True)
            for chunk in text.split():
                yield chunk + " "
                await asyncio.sleep(0.05)
        except Exception as e:
            logger.error(f"Streaming error: {e}")
            yield f"⚠️ Error: {e}"

    return StreamingResponse(token_stream(), media_type="text/plain")