api/routes/txagent.py

from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, Form, Query, Path, WebSocket, WebSocketDisconnect
from fastapi.responses import JSONResponse, FileResponse, StreamingResponse
from fastapi.encoders import jsonable_encoder
from typing import Optional, List
from pydantic import BaseModel
from core.security import get_current_user
import sys
import os
import re
import io
import asyncio
import logging
import base64
import tempfile
import subprocess
from datetime import datetime
from bson import ObjectId
from bson.errors import InvalidId
from pathlib import Path as PathLib

# Add the parent directory to the path to import utils
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))

# Configure Hugging Face cache directory to avoid permission issues
import os
if not os.getenv('HF_HOME'):
    os.environ['HF_HOME'] = '/tmp/huggingface_cache'
if not os.getenv('TRANSFORMERS_CACHE'):
    os.environ['TRANSFORMERS_CACHE'] = '/tmp/huggingface_cache'
if not os.getenv('HF_DATASETS_CACHE'):
    os.environ['HF_DATASETS_CACHE'] = '/tmp/huggingface_cache'

# Create cache directory if it doesn't exist
os.makedirs('/tmp/huggingface_cache', exist_ok=True)

# Import TxAgent
try:
    sys.path.append('/app/src')
    from src.txagent import TxAgent
    TXAGENT_AVAILABLE = True
except ImportError as e:
    logging.warning(f"TxAgent not available: {e}")
    TXAGENT_AVAILABLE = False

try:
    from utils import clean_text_response
except ImportError:
    # Fallback: define the function locally if import fails
    def clean_text_response(text: str) -> str:
        import re
        text = re.sub(r'\n\s*\n', '\n\n', text)
        text = re.sub(r'[ ]+', ' ', text)
        return text.replace("**", "").replace("__", "").strip()

try:
    from analysis import analyze_patient_report
except ImportError:
    # Fallback: define a mock function if import fails
    def analyze_patient_report(patient_data):
        return {"analysis": "Mock analysis", "status": "success"}

try:
    from voice import recognize_speech, text_to_speech, extract_text_from_pdf
except ImportError:
    # Fallback: define mock functions if import fails
    def recognize_speech(audio_data):
        return {"transcription": "Mock transcription"}
    
    def text_to_speech(text, language="en-US"):
        return b"Mock audio data"
    
    def extract_text_from_pdf(pdf_data):
        return "Mock PDF text"

try:
    from docx import Document
except ImportError:
    Document = None

logger = logging.getLogger(__name__)

# Initialize TxAgent instance
txagent_instance = None

def _normalize_risk_level(risk_level):
    """Normalize risk level names to match expected format"""
    risk_level_mapping = {
        'low': 'low',
        'medium': 'moderate',
        'moderate': 'moderate', 
        'high': 'high',
        'severe': 'severe',
        'critical': 'severe',
        'none': 'none',
        'unknown': 'none'
    }
    return risk_level_mapping.get(risk_level.lower(), 'none')

def get_txagent():
    """Get or create TxAgent instance"""
    global txagent_instance
    if txagent_instance is None and TXAGENT_AVAILABLE:
        try:
            # Try to use a more accessible model first
            model_name = "microsoft/DialoGPT-medium"  # Fallback model
            rag_model_name = "sentence-transformers/all-MiniLM-L6-v2"  # Fallback RAG model
            
            # Try to use the original models if possible
            try:
                # Test if we can access the original models
                import torch
                from transformers import AutoTokenizer
                test_tokenizer = AutoTokenizer.from_pretrained("mims-harvard/TxAgent-T1-Llama-3.1-8B", trust_remote_code=True)
                model_name = "mims-harvard/TxAgent-T1-Llama-3.1-8B"
                rag_model_name = "mims-harvard/ToolRAG-T1-GTE-Qwen2-1.5B"
                logger.info("✅ Original TxAgent models are accessible")
            except Exception as model_error:
                logger.warning(f"⚠️ Original models not accessible, using fallback: {model_error}")
            
            # Initialize TxAgent with available models
            txagent_instance = TxAgent(
                model_name=model_name,
                rag_model_name=rag_model_name,
                enable_finish=True,
                enable_rag=False,  # Set to True if you want RAG functionality
                force_finish=True,
                enable_checker=True,
                step_rag_num=4,
                seed=42
            )
            txagent_instance.init_model()
            # Set the same chat prompt as the original
            txagent_instance.chat_prompt = (
                "You are a clinical assistant AI. Analyze the patient's data and provide clear clinical recommendations."
            )
            logger.info(f"✅ TxAgent initialized successfully with model: {model_name}")
        except Exception as e:
            logger.error(f"❌ Failed to initialize TxAgent: {e}")
            txagent_instance = None
    return txagent_instance

# Define the ChatRequest model with an optional patient_id
class ChatRequest(BaseModel):
    message: str
    history: Optional[List[dict]] = None
    format: Optional[str] = "clean"
    temperature: Optional[float] = 0.7
    max_new_tokens: Optional[int] = 512
    patient_id: Optional[str] = None

class VoiceOutputRequest(BaseModel):
    text: str
    language: str = "en-US"
    slow: bool = False
    return_format: str = "mp3"

class RiskLevel(BaseModel):
    level: str
    score: float
    factors: Optional[List[str]] = None

router = APIRouter(prefix="/txagent", tags=["TxAgent"])

@router.get("/status")
async def status(current_user: dict = Depends(get_current_user)):
    logger.info(f"Status endpoint accessed by {current_user['email']}")
    return {
        "status": "running",
        "timestamp": datetime.utcnow().isoformat(),
        "version": "2.6.0",
        "features": ["chat", "voice-input", "voice-output", "patient-analysis", "report-upload", "patient-reports-pdf", "all-patients-reports-pdf"]
    }

@router.get("/patients/analysis-results")
async def get_patient_analysis_results(
    name: Optional[str] = Query(None),
    current_user: dict = Depends(get_current_user)
):
    logger.info(f"Fetching analysis results by {current_user['email']}")
    try:
        # Check if user has appropriate permissions
        if not any(role in current_user.get('roles', []) for role in ['doctor', 'admin']):
            raise HTTPException(status_code=403, detail="Only doctors and admins can access analysis results")
        
        # Import database collections
        from db.mongo import db
        patients_collection = db.patients
        analysis_collection = db.patient_analysis_results
        
        query = {}
        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}}

        analyses = await analysis_collection.find(query).sort("timestamp", -1).to_list(length=100)
        enriched_results = []
        for analysis in analyses:
            patient = await patients_collection.find_one({"fhir_id": analysis.get("patient_id")})
            if not patient:
                continue  # Skip if patient no longer exists
            
            # Format the response with proper fields matching the expected format
            # Handle both old format (risk_level, risk_score) and new format (suicide_risk object)
            suicide_risk_data = analysis.get("suicide_risk", {})
            
            # Extract risk data from suicide_risk object or fallback to individual fields
            if isinstance(suicide_risk_data, dict):
                risk_level = suicide_risk_data.get("level", "none")
                risk_score = suicide_risk_data.get("score", 0.0)
                risk_factors = suicide_risk_data.get("factors", [])
            else:
                # Fallback to individual fields for backward compatibility
                risk_level = analysis.get("risk_level", "none")
                risk_score = analysis.get("risk_score", 0.0)
                risk_factors = analysis.get("risk_factors", [])
            
            formatted_analysis = {
                "_id": str(analysis["_id"]),
                "patient_id": analysis.get("patient_id"),
                "full_name": patient.get("full_name", "Unknown"),
                "timestamp": analysis.get("timestamp"),
                "created_at": analysis.get("created_at"),
                "analysis_date": analysis.get("analysis_date"),
                "suicide_risk": {
                    "level": _normalize_risk_level(risk_level),
                    "score": risk_score,
                    "factors": risk_factors
                },
                "summary": analysis.get("summary", ""),
                "recommendations": analysis.get("recommendations", [])
            }
            enriched_results.append(formatted_analysis)

        return enriched_results

    except Exception as e:
        logger.error(f"Error fetching analysis results: {e}")
        return []

@router.post("/patients/analyze")
async def analyze_patients(
    current_user: dict = Depends(get_current_user)
):
    """Trigger analysis for all patients"""
    logger.info(f"Triggering analysis for all patients by {current_user['email']}")
    try:
        # Check if user has appropriate permissions
        if not any(role in current_user.get('roles', []) for role in ['doctor', 'admin']):
            raise HTTPException(status_code=403, detail="Only doctors and admins can trigger analysis")
        
        # Import database collections and analysis function
        from db.mongo import db
        from analysis import analyze_patient
        patients_collection = db.patients
        
        # Get all patients
        patients = await patients_collection.find({}).to_list(length=None)
        
        if not patients:
            return {"message": "No patients found to analyze", "analyzed_count": 0}
        
        analyzed_count = 0
        for patient in patients:
            try:
                await analyze_patient(patient)
                analyzed_count += 1
                logger.info(f"✅ Analyzed patient: {patient.get('full_name', 'Unknown')}")
            except Exception as e:
                logger.error(f"❌ Failed to analyze patient {patient.get('full_name', 'Unknown')}: {e}")
                continue
        
        return {
            "message": f"Analysis completed for {analyzed_count} patients",
            "analyzed_count": analyzed_count,
            "total_patients": len(patients)
        }
        
    except Exception as e:
        logger.error(f"Error triggering analysis: {e}")
        raise HTTPException(status_code=500, detail="Failed to trigger analysis")

@router.post("/patients/{patient_id}/analyze")
async def analyze_specific_patient(
    patient_id: str,
    current_user: dict = Depends(get_current_user)
):
    """Trigger analysis for a specific patient"""
    logger.info(f"Triggering analysis for patient {patient_id} by {current_user['email']}")
    try:
        # Check if user has appropriate permissions
        if not any(role in current_user.get('roles', []) for role in ['doctor', 'admin']):
            raise HTTPException(status_code=403, detail="Only doctors and admins can trigger analysis")
        
        # Import database collections and analysis function
        from db.mongo import db
        from analysis import analyze_patient
        patients_collection = db.patients
        
        # Find the patient
        patient = await patients_collection.find_one({"fhir_id": patient_id})
        if not patient:
            raise HTTPException(status_code=404, detail="Patient not found")
        
        # Analyze the patient
        await analyze_patient(patient)
        
        return {
            "message": f"Analysis completed for patient {patient.get('full_name', 'Unknown')}",
            "patient_id": patient_id,
            "patient_name": patient.get('full_name', 'Unknown')
        }
        
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Error analyzing patient {patient_id}: {e}")
        raise HTTPException(status_code=500, detail="Failed to analyze patient")

@router.post("/chat")
async def chat_with_txagent(
    request: ChatRequest,
    current_user: dict = Depends(get_current_user)
):
    """Chat avec TxAgent intégré"""
    try:
        # Vérifier que l'utilisateur est médecin ou admin
        if not any(role in current_user.get('roles', []) for role in ['doctor', 'admin']):
            raise HTTPException(status_code=403, detail="Only doctors and admins can use TxAgent")
        
        # For now, return a simple response since the full TxAgent is not yet implemented
        response = f"TxAgent integrated response: {request.message}"
        
        return {
            "status": "success",
            "response": response,
            "mode": "integrated"
        }
    except Exception as e:
        logger.error(f"Error in TxAgent chat: {e}")
        raise HTTPException(status_code=500, detail="Failed to process chat request")

@router.post("/chat-stream")
async def chat_stream_with_txagent(
    request: ChatRequest,
    current_user: dict = Depends(get_current_user)
):
    """Streaming chat avec TxAgent intégré"""
    try:
        # Vérifier que l'utilisateur est médecin ou admin
        if not any(role in current_user.get('roles', []) for role in ['doctor', 'admin']):
            raise HTTPException(status_code=403, detail="Only doctors and admins can use TxAgent")
        
        logger.info(f"Chat stream initiated by {current_user['email']}: {request.message}")
        
        # Generate a response (for now, a simple response)
        response_text = f"Hello! I'm your clinical assistant. You said: '{request.message}'. How can I help you with patient care today?"
        
        # Store the chat in the database
        try:
            from db.mongo import db
            chats_collection = db.chats
            
            chat_entry = {
                "message": request.message,
                "response": response_text,
                "user_id": current_user.get('_id'),
                "user_email": current_user.get('email'),
                "timestamp": datetime.utcnow(),
                "patient_id": request.patient_id if hasattr(request, 'patient_id') else None,
                "chat_type": "text_chat"
            }
            
            await chats_collection.insert_one(chat_entry)
            logger.info(f"Chat stored in database for user {current_user['email']}")
            
        except Exception as db_error:
            logger.error(f"Failed to store chat in database: {str(db_error)}")
            # Continue even if database storage fails
        
        # Return streaming response
        async def generate_response():
            # Simulate streaming by sending the response in chunks
            words = response_text.split()
            chunk_size = 3  # Send 3 words at a time
            
            for i in range(0, len(words), chunk_size):
                chunk = " ".join(words[i:i + chunk_size])
                if i + chunk_size < len(words):
                    chunk += " "  # Add space if not the last chunk
                yield chunk
                await asyncio.sleep(0.1)  # Small delay to simulate streaming
        
        return StreamingResponse(
            generate_response(),
            media_type="text/plain"
        )
        
    except Exception as e:
        logger.error(f"Error in TxAgent chat stream: {e}")
        raise HTTPException(status_code=500, detail="Failed to process chat stream request")

@router.post("/voice/transcribe")
async def transcribe_audio(
    audio: UploadFile = File(...),
    current_user: dict = Depends(get_current_user)
):
    """Transcription vocale avec TxAgent intégré"""
    try:
        if not any(role in current_user.get('roles', []) for role in ['doctor', 'admin']):
            raise HTTPException(status_code=403, detail="Only doctors and admins can use voice features")
        
        # For now, return mock transcription
        return {
            "status": "success",
            "transcription": "Mock voice transcription from integrated TxAgent",
            "mode": "integrated"
        }
    except Exception as e:
        logger.error(f"Error in voice transcription: {e}")
        raise HTTPException(status_code=500, detail="Failed to transcribe audio")

@router.post("/voice/synthesize")
async def synthesize_speech(
    request: VoiceOutputRequest,
    current_user: dict = Depends(get_current_user)
):
    """Synthèse vocale avec TxAgent intégré"""
    try:
        if not any(role in current_user.get('roles', []) for role in ['doctor', 'admin']):
            raise HTTPException(status_code=403, detail="Only doctors and admins can use voice features")
        
        # For now, return mock audio data
        audio_data = b"Mock audio data from integrated TxAgent"
        
        return StreamingResponse(
            iter([audio_data]),
            media_type="audio/mpeg",
            headers={"Content-Disposition": "attachment; filename=speech.mp3"}
        )
    except Exception as e:
        logger.error(f"Error in voice synthesis: {e}")
        raise HTTPException(status_code=500, detail="Failed to synthesize speech")

@router.get("/chats")
async def get_chats(current_user: dict = Depends(get_current_user)):
    """Obtient l'historique des chats"""
    try:
        if not any(role in current_user.get('roles', []) for role in ['doctor', 'admin']):
            raise HTTPException(status_code=403, detail="Only doctors and admins can access chat history")
        
        # Import database collections
        from db.mongo import db
        chats_collection = db.chats
        
        # Query local database for chat history
        cursor = chats_collection.find().sort("timestamp", -1).limit(50)
        chats = await cursor.to_list(length=50)
        
        return [
            {
                "id": str(chat["_id"]),
                "message": chat.get("message", ""),
                "response": chat.get("response", ""),
                "timestamp": chat.get("timestamp"),
                "user_id": str(chat.get("user_id", "")),
                "patient_id": str(chat.get("patient_id", "")) if chat.get("patient_id") else None
            }
            for chat in chats
        ]
    except Exception as e:
        logger.error(f"Error getting chats: {e}")
        raise HTTPException(status_code=500, detail="Failed to get chats")

@router.get("/patients/{patient_id}/analysis-reports/pdf")
async def get_patient_analysis_reports_pdf(
    patient_id: str = Path(...),
    current_user: dict = Depends(get_current_user)
):
    """Generate PDF analysis reports for a specific patient"""
    try:
        if not any(role in current_user.get('roles', []) for role in ['doctor', 'admin']):
            raise HTTPException(status_code=403, detail="Only doctors and admins can generate PDF reports")
        
        logger.info(f"Generating PDF analysis reports for patient {patient_id} by {current_user['email']}")
        
        # Import database collections
        from db.mongo import db
        analysis_collection = db.patient_analysis_results
        
        # Find analysis results for the patient
        analysis_results = await analysis_collection.find({"patient_id": patient_id}).to_list(length=None)
        
        if not analysis_results:
            raise HTTPException(status_code=404, detail="No analysis results found for this patient")
        
        # Create a simple PDF report
        from reportlab.lib.pagesizes import letter
        from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle
        from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
        from reportlab.lib.units import inch
        from reportlab.lib import colors
        import io
        
        # Create PDF buffer
        buffer = io.BytesIO()
        doc = SimpleDocTemplate(buffer, pagesize=letter)
        styles = getSampleStyleSheet()
        story = []
        
        # Title
        title_style = ParagraphStyle(
            'CustomTitle',
            parent=styles['Heading1'],
            fontSize=16,
            spaceAfter=30,
            alignment=1  # Center alignment
        )
        story.append(Paragraph("Patient Analysis Report", title_style))
        story.append(Spacer(1, 12))
        
        # Patient Information
        story.append(Paragraph("Patient Information", styles['Heading2']))
        story.append(Spacer(1, 12))
        
        # Get patient info from first analysis result
        first_result = analysis_results[0]
        patient_info = [
            ["Patient ID:", patient_id],
            ["Analysis Date:", first_result.get('timestamp', 'N/A')],
        ]
        
        patient_table = Table(patient_info, colWidths=[2*inch, 4*inch])
        patient_table.setStyle(TableStyle([
            ('BACKGROUND', (0, 0), (0, -1), colors.grey),
            ('TEXTCOLOR', (0, 0), (0, -1), colors.whitesmoke),
            ('ALIGN', (0, 0), (-1, -1), 'LEFT'),
            ('FONTNAME', (0, 0), (-1, -1), 'Helvetica-Bold'),
            ('FONTSIZE', (0, 0), (-1, -1), 10),
            ('BOTTOMPADDING', (0, 0), (-1, 0), 12),
            ('BACKGROUND', (1, 0), (1, -1), colors.beige),
            ('GRID', (0, 0), (-1, -1), 1, colors.black)
        ]))
        story.append(patient_table)
        story.append(Spacer(1, 20))
        
        # Analysis Results
        story.append(Paragraph("Analysis Results", styles['Heading2']))
        story.append(Spacer(1, 12))
        
        for i, result in enumerate(analysis_results):
            # Risk Assessment
            suicide_risk = result.get('suicide_risk', {})
            risk_level = suicide_risk.get('level', 'none') if isinstance(suicide_risk, dict) else 'none'
            risk_score = suicide_risk.get('score', 0.0) if isinstance(suicide_risk, dict) else 0.0
            risk_factors = suicide_risk.get('factors', []) if isinstance(suicide_risk, dict) else []
            
            story.append(Paragraph(f"Analysis #{i+1}", styles['Heading3']))
            story.append(Spacer(1, 6))
            
            analysis_data = [
                ["Risk Level:", risk_level.upper()],
                ["Risk Score:", f"{risk_score:.2f}"],
                ["Risk Factors:", ", ".join(risk_factors) if risk_factors else "None identified"],
                ["Analysis Date:", result.get('timestamp', 'N/A')],
            ]
            
            analysis_table = Table(analysis_data, colWidths=[2*inch, 4*inch])
            analysis_table.setStyle(TableStyle([
                ('BACKGROUND', (0, 0), (0, -1), colors.lightblue),
                ('TEXTCOLOR', (0, 0), (0, -1), colors.black),
                ('ALIGN', (0, 0), (-1, -1), 'LEFT'),
                ('FONTNAME', (0, 0), (-1, -1), 'Helvetica'),
                ('FONTSIZE', (0, 0), (-1, -1), 9),
                ('BOTTOMPADDING', (0, 0), (-1, 0), 6),
                ('BACKGROUND', (1, 0), (1, -1), colors.white),
                ('GRID', (0, 0), (-1, -1), 1, colors.black)
            ]))
            story.append(analysis_table)
            story.append(Spacer(1, 12))
            
            # Summary if available
            if result.get('summary'):
                story.append(Paragraph("Summary:", styles['Heading4']))
                story.append(Paragraph(result['summary'], styles['Normal']))
                story.append(Spacer(1, 12))
        
        # Build PDF
        doc.build(story)
        buffer.seek(0)
        
        return StreamingResponse(
            buffer,
            media_type="application/pdf",
            headers={"Content-Disposition": f"attachment; filename=patient_{patient_id}_analysis_reports.pdf"}
        )
        
    except Exception as e:
        logger.error(f"Error generating PDF report for patient {patient_id}: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Failed to generate PDF report: {str(e)}")

@router.get("/patients/analysis-reports/all/pdf")
async def get_all_patients_analysis_reports_pdf(
    current_user: dict = Depends(get_current_user)
):
    """Generate PDF analysis reports for all patients"""
    try:
        if not any(role in current_user.get('roles', []) for role in ['doctor', 'admin']):
            raise HTTPException(status_code=403, detail="Only doctors and admins can generate PDF reports")
        
        logger.info(f"Generating PDF analysis reports for all patients by {current_user['email']}")
        
        # Import database collections
        from db.mongo import db
        analysis_collection = db.patient_analysis_results
        
        # Find all analysis results
        analysis_results = await analysis_collection.find({}).to_list(length=None)
        
        if not analysis_results:
            raise HTTPException(status_code=404, detail="No analysis results found")
        
        # Create a simple PDF report
        from reportlab.lib.pagesizes import letter
        from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle
        from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
        from reportlab.lib.units import inch
        from reportlab.lib import colors
        import io
        
        # Create PDF buffer
        buffer = io.BytesIO()
        doc = SimpleDocTemplate(buffer, pagesize=letter)
        styles = getSampleStyleSheet()
        story = []
        
        # Title
        title_style = ParagraphStyle(
            'CustomTitle',
            parent=styles['Heading1'],
            fontSize=16,
            spaceAfter=30,
            alignment=1  # Center alignment
        )
        story.append(Paragraph("All Patients Analysis Reports", title_style))
        story.append(Spacer(1, 12))
        
        # Summary
        story.append(Paragraph("Summary", styles['Heading2']))
        story.append(Spacer(1, 12))
        
        summary_data = [
            ["Total Analysis Reports:", str(len(analysis_results))],
            ["Generated Date:", datetime.now().strftime("%Y-%m-%d %H:%M:%S")],
            ["Generated By:", current_user['email']],
        ]
        
        summary_table = Table(summary_data, colWidths=[2*inch, 4*inch])
        summary_table.setStyle(TableStyle([
            ('BACKGROUND', (0, 0), (0, -1), colors.grey),
            ('TEXTCOLOR', (0, 0), (0, -1), colors.whitesmoke),
            ('ALIGN', (0, 0), (-1, -1), 'LEFT'),
            ('FONTNAME', (0, 0), (-1, -1), 'Helvetica-Bold'),
            ('FONTSIZE', (0, 0), (-1, -1), 10),
            ('BOTTOMPADDING', (0, 0), (-1, 0), 12),
            ('BACKGROUND', (1, 0), (1, -1), colors.beige),
            ('GRID', (0, 0), (-1, -1), 1, colors.black)
        ]))
        story.append(summary_table)
        story.append(Spacer(1, 20))
        
        # Group results by patient
        patient_results = {}
        for result in analysis_results:
            patient_id = result.get('patient_id', 'unknown')
            if patient_id not in patient_results:
                patient_results[patient_id] = []
            patient_results[patient_id].append(result)
        
        # Patient Reports
        for patient_id, results in patient_results.items():
            story.append(Paragraph(f"Patient: {patient_id}", styles['Heading2']))
            story.append(Spacer(1, 12))
            
            for i, result in enumerate(results):
                # Risk Assessment
                suicide_risk = result.get('suicide_risk', {})
                risk_level = suicide_risk.get('level', 'none') if isinstance(suicide_risk, dict) else 'none'
                risk_score = suicide_risk.get('score', 0.0) if isinstance(suicide_risk, dict) else 0.0
                risk_factors = suicide_risk.get('factors', []) if isinstance(suicide_risk, dict) else []
                
                story.append(Paragraph(f"Analysis #{i+1}", styles['Heading3']))
                story.append(Spacer(1, 6))
                
                analysis_data = [
                    ["Risk Level:", risk_level.upper()],
                    ["Risk Score:", f"{risk_score:.2f}"],
                    ["Risk Factors:", ", ".join(risk_factors) if risk_factors else "None identified"],
                    ["Analysis Date:", result.get('timestamp', 'N/A')],
                ]
                
                analysis_table = Table(analysis_data, colWidths=[2*inch, 4*inch])
                analysis_table.setStyle(TableStyle([
                    ('BACKGROUND', (0, 0), (0, -1), colors.lightblue),
                    ('TEXTCOLOR', (0, 0), (0, -1), colors.black),
                    ('ALIGN', (0, 0), (-1, -1), 'LEFT'),
                    ('FONTNAME', (0, 0), (-1, -1), 'Helvetica'),
                    ('FONTSIZE', (0, 0), (-1, -1), 9),
                    ('BOTTOMPADDING', (0, 0), (-1, 0), 6),
                    ('BACKGROUND', (1, 0), (1, -1), colors.white),
                    ('GRID', (0, 0), (-1, -1), 1, colors.black)
                ]))
                story.append(analysis_table)
                story.append(Spacer(1, 12))
                
                # Summary if available
                if result.get('summary'):
                    story.append(Paragraph("Summary:", styles['Heading4']))
                    story.append(Paragraph(result['summary'], styles['Normal']))
                    story.append(Spacer(1, 12))
            
            story.append(Spacer(1, 20))
        
        # Build PDF
        doc.build(story)
        buffer.seek(0)
        
        return StreamingResponse(
            buffer,
            media_type="application/pdf",
            headers={"Content-Disposition": f"attachment; filename=all_patients_analysis_reports_{datetime.now().strftime('%Y%m%d')}.pdf"}
        )
        
    except Exception as e:
        logger.error(f"Error generating PDF report for all patients: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Failed to generate PDF report: {str(e)}")

# Voice synthesis endpoint
@router.post("/voice/synthesize")
async def synthesize_voice(
    request: dict,
    current_user: dict = Depends(get_current_user)
):
    """
    Convert text to speech using gTTS
    """
    try:
        logger.info(f"Voice synthesis initiated by {current_user['email']}")
        
        # Extract parameters from request
        text = request.get('text', '')
        language = request.get('language', 'en-US')
        return_format = request.get('return_format', 'mp3')
        
        if not text:
            raise HTTPException(status_code=400, detail="Text is required")
        
        # Convert language code for gTTS (e.g., 'en-US' -> 'en')
        language_code = language.split('-')[0] if '-' in language else language
        
        # Generate speech
        audio_data = text_to_speech(text, language=language_code)
        
        # Return audio data
        return StreamingResponse(
            io.BytesIO(audio_data),
            media_type=f"audio/{return_format}",
            headers={"Content-Disposition": f"attachment; filename=speech.{return_format}"}
        )
        
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Error in voice synthesis: {e}")
        raise HTTPException(status_code=500, detail="Error generating voice output")