endpoints.py

from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, Query, Form
from fastapi.responses import StreamingResponse, JSONResponse
from fastapi.encoders import jsonable_encoder
from typing import Optional, List
from pydantic import BaseModel
from auth import get_current_user
from utils import clean_text_response
from analysis import analyze_patient_report
from voice import recognize_speech, text_to_speech, extract_text_from_pdf
from docx import Document
import re
import io
from datetime import datetime
from bson import ObjectId
import asyncio
from bson.errors import InvalidId

# 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  # Added optional patient_id field

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

def create_router(agent, logger, patients_collection, analysis_collection, users_collection):
    router = APIRouter()

    @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"]
        }

    @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:
            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
                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")

    @router.post("/chat-stream")
    async def chat_stream_endpoint(
        request: ChatRequest,
        current_user: dict = Depends(get_current_user)
    ):
        logger.info(f"Chat stream initiated by {current_user['email']}")
        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)
                cleaned_text = clean_text_response(text)
                full_response = ""

                # Store chat session in database
                chat_entry = {
                    "user_id": current_user["email"],
                    "patient_id": request.patient_id,  # Now safely optional, defaults to None
                    "message": request.message,
                    "response": cleaned_text,
                    "chat_type": "chat",
                    "timestamp": datetime.utcnow(),
                    "temperature": request.temperature,
                    "max_new_tokens": request.max_new_tokens
                }
                result = await analysis_collection.insert_one(chat_entry)
                chat_entry["_id"] = str(result.inserted_id)

                for chunk in cleaned_text.split():
                    full_response += chunk + " "
                    yield chunk + " "
                    await asyncio.sleep(0.05)

                # Update chat entry with full response
                await analysis_collection.update_one(
                    {"_id": result.inserted_id},
                    {"$set": {"response": full_response}}
                )

            except Exception as e:
                logger.error(f"Streaming error: {e}")
                yield f"⚠️ Error: {e}"

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

    @router.get("/chats")
    async def get_chats(
        current_user: dict = Depends(get_current_user)
    ):
        logger.info(f"Fetching chats for {current_user['email']}")
        try:
            chats = await analysis_collection.find({"user_id": current_user["email"], "chat_type": "chat"}).sort("timestamp", -1).to_list(length=100)
            return [
                {
                    "id": str(chat["_id"]),
                    "title": chat.get("message", "Untitled Chat")[:30],  # First 30 chars of message as title
                    "timestamp": chat["timestamp"].isoformat(),
                    "message": chat["message"],
                    "response": chat["response"]
                }
                for chat in chats
            ]
        except Exception as e:
            logger.error(f"Error fetching chats: {e}")
            raise HTTPException(status_code=500, detail="Failed to retrieve chats")

    @router.post("/voice/transcribe")
    async def transcribe_voice(
        audio: UploadFile = File(...),
        language: str = Query("en-US", description="Language code for speech recognition"),
        current_user: dict = Depends(get_current_user)
    ):
        logger.info(f"Voice transcription initiated by {current_user['email']}")
        try:
            audio_data = await audio.read()
            if not audio.filename.lower().endswith(('.wav', '.mp3', '.ogg', '.flac')):
                raise HTTPException(status_code=400, detail="Unsupported audio format")
            
            text = recognize_speech(audio_data, language)
            return {"text": text}
        
        except HTTPException:
            raise
        except Exception as e:
            logger.error(f"Error in voice transcription: {e}")
            raise HTTPException(status_code=500, detail="Error processing voice input")

    @router.post("/voice/synthesize")
    async def synthesize_voice(
        request: VoiceOutputRequest,
        current_user: dict = Depends(get_current_user)
    ):
        logger.info(f"Voice synthesis initiated by {current_user['email']}")
        try:
            audio_data = text_to_speech(request.text, request.language, request.slow)
            
            if request.return_format == "base64":
                return {"audio": base64.b64encode(audio_data).decode('utf-8')}
            else:
                return StreamingResponse(
                    io.BytesIO(audio_data),
                    media_type="audio/mpeg",
                    headers={"Content-Disposition": "attachment; filename=speech.mp3"}
                )
        
        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")

    @router.post("/voice/chat")
    async def voice_chat_endpoint(
        audio: UploadFile = File(...),
        language: str = Query("en-US", description="Language code for speech recognition"),
        temperature: float = Query(0.7, ge=0.1, le=1.0),
        max_new_tokens: int = Query(512, ge=50, le=1024),
        current_user: dict = Depends(get_current_user)
    ):
        logger.info(f"Voice chat initiated by {current_user['email']}")
        try:
            audio_data = await audio.read()
            user_message = recognize_speech(audio_data, language)
            
            chat_response = agent.chat(
                message=user_message,
                history=[],
                temperature=temperature,
                max_new_tokens=max_new_tokens
            )
            
            audio_data = text_to_speech(chat_response, language.split('-')[0])
            
            # Store voice chat in database
            chat_entry = {
                "user_id": current_user["email"],
                "patient_id": None,
                "message": user_message,
                "response": chat_response,
                "chat_type": "voice_chat",
                "timestamp": datetime.utcnow(),
                "temperature": temperature,
                "max_new_tokens": max_new_tokens
            }
            result = await analysis_collection.insert_one(chat_entry)
            chat_entry["_id"] = str(result.inserted_id)

            return StreamingResponse(
                io.BytesIO(audio_data),
                media_type="audio/mpeg",
                headers={"Content-Disposition": "attachment; filename=response.mp3"}
            )
        
        except HTTPException:
            raise
        except Exception as e:
            logger.error(f"Error in voice chat: {e}")
            raise HTTPException(status_code=500, detail="Error processing voice chat")

    @router.post("/analyze-report")
    async def analyze_clinical_report(
        file: UploadFile = File(...),
        patient_id: Optional[str] = Form(None),
        temperature: float = Form(0.5),
        max_new_tokens: int = Form(1024),
        current_user: dict = Depends(get_current_user)
    ):
        logger.info(f"Report analysis initiated by {current_user['email']}")
        try:
            content_type = file.content_type
            allowed_types = [
                'application/pdf',
                'text/plain',
                'application/vnd.openxmlformats-officedocument.wordprocessingml.document'
            ]

            if content_type not in allowed_types:
                raise HTTPException(
                    status_code=400,
                    detail=f"Unsupported file type: {content_type}. Supported types: PDF, TXT, DOCX"
                )

            file_content = await file.read()

            if content_type == 'application/pdf':
                text = extract_text_from_pdf(file_content)
            elif content_type == 'text/plain':
                text = file_content.decode('utf-8')
            elif content_type == 'application/vnd.openxmlformats-officedocument.wordprocessingml.document':
                doc = Document(io.BytesIO(file_content))
                text = "\n".join([para.text for para in doc.paragraphs])
            else:
                raise HTTPException(status_code=400, detail="Unsupported file type")

            text = clean_text_response(text)
            if len(text.strip()) < 50:
                raise HTTPException(
                    status_code=400,
                    detail="Extracted text is too short (minimum 50 characters required)"
                )

            analysis = await analyze_patient_report(
                patient_id=patient_id,
                report_content=text,
                file_type=content_type,
                file_content=file_content
            )

            if "_id" in analysis and isinstance(analysis["_id"], ObjectId):
                analysis["_id"] = str(analysis["_id"])
            if "timestamp" in analysis and isinstance(analysis["timestamp"], datetime):
                analysis["timestamp"] = analysis["timestamp"].isoformat()

            return JSONResponse(content=jsonable_encoder({
                "status": "success",
                "analysis": analysis,
                "patient_id": patient_id,
                "file_type": content_type,
                "file_size": len(file_content)
            }))

        except HTTPException:
            raise
        except Exception as e:
            logger.error(f"Error in report analysis: {str(e)}")
            raise HTTPException(
                status_code=500,
                detail=f"Failed to analyze report: {str(e)}"
            )

    @router.delete("/patients/{patient_id}")
    async def delete_patient(
        patient_id: str,
        current_user: dict = Depends(get_current_user)
    ):
        logger.info(f"Patient deletion initiated by {current_user['email']} for patient {patient_id}")
        try:
            # Check if the patient exists
            patient = await patients_collection.find_one({"fhir_id": patient_id})
            if not patient:
                raise HTTPException(status_code=404, detail="Patient not found")

            # Check if the current user is authorized (e.g., created_by matches or is admin)
            if patient.get("created_by") != current_user["email"] and not current_user.get("is_admin", False):
                raise HTTPException(status_code=403, detail="Not authorized to delete this patient")

            # Delete all analyses and chats associated with this patient
            await analysis_collection.delete_many({"patient_id": patient_id})
            logger.info(f"Deleted analyses and chats for patient {patient_id}")

            # Delete the patient
            await patients_collection.delete_one({"fhir_id": patient_id})
            logger.info(f"Patient {patient_id} deleted successfully")

            return {"status": "success", "message": f"Patient {patient_id} and associated analyses/chats deleted"}

        except HTTPException:
            raise
        except Exception as e:
            logger.error(f"Error deleting patient {patient_id}: {str(e)}")
            raise HTTPException(status_code=500, detail=f"Failed to delete patient: {str(e)}")

    return router