from fastapi import APIRouter, File, UploadFile, Form, Request
from starlette.concurrency import run_in_threadpool
from starlette.responses import JSONResponse
from typing import List, Optional
import numpy as np
import cv2
import base64
import requests
from app.services.face_recognition import face_engine
from app.core.database import db
from app.models.schemas import AttendanceResponse, LightingAnalysis, ImageRequest
from datetime import datetime
from app.core.config import settings
from app.utils.anti_spoofing import anti_spoofing
import logging

logger = logging.getLogger("face-service-api")
router = APIRouter()

# --- HELPER FUNCTIONS FOR OFF-THREAD EXECUTION ---

def decode_image(contents: bytes) -> Optional[np.ndarray]:
    """Decodes image bytes to a numpy array (OpenCV format)."""
    nparr = np.frombuffer(contents, np.uint8)
    return cv2.imdecode(nparr, cv2.IMREAD_COLOR)

def encode_image_base64(img: np.ndarray, quality: int = 60) -> str:
    """Encodes a numpy image to base64 string."""
    _, buffer = cv2.imencode('.jpg', img, [cv2.IMWRITE_JPEG_QUALITY, quality])
    return base64.b64encode(buffer).decode('utf-8')

def calculate_average_embedding(embeddings: List[np.ndarray]) -> Optional[np.ndarray]:
    """Calculates normalized average embedding from a list."""
    if not embeddings:
        return None
    avg = np.mean(embeddings, axis=0)
    norm = np.linalg.norm(avg)
    return avg / norm if norm > 0 else avg

def compare_embeddings(target: np.ndarray, stored: np.ndarray) -> float:
    """Calculates cosine similarity between two embeddings."""
    return float(np.dot(target, stored))

async def log_spoof_attempt(student_id: str, img: np.ndarray, score: float, lighting_data: dict = None):
    """Log spoofing to core server without blocking."""
    try:
        img_base64 = await run_in_threadpool(encode_image_base64, img)
        
        payload = {
            "studentId": student_id,
            "timestamp": datetime.utcnow().isoformat(),
            "livenessScore": float(score),
            "snapshot": img_base64,
            "lighting_quality": lighting_data.get("lighting_quality") if lighting_data else "UNKNOWN",
            "brightness": lighting_data.get("brightness") if lighting_data else 0
        }
        
        await run_in_threadpool(
            requests.post, 
            f"{settings.CORE_SERVER_URL}/api/v1/audit/spoof-attempt", 
            json=payload, 
            timeout=5,
            headers={"X-API-Key": getattr(settings, 'FACE_API_KEY', '')}
        )
    except Exception as e:
        logger.error(f"Failed to log spoof attempt: {e}")

# --- API ENDPOINTS ---

@router.post("/register", status_code=201)
async def register(
    studentId: str = Form(...),
    ownerAdmin: str = Form("SYSTEM"),
    files: List[UploadFile] = File(...)
):
    logger.info(f"Registering student {studentId} with {len(files)} images")
    
    embeddings = []
    for file in files:
        contents = await file.read()
        img = await run_in_threadpool(decode_image, contents)
        
        if img is None:
            continue
            
        emb = await run_in_threadpool(face_engine.extract_embedding, img)
        if emb is not None:
            embeddings.append(emb)

    if not embeddings:
        return JSONResponse(
            status_code=400,
            content={"success": False, "error": "No faces detected in the provided images."}
        )

    # Process embeddings (CPU heavy)
    avg_embedding = await run_in_threadpool(calculate_average_embedding, embeddings)
    
    # Store in MongoDB (Async IO)
    student_data = {
        "faceEmbedding": avg_embedding.tolist(),
        "last_updated": datetime.utcnow()
    }
    
    collection = db.get_collection()
    existing = await collection.find_one({"studentId": studentId, "ownerAdmin": ownerAdmin})
    
    if existing:
        await collection.update_one({"studentId": studentId, "ownerAdmin": ownerAdmin}, {"$set": student_data})
        msg = "Student face recorded updated."
    else:
        student_data["studentId"] = studentId
        student_data["ownerAdmin"] = ownerAdmin
        await collection.insert_one(student_data)
        msg = "New student face record created."

    return {
        "success": True, 
        "message": msg, 
        "studentId": studentId, 
        "faces_processed": len(embeddings)
    }

@router.post("/check-lighting", response_model=LightingAnalysis)
async def check_lighting(request: ImageRequest):
    try:
        # Image is base64
        _, encoded = request.image.split(",", 1) if "," in request.image else (None, request.image)
        contents = base64.b64decode(encoded)
        img = await run_in_threadpool(decode_image, contents)
        
        if img is None:
            return JSONResponse(
                status_code=400, 
                content={"success": False, "error": "Invalid image data."}
            )

        lighting_result = await run_in_threadpool(anti_spoofing.analyze_lighting, img)
        lighting_result["success"] = True
        return lighting_result
    except Exception as e:
        return JSONResponse(
            status_code=400, 
            content={"success": False, "error": f"Error processing image: {str(e)}"}
        )

@router.post("/mark-attendance")
async def mark_attendance(
    file: UploadFile = File(...),
    studentId: str = Form(...),
    ownerAdmin: str = Form("SYSTEM")
):
    contents = await file.read()
    img = await run_in_threadpool(decode_image, contents)
    
    if img is None:
        return JSONResponse(
            status_code=400, 
            content={"success": False, "error": "Invalid image file."}
        )

    # All heavy operations in threadpool
    lighting_result = await run_in_threadpool(anti_spoofing.analyze_lighting, img)
    face_data_full = await run_in_threadpool(face_engine.process_complete, img)
    
    target_embedding = face_data_full["embedding"]
    face_data = face_data_full["eye_data"] if face_data_full["face_detected"] else None

    # Liveness Detection (LBP is heavy)
    liveness_score, liveness_passed = await run_in_threadpool(anti_spoofing.check_liveness_lbp, img)
    
    if not liveness_passed:
        await log_spoof_attempt(studentId, img, liveness_score, lighting_result)
        return {
            "success": False,
            "status": "SPOOF_DETECTED",
            "error": "Possible photo or screen detected. Please use your real face.",
            "liveness_score": float(liveness_score),
            "liveness_passed": False,
            "lighting_quality": lighting_result["lighting_quality"]
        }

    # Blink Detection (EAR)
    blink_detected = False
    if face_data:
        left_ear = anti_spoofing.calculate_ear(face_data["left_eye"])
        right_ear = anti_spoofing.calculate_ear(face_data["right_eye"])
        avg_ear = (left_ear + right_ear) / 2.0
        blink_detected = avg_ear < settings.BLINK_EAR_THRESHOLD

    if target_embedding is None:
        return {
            "success": False,
            "status": "failed",
            "error": "No face detected or processing failed.",
            "liveness_score": float(liveness_score),
            "liveness_passed": True
        }

    # Fetch stored embedding
    collection = db.get_collection()
    doc = await collection.find_one({"studentId": studentId, "ownerAdmin": ownerAdmin})
    
    if not doc or "faceEmbedding" not in doc:
        return {
            "success": False,
            "status": "failed",
            "error": "Student not registered for face recognition.",
            "liveness_score": float(liveness_score),
            "liveness_passed": True
        }

    stored_embedding = await run_in_threadpool(np.array, doc["faceEmbedding"])
    sim = await run_in_threadpool(compare_embeddings, target_embedding, stored_embedding)
    
    threshold = settings.FACE_SIMILARITY_THRESHOLD
    
    if sim > threshold:
        return {
            "success": True,
            "studentId": studentId,
            "confidence": float(sim),
            "status": "success",
            "message": "Face recognized successfully.",
            "liveness_score": float(liveness_score),
            "blink_detected": blink_detected,
            "lighting_quality": lighting_result["lighting_quality"]
        }
    else:
        return {
            "success": False,
            "confidence": float(sim),
            "status": "failed",
            "error": "Face mismatch. Please try again.",
            "liveness_score": float(liveness_score)
        }

@router.post("/detect")
async def detect_face(file: UploadFile = File(...)):
    contents = await file.read()
    img = await run_in_threadpool(decode_image, contents)
    
    if img is None:
        return JSONResponse(
            status_code=400, 
            content={"success": False, "error": "Invalid image file."}
        )

    detected, guidance = await run_in_threadpool(face_engine.detect_only, img)

    return {
        "success": True,
        "detected": detected, 
        "guidance": guidance,
        "status": "normal"
    }