from fastapi import FastAPI, UploadFile, File, HTTPException, WebSocket, WebSocketDisconnect
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse, StreamingResponse
from pydantic import BaseModel
from typing import Optional, Literal
import asyncio
import time
import hashlib
import io

# Import our utilities
import sys
from pathlib import Path
sys.path.append(str(Path(__file__).parent.parent))

from config import get_settings
from utils.model_loader import ModelManager
from utils.image_processing import (
    load_image_from_bytes,
    load_image_from_base64,
    array_to_base64,
    depth_to_colormap,
    create_side_by_side
)
from utils.demo_depth import generate_smart_depth


# Initialize FastAPI app
app = FastAPI(
    title="Dimensio API",
    description="Add Dimension to Everything - High-performance depth estimation and 3D visualization API",
    version="1.0.0"
)

settings = get_settings()

# CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=settings.CORS_ORIGINS,
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


# Global model manager
model_manager = ModelManager()
DEMO_MODE = False  # Will be set to True if no models available


# Request/Response models
class DepthRequest(BaseModel):
    """Request model for depth estimation"""
    image: str  # Base64 encoded image
    model: Literal["small", "large"] = "small"
    output_format: Literal["grayscale", "colormap", "both"] = "colormap"
    colormap: Literal["inferno", "viridis", "plasma", "turbo"] = "inferno"


class DepthResponse(BaseModel):
    """Response model for depth estimation"""
    depth_map: str  # Base64 encoded depth map
    metadata: dict
    processing_time_ms: float


# Startup/shutdown events
@app.on_event("startup")
async def startup_event():
    """Initialize models on startup"""
    print(">> Starting Dimensio API...")

    try:
        # Load small model (fast preview)
        small_model_path = Path(settings.MODEL_CACHE_DIR) / settings.DEPTH_MODEL_SMALL
        if small_model_path.exists():
            model_manager.load_model(
                "small",
                str(small_model_path),
                use_gpu=settings.USE_GPU,
                use_tensorrt=settings.TRT_OPTIMIZATION
            )
            print("[+] Small model loaded")
        else:
            print(f"[!] Small model not found: {small_model_path}")

        # Load large model (high quality)
        large_model_path = Path(settings.MODEL_CACHE_DIR) / settings.DEPTH_MODEL_LARGE
        if large_model_path.exists():
            model_manager.load_model(
                "large",
                str(large_model_path),
                use_gpu=settings.USE_GPU,
                use_tensorrt=settings.TRT_OPTIMIZATION
            )
            print("[+] Large model loaded")
        else:
            print(f"[!] Large model not found: {large_model_path}")

        if not model_manager.models:
            global DEMO_MODE
            DEMO_MODE = True
            print("\n[!] No models loaded - Running in DEMO MODE")
            print("Demo mode uses synthetic depth maps for testing the UI.")
            print("\nTo use real AI models:")
            print("1. Run: python download_models.py")
            print("2. Place ONNX models in models/cache/")
            print("3. Restart the server")

    except Exception as e:
        print(f"[X] Error loading models: {e}")
        print("Server will start but depth estimation will not work.")


@app.on_event("shutdown")
async def shutdown_event():
    """Cleanup on shutdown"""
    print(">> Shutting down Depth Flow Pro API...")


# Health check
@app.get("/")
async def root():
    """API health check"""
    return {
        "name": "Depth Flow Pro API",
        "version": "1.0.0",
        "status": "online",
        "models_loaded": list(model_manager.models.keys())
    }


@app.get("/health")
async def health_check():
    """Detailed health check"""
    return {
        "status": "healthy",
        "models": {
            name: "loaded" for name in model_manager.models.keys()
        },
        "gpu_enabled": settings.USE_GPU,
        "tensorrt_enabled": settings.TRT_OPTIMIZATION
    }


# Depth estimation endpoints
@app.post("/api/v1/depth/preview", response_model=DepthResponse)
async def estimate_depth_preview(file: UploadFile = File(...)):
    """
    Fast depth estimation using small model (preview quality)
    Optimized for speed, ~50-100ms on GPU
    """
    try:
        start_time = time.time()

        # Load image
        image_bytes = await file.read()
        image = load_image_from_bytes(image_bytes)

        # Check if demo mode or use real model
        if DEMO_MODE:
            # Use synthetic depth for demo
            depth = generate_smart_depth(image)
            model_name = "demo"
        else:
            # Get small model
            model = model_manager.get_model("small")
            if model is None:
                raise HTTPException(
                    status_code=503,
                    detail="Small model not loaded. Please check server logs."
                )
            # Run depth estimation
            depth = model.predict(image)
            model_name = "small"

        # Convert to colormap
        depth_colored = depth_to_colormap(depth)

        # Encode to base64
        depth_base64 = array_to_base64(depth_colored, format='PNG')

        processing_time = (time.time() - start_time) * 1000

        return DepthResponse(
            depth_map=depth_base64,
            metadata={
                "model": model_name,
                "input_size": image.shape[:2],
                "output_size": depth.shape[:2],
                "demo_mode": DEMO_MODE
            },
            processing_time_ms=round(processing_time, 2)
        )

    except Exception as e:
        print(f"❌ Error: {type(e).__name__}: {str(e)}")
        import traceback
        traceback.print_exc()
        raise HTTPException(status_code=500, detail=str(e))


@app.post("/api/v1/depth/hq", response_model=DepthResponse)
async def estimate_depth_hq(file: UploadFile = File(...)):
    """
    High-quality depth estimation using large model
    Slower but more accurate, ~500-1500ms on GPU
    """
    try:
        start_time = time.time()

        # Load image
        image_bytes = await file.read()
        image = load_image_from_bytes(image_bytes)

        # Check if demo mode or use real model
        if DEMO_MODE:
            # Use synthetic depth for demo
            depth = generate_smart_depth(image)
            model_name = "demo (HQ)"
        else:
            # Get large model
            model = model_manager.get_model("large")
            if model is None:
                # Fallback to small model if large not available
                model = model_manager.get_model("small")
                if model is None:
                    raise HTTPException(
                        status_code=503,
                        detail="No models loaded. Please check server logs."
                    )
                model_name = "small (fallback)"
            else:
                model_name = "large"

            # Run depth estimation
            depth = model.predict(image)

        # Convert to colormap
        depth_colored = depth_to_colormap(depth)

        # Encode to base64
        depth_base64 = array_to_base64(depth_colored, format='PNG')

        processing_time = (time.time() - start_time) * 1000

        return DepthResponse(
            depth_map=depth_base64,
            metadata={
                "model": model_name,
                "input_size": image.shape[:2],
                "output_size": depth.shape[:2],
                "demo_mode": DEMO_MODE
            },
            processing_time_ms=round(processing_time, 2)
        )

    except Exception as e:
        print(f"❌ Error: {type(e).__name__}: {str(e)}")
        import traceback
        traceback.print_exc()
        raise HTTPException(status_code=500, detail=str(e))


@app.post("/api/v1/depth/estimate")
async def estimate_depth(request: DepthRequest):
    """
    Depth estimation with custom options
    Accepts base64 encoded image
    """
    try:
        start_time = time.time()

        # Load image from base64
        image = load_image_from_base64(request.image)

        # Get model
        model = model_manager.get_model(request.model)
        if model is None:
            raise HTTPException(
                status_code=503,
                detail=f"Model '{request.model}' not loaded"
            )

        # Run depth estimation
        depth = model.predict(image)

        # Process output based on format
        if request.output_format == "grayscale":
            output = (depth * 255).astype('uint8')
            depth_base64 = array_to_base64(output, format='PNG')
        elif request.output_format == "colormap":
            import cv2
            colormap_dict = {
                "inferno": cv2.COLORMAP_INFERNO,
                "viridis": cv2.COLORMAP_VIRIDIS,
                "plasma": cv2.COLORMAP_PLASMA,
                "turbo": cv2.COLORMAP_TURBO
            }
            depth_colored = depth_to_colormap(depth, colormap_dict[request.colormap])
            depth_base64 = array_to_base64(depth_colored, format='PNG')
        else:  # both
            side_by_side = create_side_by_side(image, depth, colormap=True)
            depth_base64 = array_to_base64(side_by_side, format='PNG')

        processing_time = (time.time() - start_time) * 1000

        return DepthResponse(
            depth_map=depth_base64,
            metadata={
                "model": request.model,
                "output_format": request.output_format,
                "colormap": request.colormap,
                "input_size": image.shape[:2],
                "output_size": depth.shape[:2]
            },
            processing_time_ms=round(processing_time, 2)
        )

    except Exception as e:
        print(f"❌ Error: {type(e).__name__}: {str(e)}")
        import traceback
        traceback.print_exc()
        raise HTTPException(status_code=500, detail=str(e))


# WebSocket for streaming
@app.websocket("/api/v1/stream")
async def websocket_endpoint(websocket: WebSocket):
    """
    WebSocket endpoint for real-time depth estimation
    Supports streaming multiple images
    """
    await websocket.accept()

    try:
        while True:
            # Receive image data
            data = await websocket.receive_json()

            if data.get("action") == "estimate":
                start_time = time.time()

                # Load image
                image = load_image_from_base64(data["image"])

                # Get model
                model_name = data.get("model", "small")
                model = model_manager.get_model(model_name)

                if model is None:
                    await websocket.send_json({
                        "error": f"Model '{model_name}' not loaded"
                    })
                    continue

                # Send progress update
                await websocket.send_json({
                    "status": "processing",
                    "progress": 50
                })

                # Run depth estimation
                depth = model.predict(image)

                # Convert to colormap
                depth_colored = depth_to_colormap(depth)
                depth_base64 = array_to_base64(depth_colored, format='PNG')

                processing_time = (time.time() - start_time) * 1000

                # Send result
                await websocket.send_json({
                    "status": "complete",
                    "depth_map": depth_base64,
                    "processing_time_ms": round(processing_time, 2)
                })

    except WebSocketDisconnect:
        print("WebSocket disconnected")
    except Exception as e:
        print(f"WebSocket error: {e}")
        await websocket.send_json({"error": str(e)})


if __name__ == "__main__":
    import uvicorn
    uvicorn.run(
        "main:app",
        host=settings.HOST,
        port=settings.PORT,
        reload=settings.DEBUG
    )