import base64
import os
import re
import sqlite3
import tempfile
import uuid
from io import BytesIO
from typing import Dict, List, Optional

import cv2
import numpy as np
from PIL import Image
from fastapi import FastAPI, HTTPException, UploadFile, File
from pydantic import BaseModel
from starlette.middleware.cors import CORSMiddleware

from ai import generate_cube

app = FastAPI(title="LUT Transformation API", version="1.0.0")

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


class LUTRequest(BaseModel):
    file_id: str
    user_prompt: str


class LUTTransformRequest(BaseModel):
    file_id: str
    user_prompt: str


class CubeFileResponse(BaseModel):
    file_id: str
    file_name: str


class CubeFileListItem(BaseModel):
    file_id: str
    file_name: str
    upload_date: str


DATABASE_PATH = "cube_files.db"


def init_database():
    """Initialize SQLite database and create tables"""
    conn = sqlite3.connect(DATABASE_PATH)
    cursor = conn.cursor()

    cursor.execute("""
        CREATE TABLE IF NOT EXISTS cube_files (
            id TEXT PRIMARY KEY,
            file_name TEXT NOT NULL,
            file_data BLOB NOT NULL,
            upload_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP
        )
    """)

    conn.commit()
    conn.close()


def save_cube_file_to_db(file_name: str, file_data: bytes) -> str:
    """Save cube file to database and return file ID"""
    file_id = str(uuid.uuid4())
    conn = sqlite3.connect(DATABASE_PATH)
    cursor = conn.cursor()

    cursor.execute(
        "INSERT INTO cube_files (id, file_name, file_data) VALUES (?, ?, ?)",
        (file_id, file_name, file_data)
    )

    conn.commit()
    conn.close()
    return file_id


def get_cube_file_from_db(file_id: str) -> Optional[tuple]:
    """Retrieve cube file from database by ID"""
    conn = sqlite3.connect(DATABASE_PATH)
    cursor = conn.cursor()

    cursor.execute(
        "SELECT file_name, file_data FROM cube_files WHERE id = ?",
        (file_id,)
    )

    result = cursor.fetchone()
    conn.close()
    return result


def list_cube_files_from_db() -> List[tuple]:
    """List all cube files from database"""
    conn = sqlite3.connect(DATABASE_PATH)
    cursor = conn.cursor()

    cursor.execute(
        "SELECT id, file_name, upload_date FROM cube_files ORDER BY upload_date DESC"
    )

    results = cursor.fetchall()
    conn.close()
    return results


class LUTTransformer:
    def __init__(self):
        self.title = ""
        self.size = 0
        self.lut_data = []

    def parse_cube_file(self, filepath: str) -> bool:
        """Parse .cube file and extract LUT data"""
        try:
            with open(filepath, "r") as file:
                lines = file.readlines()

            self.lut_data = []

            for line in lines:
                line = line.strip()

                if not line or line.startswith("#"):
                    continue

                if line.startswith("TITLE"):
                    self.title = line.split('"')[1] if '"' in line else line.split()[1]

                elif line.startswith("LUT_3D_SIZE"):
                    self.size = int(line.split()[1])

                else:
                    rgb_match = re.findall(r"[\d.]+", line)
                    if len(rgb_match) >= 3:
                        r, g, b = map(float, rgb_match[:3])
                        self.lut_data.append([r, g, b])

            return len(self.lut_data) > 0

        except Exception as e:
            print(f"Error parsing cube file: {e}")
            return False

    def apply_json_transformation(self, json_adjustments: Dict) -> bool:
        """Apply JSON color adjustments to LUT data"""
        try:
            lut_array = np.array(self.lut_data)

            for i, (r, g, b) in enumerate(lut_array):
                luminance = 0.299 * r + 0.587 * g + 0.114 * b

                if luminance < 0.33:
                    if "shadows" in json_adjustments:
                        adj = json_adjustments["shadows"]
                        lut_array[i] *= [
                            adj.get("r", 1.0),
                            adj.get("g", 1.0),
                            adj.get("b", 1.0),
                        ]

                elif luminance < 0.66:
                    if "midtones" in json_adjustments:
                        adj = json_adjustments["midtones"]
                        lut_array[i] *= [
                            adj.get("r", 1.0),
                            adj.get("g", 1.0),
                            adj.get("b", 1.0),
                        ]

                else:
                    if "highlights" in json_adjustments:
                        adj = json_adjustments["highlights"]
                        lut_array[i] *= [
                            adj.get("r", 1.0),
                            adj.get("g", 1.0),
                            adj.get("b", 1.0),
                        ]

            if "glob" in json_adjustments:
                global_adj = json_adjustments["glob"]
                lut_array *= [
                    global_adj.get("r", 1.0),
                    global_adj.get("g", 1.0),
                    global_adj.get("b", 1.0),
                ]

            lut_array = np.clip(lut_array, 0.0, 1.0)
            self.lut_data = lut_array.tolist()

            return True

        except Exception as e:
            print(f"Error applying transformation: {e}")
            return False

    def save_cube_file(self, output_path: str, new_title: str = None) -> bool:
        """Save modified LUT as .cube file"""
        try:
            with open(output_path, "w") as file:
                title = new_title if new_title else f"{self.title}_modified"
                file.write(f'TITLE "{title}"\n')
                file.write(f"LUT_3D_SIZE {self.size}\n\n")

                for r, g, b in self.lut_data:
                    file.write(f"{r:.6f} {g:.6f} {b:.6f}\n")

            return True

        except Exception as e:
            print(f"Error saving cube file: {e}")
            return False


def generate_new_cube(user_prompt: str) -> dict:
    """
    Placeholder for AI function that generates JSON adjustments based on user prompt.
    This function should be replaced with the actual AI implementation.
    """
    response = generate_cube(user_prompt)
    return response.model_dump(mode="json")


def apply_lut_to_image(image_path: str, lut_path: str) -> np.ndarray:
    """Apply LUT to image using OpenCV"""
    try:
        img = cv2.imread(image_path)
        if img is None:
            raise ValueError(f"Could not load image: {image_path}")

        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

        transformer = LUTTransformer()
        if not transformer.parse_cube_file(lut_path):
            raise ValueError(f"Could not parse LUT file: {lut_path}")

        lut_data = np.array(transformer.lut_data)
        lut_size = transformer.size

        lut_3d = lut_data.reshape((lut_size, lut_size, lut_size, 3))

        img_normalized = img.astype(np.float32) / 255.0

        result = np.zeros_like(img_normalized)

        for i in range(img.shape[0]):
            for j in range(img.shape[1]):
                r, g, b = img_normalized[i, j]

                r_idx = min(int(r * (lut_size - 1)), lut_size - 1)
                g_idx = min(int(g * (lut_size - 1)), lut_size - 1)
                b_idx = min(int(b * (lut_size - 1)), lut_size - 1)

                result[i, j] = lut_3d[r_idx, g_idx, b_idx]

        result = np.clip(result * 255, 0, 255).astype(np.uint8)
        return result

    except Exception as e:
        print(f"Error applying LUT to image: {e}")
        raise


def create_split_preview(
        original_lut_path: str, new_lut_path: str, sample_image_path: str
) -> str:
    """Create a split preview image and return as base64"""
    try:
        original_processed = apply_lut_to_image(sample_image_path, original_lut_path)
        new_processed = apply_lut_to_image(sample_image_path, new_lut_path)

        height, width = original_processed.shape[:2]
        split_image = np.zeros_like(original_processed)

        mid_point = width // 2
        split_image[:, :mid_point] = original_processed[:, :mid_point]
        split_image[:, mid_point:] = new_processed[:, mid_point:]

        cv2.line(split_image, (mid_point, 0), (mid_point, height), (255, 255, 255), 2)

        pil_image = Image.fromarray(split_image)

        buffer = BytesIO()
        pil_image.save(buffer, format="PNG")
        buffer.seek(0)

        base64_string = base64.b64encode(buffer.getvalue()).decode("utf-8")
        return base64_string

    except Exception as e:
        print(f"Error creating split preview: {e}")
        raise


@app.on_event("startup")
async def startup_event():
    init_database()


@app.get("/")
async def root():
    return {"message": "LUT Transformation API", "version": "1.0.0"}


@app.post("/upload-cube", response_model=CubeFileResponse)
async def upload_cube_file(file: UploadFile = File(...)):
    """
    Upload a .cube file and save it to the database
    """
    try:
        if not file.filename.endswith('.cube'):
            raise HTTPException(status_code=400, detail="Only .cube files are allowed")

        file_data = await file.read()

        file_id = save_cube_file_to_db(file.filename, file_data)

        return CubeFileResponse(
            file_id=file_id,
            file_name=file.filename
        )

    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error uploading file: {str(e)}")


@app.get("/cube-files", response_model=List[CubeFileListItem])
async def list_cube_files():
    """
    List all uploaded cube files with their IDs and names
    """
    try:
        files = list_cube_files_from_db()
        return [
            CubeFileListItem(
                file_id=file_id,
                file_name=file_name,
                upload_date=upload_date
            )
            for file_id, file_name, upload_date in files
        ]
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error listing files: {str(e)}")


@app.post("/transform-lut")
async def transform_lut(request: LUTTransformRequest):
    """
    Transform a LUT based on file ID and user prompt, return split preview image
    """
    try:
        file_data = get_cube_file_from_db(request.file_id)
        if not file_data:
            raise HTTPException(status_code=404, detail="Cube file not found")

        file_name, cube_data = file_data

        with tempfile.NamedTemporaryFile(
                mode="wb", suffix=".cube", delete=False
        ) as temp_cube:
            temp_cube.write(cube_data)
            original_cube_path = temp_cube.name

        try:
            adjustments = generate_new_cube(request.user_prompt)
            transformer = LUTTransformer()
            if not transformer.parse_cube_file(original_cube_path):
                raise HTTPException(status_code=400, detail="Failed to parse cube file")

            if not transformer.apply_json_transformation(adjustments):
                raise HTTPException(
                    status_code=500, detail="Failed to apply transformations"
                )

            with tempfile.NamedTemporaryFile(
                    mode="w", suffix=".cube", delete=False
            ) as temp_new_cube:
                new_cube_path = temp_new_cube.name

            if not transformer.save_cube_file(
                    new_cube_path, f"{transformer.title}_AI_Modified"
            ):
                raise HTTPException(
                    status_code=500, detail="Failed to save new cube file"
                )

            sample_image_path = "sample.jpg"
            if not os.path.exists(sample_image_path):
                raise HTTPException(status_code=404, detail="Sample image not found")

            split_preview_base64 = create_split_preview(
                original_cube_path, new_cube_path, sample_image_path
            )

            return {
                "success": True,
                "message": "LUT transformation completed successfully",
                "file_name": file_name,
                "adjustments_applied": adjustments,
                "split_preview_base64": split_preview_base64,
            }

        finally:
            if os.path.exists(original_cube_path):
                os.unlink(original_cube_path)
            if "new_cube_path" in locals() and os.path.exists(new_cube_path):
                os.unlink(new_cube_path)

    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")


@app.get("/health")
async def health_check():
    return {"status": "healthy", "sample_image_exists": os.path.exists("sample.jpg")}