import cv2
import os
from PIL import Image
import numpy as np
import torch
from torch.autograd import Variable
from torchvision import transforms
import torch.nn.functional as F
from flask import Flask, request, jsonify, send_file
import io
from werkzeug.utils import secure_filename
import warnings
warnings.filterwarnings("ignore")

# モデルと設定の初期化
device = 'cuda' if torch.cuda.is_available() else 'cpu'

class GOSNormalize(object):
    def __init__(self, mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225]):
        self.mean = mean
        self.std = std

    def __call__(self, image):
        image = normalize(image, self.mean, self.std)
        return image

transform = transforms.Compose([GOSNormalize([0.5,0.5,0.5],[1.0,1.0,1.0])])

def load_image(im_path, hypar):
    im = im_reader(im_path)
    im, im_shp = im_preprocess(im, hypar["cache_size"])
    im = torch.divide(im, 255.0)
    shape = torch.from_numpy(np.array(im_shp))
    return transform(im).unsqueeze(0), shape.unsqueeze(0)

def build_model(hypar, device):
    net = hypar["model"]
    if(hypar["model_digit"]=="half"):
        net.half()
        for layer in net.modules():
            if isinstance(layer, nn.BatchNorm2d):
                layer.float()
    net.to(device)
    if(hypar["restore_model"]!=""):
        net.load_state_dict(torch.load(hypar["model_path"]+"/"+hypar["restore_model"], map_location=device))
        net.to(device)
    net.eval()  
    return net

def predict(net, inputs_val, shapes_val, hypar, device):
    net.eval()
    if(hypar["model_digit"]=="full"):
        inputs_val = inputs_val.type(torch.FloatTensor)
    else:
        inputs_val = inputs_val.type(torch.HalfTensor)
  
    inputs_val_v = Variable(inputs_val, requires_grad=False).to(device)
    ds_val = net(inputs_val_v)[0]
    pred_val = ds_val[0][0,:,:,:]

    pred_val = torch.squeeze(F.upsample(torch.unsqueeze(pred_val,0),(shapes_val[0][0],shapes_val[0][1]),mode='bilinear'))

    ma = torch.max(pred_val)
    mi = torch.min(pred_val)
    pred_val = (pred_val-mi)/(ma-mi)

    if device == 'cuda': torch.cuda.empty_cache()
    return (pred_val.detach().cpu().numpy()*255).astype(np.uint8)

# パラメータ設定
hypar = {
    "model_path": "./saved_models",
    "restore_model": "isnet.pth",
    "interm_sup": False,
    "model_digit": "full",
    "seed": 0,
    "cache_size": [1024, 1024],
    "input_size": [1024, 1024],
    "crop_size": [1024, 1024],
    "model": ISNetDIS()
}

# モデルをビルド
net = build_model(hypar, device)

app = Flask(__name__)
app.config['UPLOAD_FOLDER'] = 'uploads'
os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)

@app.route('/api/remove-background', methods=['POST'])
def remove_background():
    if 'file' not in request.files:
        return jsonify({"error": "No file provided"}), 400
    
    file = request.files['file']
    if file.filename == '':
        return jsonify({"error": "No selected file"}), 400
    
    # ファイルを保存
    filename = secure_filename(file.filename)
    filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
    file.save(filepath)
    
    try:
        # 画像処理
        image_tensor, orig_size = load_image(filepath, hypar) 
        mask = predict(net, image_tensor, orig_size, hypar, device)
        
        pil_mask = Image.fromarray(mask).convert('L')
        im_rgb = Image.open(filepath).convert("RGB")
        im_rgba = im_rgb.copy()
        im_rgba.putalpha(pil_mask)
        
        # 結果をバイトデータとして返す
        output_buffer = io.BytesIO()
        im_rgba.save(output_buffer, format="PNG")
        output_buffer.seek(0)
        
        # 一時ファイルを削除
        os.remove(filepath)
        
        return send_file(
            output_buffer,
            mimetype='image/png',
            as_attachment=True,
            download_name='output.png'
        )
    except Exception as e:
        return jsonify({"error": str(e)}), 500

@app.route('/api/health', methods=['GET'])
def health_check():
    return jsonify({"status": "healthy"}), 200

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000, debug=True)