from flask import Flask, request, jsonify, render_template
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
import uuid

import gdown
import matplotlib.pyplot as plt
import warnings

app = Flask(__name__)

# モデル設定と初期化コード
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)

# モデル初期化
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()
}

net = build_model(hypar, device)

# 結果を保存するディレクトリを作成
os.makedirs('static/results', exist_ok=True)

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/api/remove_bg', methods=['POST'])
def remove_bg():
    if 'image' not in request.files:
        return jsonify({'error': 'No image provided'}), 400
    
    file = request.files['image']
    if file.filename == '':
        return jsonify({'error': 'No image selected'}), 400
    
    # 一時ファイルとして保存
    temp_path = f"static/temp_{uuid.uuid4().hex}.png"
    file.save(temp_path)
    
    try:
        # 画像処理
        image_tensor, orig_size = load_image(temp_path, hypar) 
        mask = predict(net, image_tensor, orig_size, hypar, device)
        
        pil_mask = Image.fromarray(mask).convert('L')
        im_rgb = Image.open(temp_path).convert("RGB")
        
        # 結果を保存
        result_id = uuid.uuid4().hex
        rgba_path = f"static/results/{result_id}_rgba.png"
        mask_path = f"static/results/{result_id}_mask.png"
        
        im_rgba = im_rgb.copy()
        im_rgba.putalpha(pil_mask)
        im_rgba.save(rgba_path)
        pil_mask.save(mask_path)
        
        # 一時ファイルを削除
        os.remove(temp_path)
        
        return jsonify({
            'rgba_url': f"/{rgba_path}",
            'mask_url': f"/{mask_path}"
        })
    except Exception as e:
        # エラーが発生したら一時ファイルを削除
        if os.path.exists(temp_path):
            os.remove(temp_path)
        return jsonify({'error': str(e)}), 500

if __name__ == '__main__':
    app.run(debug=True)