import json
import os
from pprint import pprint
from tqdm import tqdm
import numpy as np

from app.service.outfit_matcher.dataset import FashionDataset
from app.service.outfit_matcher.outfit_evaluator import OutfitMaterTypeAware, Backbone

if __name__ == '__main__':
    with open("./test_param/recommendation_test.json", "r") as f:
        param = json.load(f)
    fashion_dataset = FashionDataset(param["database"])
    backbone_service = Backbone()
    service = OutfitMaterTypeAware()

    # read feature from vector database
    all_items = param["query"] + param["database"]
    unextracted_item = []
    prepared_feature = {}
    for item in all_items:
        if f'{item["item_name"]}.npy' not in os.listdir("feature"):
            unextracted_item.append(item)
    if len(unextracted_item) > 0:
        extracted_features = backbone_service.get_result(unextracted_item)
        for i, item in enumerate(unextracted_item):
            np.save(f'feature/{item["item_name"]}.npy', extracted_features[i])
    for item in all_items:
        if item["item_name"] not in prepared_feature.keys():
            prepared_feature[item["item_name"]] = np.load(f'feature/{item["item_name"]}.npy')
    for item in tqdm(param["query"] * 10):
        outfits = fashion_dataset.generate_outfit(item, param["topk"], param["max_outfits"])
<<<<<<< HEAD
        scores = service.get_result(outfits, prepared_feature)
=======
        scores, features = service.get_result(outfits)
        # save  features

        # 链接milvus

        # 存入数据库
        # 关闭链接

>>>>>>> 1f23781b16e59bfbcbbb4d252e6a61685267e6c7
        # print(scores)
        # print(len(scores))
        # service.visualize(outfits, scores, param["topk"], best=True,
        #                   output_path=os.path.join(r"D:\PhD_Study\MIXI\mitu\image\123",
        #                                            f"{item['item_name']}_best_{param['topk']}.png"))
        # service.visualize(outfits, scores, param["topk"], best=False,
        #                   output_path=os.path.join(r"D:\PhD_Study\MIXI\mitu\image\123",
        #                                            f"{item['item_name']}_worst_{param['topk']}.png"))
        sorted_indices = np.argsort(scores)[:param["topk"]]  # type-aware
        outfits = [outfits[i] for i in sorted_indices]  # 最好的五个