diff --git a/deploy/python/predict_system.py b/deploy/python/predict_system.py
index bf7271e77..cf8b0a3f6 100644
--- a/deploy/python/predict_system.py
+++ b/deploy/python/predict_system.py
@@ -29,23 +29,6 @@ from utils import logger
 from utils import config
 from utils.get_image_list import get_image_list
 
-
-def split_datafile(data_file, image_root):
-    gallery_images = []
-    gallery_docs = []
-    with open(data_file) as f:
-        lines = f.readlines()
-        for i, line in enumerate(lines):
-            line = line.strip().split("\t")
-            if line[0] == 'image_id':
-                 continue
-            image_file = os.path.join(image_root, line[3])
-            image_doc = line[1]
-            gallery_images.append(image_file)
-            gallery_docs.append(image_doc)
-    return gallery_images, gallery_docs
-
-
 class SystemPredictor(object):
     def __init__(self, config):
 
@@ -54,48 +37,30 @@ class SystemPredictor(object):
         self.det_predictor = DetPredictor(config)
 
         assert 'IndexProcess' in config.keys(), "Index config not found ... "
-        self.indexer(config['IndexProcess'])
-        self.return_k = self.config['IndexProcess']['infer']['return_k']
-        self.search_budget = self.config['IndexProcess']['infer']['search_budget']
-    
-    def indexer(self, config):
-        if 'build' in config.keys() and config['build']['enable']:  # build the index from scratch    
-            with open(config['build']['data_file']) as f:
-                lines = f.readlines()
-            gallery_images, gallery_docs = split_datafile(config['build']['data_file'], config['build']['image_root'])
-            # extract gallery features
-            gallery_features = np.zeros([len(gallery_images), config['build']['embedding_size']], dtype=np.float32)
-            for i, image_file in enumerate(gallery_images):
-                img = cv2.imread(image_file)[:, :, ::-1]
-                rec_feat = self.rec_predictor.predict(img)
-                gallery_features[i,:] = rec_feat
-            # train index 
-            self.Searcher = Graph_Index(dist_type=config['build']['dist_type']) 
-            self.Searcher.build(gallery_vectors=gallery_features, gallery_docs=gallery_docs, 
-                pq_size=config['build']['pq_size'], index_path=config['build']['index_path'])
-            
-        else:   # load local index
-            self.Searcher = Graph_Index(dist_type=config['build']['dist_type']) 
-            self.Searcher.load(config['infer']['index_path'])
+        self.return_k = self.config['IndexProcess']['return_k']
+        self.search_budget = self.config['IndexProcess']['search_budget']
+
+        self.Searcher = Graph_Index(dist_type=config['IndexProcess']['dist_type']) 
+        self.Searcher.load(config['IndexProcess']['index_path'])
 
     def predict(self, img):
         output = []
         results = self.det_predictor.predict(img)
         for result in results:
+            preds = {}
             xmin, ymin, xmax, ymax = result["bbox"].astype("int")
             crop_img = img[xmin:xmax, ymin:ymax, :].copy()
             rec_results = self.rec_predictor.predict(crop_img)
-            result["feature"] = rec_results
-
+            #preds["feature"] = rec_results
+            preds["bbox"] = [xmin, ymin, xmax, ymax]
             scores, docs = self.Searcher.search(query=rec_results, return_k=self.return_k, search_budget=self.search_budget)
-            result["ret_docs"] = docs
-            result["ret_scores"] = scores
+            preds["rec_docs"] = docs
+            preds["rec_scores"] = scores
 
-            output.append(result)
+            output.append(preds)
         return output
             
 
-
 def main(config):
     system_predictor = SystemPredictor(config)
     image_list = get_image_list(config["Global"]["infer_imgs"])
@@ -104,7 +69,7 @@ def main(config):
     for idx, image_file in enumerate(image_list):
         img = cv2.imread(image_file)[:, :, ::-1]
         output = system_predictor.predict(img)
-        #print(output)
+        print(output)
     return