diff --git a/mini-nav/feature_retrieval.py b/mini-nav/feature_retrieval.py
index b3eec8a..ed0f1a5 100644
--- a/mini-nav/feature_retrieval.py
+++ b/mini-nav/feature_retrieval.py
@@ -1,4 +1,4 @@
-from typing import cast
+from typing import Any, Dict, List, Optional, cast
 
 import torch
 from database import db_manager
@@ -8,7 +8,14 @@ from transformers import AutoImageProcessor, AutoModel
 
 
 @torch.no_grad()
-def establish_database(processor, model, images, labels, batch_size=64):
+def establish_database(
+    processor,
+    model,
+    images: List[Any],
+    labels: List[int] | List[str],
+    batch_size=64,
+    label_map: Optional[Dict[int, str] | List[str]] = None,
+):
     device = model.device
     model.eval()
 
@@ -29,7 +36,13 @@ def establish_database(processor, model, images, labels, batch_size=64):
 
         # 迁移输出到CPU
         cls_tokens = cls_tokens.cpu()
-        batch_labels = labels[i : i + batch_size]
+        batch_labels = (
+            labels[i : i + batch_size]
+            if label_map is None
+            else list(
+                map(lambda x: label_map[cast(int, x)], labels[i : i + batch_size])
+            )
+        )
         actual_batch_size = len(batch_labels)
 
         # 存库
@@ -44,10 +57,28 @@ def establish_database(processor, model, images, labels, batch_size=64):
 if __name__ == "__main__":
     train_dataset = load_dataset("uoft-cs/cifar10", split="train")
     train_dataset = cast(Dataset, train_dataset)
+    label_map = [
+        "airplane",
+        "automobile",
+        "bird",
+        "cat",
+        "deer",
+        "dog",
+        "frog",
+        "horse",
+        "ship",
+        "truck",
+    ]
 
     processor = AutoImageProcessor.from_pretrained(
         "facebook/dinov2-large", device_map="cuda"
     )
     model = AutoModel.from_pretrained("facebook/dinov2-large", device_map="cuda")
 
-    establish_database(processor, model, train_dataset["img"], train_dataset["label"])
+    establish_database(
+        processor,
+        model,
+        train_dataset["img"],
+        train_dataset["label"],
+        label_map=label_map,
+    )
diff --git a/mini-nav/visualizer/app.py b/mini-nav/visualizer/app.py
index fe4adee..2425d5a 100644
--- a/mini-nav/visualizer/app.py
+++ b/mini-nav/visualizer/app.py
@@ -1,11 +1,30 @@
+import datetime
 from typing import Optional
 
 import dash_ag_grid as dag
 import dash_mantine_components as dmc
-from dash import Dash
+from dash import Dash, Input, Output, State, callback, dcc, html
 from database import db_manager
 
 
+def parse_contents(contents, filename, date):
+    return html.Div(
+        [
+            html.H5(filename),
+            html.H6(datetime.datetime.fromtimestamp(date)),
+            # HTML images accept base64 encoded strings in the same format
+            # that is supplied by the upload
+            html.Img(src=contents),
+            html.Hr(),
+            html.Div("Raw Content"),
+            html.Pre(
+                contents[0:200] + "...",
+                style={"whiteSpace": "pre-wrap", "wordBreak": "break-all"},
+            ),
+        ]
+    )
+
+
 class APP(Dash):
     """Singleton Dash Application"""
 
@@ -19,7 +38,12 @@ class APP(Dash):
     def __init__(self):
         super().__init__(__name__)
 
-        df = db_manager.table.search().select(["id", "label", "vector"]).to_polars()
+        df = (
+            db_manager.table.search()
+            .select(["id", "label", "vector"])
+            .limit(1000)
+            .to_polars()
+        )
 
         columnDefs = [
             {"headerName": column.capitalize(), "field": column}
@@ -27,11 +51,55 @@ class APP(Dash):
         ]
 
         self.layout = dmc.MantineProvider(
-            dag.AgGrid(
-                rowData=df.to_dicts(),
-                columnDefs=columnDefs,
+            dmc.Container(
+                dmc.Flex(
+                    [
+                        dcc.Upload(
+                            id="upload-image",
+                            children=html.Div(
+                                ["Drag and Drop or ", html.A("Select Files")]
+                            ),
+                            style={
+                                "width": "100%",
+                                "height": "60px",
+                                "lineHeight": "60px",
+                                "borderWidth": "1px",
+                                "borderStyle": "dashed",
+                                "borderRadius": "5px",
+                                "textAlign": "center",
+                                "margin": "10px",
+                            },
+                            # Allow multiple files to be uploaded
+                            multiple=True,
+                        ),
+                        html.Div(id="output-image-upload"),
+                        dag.AgGrid(
+                            rowData=df.to_dicts(),
+                            columnDefs=columnDefs,
+                        ),
+                    ],
+                    gap="md",
+                    justify="center",
+                    align="center",
+                    direction="column",
+                    wrap="wrap",
+                ),
             )
         )
 
+    @callback(
+        Output("output-image-upload", "children"),
+        Input("upload-image", "contents"),
+        State("upload-image", "filename"),
+        State("upload-image", "last_modified"),
+    )
+    def update_output(list_of_contents, list_of_names, list_of_dates):
+        if list_of_contents is not None:
+            children = [
+                parse_contents(c, n, d)
+                for c, n, d in zip(list_of_contents, list_of_names, list_of_dates)
+            ]
+            return children
+
 
 app = APP()