add sample images

app.py

@@ -1,20 +1,13 @@
-# %%
-import os, json, itertools, bisect, gc
+import gradio as gr
+import albumentations as A
+from albumentations.pytorch import ToTensorV2
 from PIL import Image
 import torch
 import torch.nn as nn
 import torchvision.models as models
 import torchvision.transforms as transforms
-import os
-import gradio as gr
-import albumentations as A
-from albumentations.pytorch import ToTensorV2
 import urllib.request
 
-MODEL_URL = "https://huggingface.co/caisarl76/HI_motorcycle_trunk_cls_model/resolve/main/best_model.pth"
-MODEL_PATH = "/tmp/best_model.pth" 
-urllib.request.urlretrieve(MODEL_URL, MODEL_PATH)
-
 test_transforms = A.Compose(
     [
         A.SmallestMaxSize(max_size=350),
@@ -24,6 +17,12 @@ test_transforms = A.Compose(
     ]
 )
 
+img_samples = os.listdir('./sample/')
+MODEL_URL = "https://huggingface.co/caisarl76/HI_motorcycle_trunk_cls_model/resolve/main/best_model.pth"
+MODEL_PATH = "/tmp/best_model.pth" 
+urllib.request.urlretrieve(MODEL_URL, MODEL_PATH)
+
+
 def predict(img):
     img = Image.fromarray(img.astype('uint8'), 'RGB')
     img = transforms.ToTensor()(img).unsqueeze(0)
@@ -57,7 +56,8 @@ inputs = gr.inputs.Image()
 outputs = gr.outputs.Label(num_top_classes=1)
 gr.Interface(fn=predict,
              inputs=inputs,
-             outputs=outputs).launch()
+             outputs=outputs,
+             examples=img_samples).launch()