adds examples

app.py

@@ -1,10 +1,15 @@
+import glob
 import gradio as gr
 import numpy as np
 from os import environ
+from pathlib import Path
+from PIL import Image
 from torchvision import transforms as T
 from transformers import MaskFormerForInstanceSegmentation, MaskFormerImageProcessor
 
 
+example_images = sorted(glob.glob('examples/map*.jpg'))
+
 ade_mean=[0.485, 0.456, 0.406]
 ade_std=[0.229, 0.224, 0.225]
 
@@ -57,9 +62,10 @@ def visualize_instance_seg_mask(img_in, mask, id2label):
     return image_res, dataframe
 
 
-def query_image(img):
+def query_image(image_path):
+    img = np.array(Image.open(Path(image_path)))
     img_size = (img.shape[0], img.shape[1])
-    inputs = preprocessor(images=test_transform(np.array(img)), return_tensors="pt")
+    inputs = preprocessor(images=test_transform(img), return_tensors="pt")
 
     outputs = model(**inputs)
 
@@ -70,14 +76,16 @@ def query_image(img):
 
 demo = gr.Interface(
     query_image,
-    inputs=[gr.Image(label="Input Image")],
+    inputs=[gr.Image(type="filepath", label="Input Image")],
     outputs=[
         gr.Image(label="Vegetation"),
         gr.DataFrame(label="Info", headers=["Object Label", "Pixel Percent", "Square Length"])
     ],
     title="Maskformer Satellite+Trees",
     allow_flagging="never",
-    analytics_enabled=None
+    analytics_enabled=None,
+    examples=example_images,
+    cache_examples=True
 )
 
 demo.launch(show_api=False)

requirements.txt

@@ -1,3 +1,4 @@
+Pillow
 scipy
 torch
 torchvision