Updated Gradio

app.py

@@ -1,12 +1,16 @@
+import gradio as gr
+import numpy as np
+import skimage.transform
 import torch
+import torchvision.models as models
+import torchvision.transforms as transforms
 from matplotlib import pyplot as plt
-from PIL import Image
-import numpy as np
 from numpy import matlib as mb
-import torchvision.transforms as transforms
-import torchvision.models as models
-import skimage.transform
-import gradio as gr
+from PIL import Image
+import csv
+import sys
+
+csv.field_size_limit(sys.maxsize)
 
 
 def compute_spatial_similarity(conv1, conv2):
@@ -90,13 +94,13 @@ def visualize_similarities(image1, image2):
     fig, axes = plt.subplots(1, 2, figsize=(12, 5))
     axes[0].imshow(display_transform(image1))
     im1 = axes[0].imshow(
-        skimage.transform.resize(sim1, (224, 224)), alpha=0.6, cmap="jet"
+        skimage.transform.resize(sim1, (224, 224)), alpha=0.5, cmap="jet"
     )
     # axes[0].colorbar()
 
     axes[1].imshow(display_transform(image2))
     im2 = axes[1].imshow(
-        skimage.transform.resize(sim2, (224, 224)), alpha=0.6, cmap="jet"
+        skimage.transform.resize(sim2, (224, 224)), alpha=0.5, cmap="jet"
     )
     # axes[1].colorbar()
 
@@ -110,10 +114,19 @@ def visualize_similarities(image1, image2):
 iface = gr.Interface(
     fn=visualize_similarities,
     inputs=[
-        gr.inputs.Image(shape=(300, 300), type="pil"),
-        gr.inputs.Image(shape=(300, 300), type="pil"),
+        gr.Image(type="pil"),
+        gr.Image(type="pil"),
+    ],
+    allow_flagging="never",
+    outputs=[gr.Plot(type="matplotlib")],
+    cache_examples=True,
+    enable_queue=False,
+    examples=[
+        [
+            "./examples/Red_Winged_Blackbird_0012_6015.jpg",
+            "./examples/Red_Winged_Blackbird_0025_5342.jpg",
+        ]
     ],
-    outputs=[gr.outputs.Plot(type="matplotlib")],
 )
 
 iface.launch()