added validation set and improve the prediction function

app.py

@@ -44,30 +44,47 @@ dataset = numpy.load("test_samples.npz")
 images = dataset["images"]
 labels = dataset["labels"]
 
-example_rows = []
+val_dataset = numpy.load("val_samples.npz")
+val_images = val_dataset["images"]
+val_labels = val_dataset["labels"]
+
+example_rows_test = []
+example_rows_val = []
+
 number_of_examples = len(labels)
 
 os.makedirs("ui_examples", exist_ok=True)
 
 for i in range(number_of_examples):
     img_array = images[i]
+    val_img_arr = val_images[i]
     label_index = int(labels[i].item() if hasattr(labels[i], 'item') else labels[i])
+    val_label_index = int(val_labels[i].item() if hasattr(val_labels[i], 'item') else val_labels[i])
     
     truth_label_text = LABELS[label_index] if label_index < len(LABELS) else f"Class {label_index}"
+    val_truth_label_text = LABELS[val_label_index] if val_label_index < len(LABELS) else f"Class {val_label_index}"
     
     file_path = f"ui_examples/sample_{i}.jpg"
+    val_file_path = f"ui_examples/val_sample_{i}.jpg"
+
     Image.fromarray(img_array.astype("uint8"), "RGB").save(file_path)
+    Image.fromarray(val_img_arr.astype("uint8"), "RGB").save(val_file_path)
 
-    example_rows.append([file_path, truth_label_text])
+    example_rows_test.append([file_path, truth_label_text])
+    example_rows_val.append([val_file_path, val_truth_label_text])
 
 @spaces.GPU
 def predict(image,truth_labels=None):
     if image is None:
         return None
     
-    pil_img = Image.fromarray(image.astype('uint8'), 'RGB')
-    
-    img_tensor = tf(pil_img).unsqueeze(0).to(device)
+    if truth_labels:
+        # already preprocessed test/val_samples
+        img_tensor = torch.from_numpy(image).unsqueeze(0).to(device)
+    else:
+        # uplouded image
+        pil_img = Image.fromarray(image.astype('uint8'), 'RGB')
+        img_tensor = tf(pil_img).unsqueeze(0).to(device)
 
     with torch.no_grad():
         outputs = model(img_tensor)
@@ -85,11 +102,11 @@ custom_css = """
 """
 
 
-with gradio.Blocks() as demo:
+with gradio.Blocks(css=custom_css) as demo:
     gradio.Markdown("# PathMNIST Image Classification")
 
     with gradio.Tab("Predict"):
-        gradio.Markdown("Upload a tissue patch image for classification.")
+        gradio.Markdown("## Upload a tissue image for classification")
         with gradio.Row():
             input_img = gradio.Image(height=512, width=512)
             with gradio.Column():
@@ -97,8 +114,25 @@ with gradio.Blocks() as demo:
                 btn = gradio.Button("Predict")
                 btn.click(fn=predict, inputs=input_img, outputs=output_lbl)
 
-    with gradio.Tab("Examples"):
-        gradio.Markdown("Click an example below to test the model against the PathMNIST test dataset.")
+    with gradio.Tab("Examples (Validation)"):
+        gradio.Markdown("## Select an example below to test the model against the PathMNIST validation dataset")
+        
+        with gradio.Row():
+            with gradio.Column():
+                input_img_ex = gradio.Image(label="Selected Test Image", height=512, width=512)
+                truth_box = gradio.Textbox(label="Ground Truth Label", interactive=False)
+            output_lbl_ex = gradio.Label(num_top_classes=3, label="Model Prediction")
+        
+        gradio.Examples(
+            examples=example_rows_val, 
+            inputs=[input_img_ex, truth_box],
+            outputs=output_lbl_ex,
+            fn=predict,
+            cache_examples=True,
+        )
+
+    with gradio.Tab("Examples (Test)"):
+        gradio.Markdown("## Select an example below to test the model against the PathMNIST test dataset")
         
         with gradio.Row():
             with gradio.Column():
@@ -107,7 +141,7 @@ with gradio.Blocks() as demo:
             output_lbl_ex = gradio.Label(num_top_classes=3, label="Model Prediction")
         
         gradio.Examples(
-            examples=example_rows, 
+            examples=example_rows_test, 
             inputs=[input_img_ex, truth_box],
             outputs=output_lbl_ex,
             fn=predict,

val_samples.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3085e9bbff8d470ed1375d618232b580ebb7fe8dbd0fd02c8a584e115936a5d6
+size 2618925