done

TestSampleDataset.py

@@ -1,27 +0,0 @@
-import numpy as np
-import torch
-from torch.utils.data import Dataset
-
-
-class TestDataset(Dataset):
-
-    def __init__(self, npz_path, transform=None):
-        data = np.load(npz_path)
-
-        self.images = data["images"]
-        self.labels = data["labels"]
-        self.transform = transform
-
-    def __len__(self):
-        return len(self.images)
-
-    def __getitem__(self, idx):
-        image = self.images[idx]
-        label = self.labels[idx]
-
-        if self.transform:
-            image = self.transform(image)
-
-        label = torch.tensor(label, dtype=torch.float32).squeeze()
-
-        return image, label

app.py

@@ -27,7 +27,6 @@ model = Predictor(n_labels=len(LABELS))
 
 model_file = hf_hub_download(
     repo_id="Hali5/Mae-Model-MedMNIST-Predictor",
-        # Create a read-only text box to display the column for Truth Labels
     filename="checkpoints/model_linear_v2_epoch_100.pt"
 )
 
@@ -81,7 +80,7 @@ with gradio.Blocks() as demo:
 
     with gradio.Tab("Predict"):
         gradio.Markdown("Upload a tissue patch image for classification.")
-        input_img = gradio.Image()
+        input_img = gradio.Image(height=512, width=512)
         output_lbl = gradio.Label(num_top_classes=3)
         btn = gradio.Button("Predict")
         btn.click(fn=predict, inputs=input_img, outputs=output_lbl)
@@ -89,11 +88,10 @@ with gradio.Blocks() as demo:
     with gradio.Tab("Examples"):
         gradio.Markdown("Click an example below to test the model against the PathMNIST test dataset.")
         
-        with gradio.Column():
-                input_img_ex = gradio.Image(label="Selected Test Image")
+        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)
-            
-        with gradio.Column():
             output_lbl_ex = gradio.Label(num_top_classes=3, label="Model Prediction")
         
         gradio.Examples(