Added files

app.py

@@ -8,6 +8,7 @@ from torchvision import transforms
 import modules.config as config
 import numpy as np
 import torch
+from PIL import Image
 
 
 TITLE = "CIFAR10 Image classification using a Custom ResNet Model"
@@ -118,7 +119,7 @@ def app_interface(
     num_gradcam_misclassified,
 ):
     """Function which provides the Gradio interface"""
-
+    input_img = resize_image_pil(input_img, 32, 32)
     # Get the prediction for the input image along with confidence and display_image
     confidences, display_image = generate_prediction(input_image, num_classes, show_gradcam, transparency, layer_name)
 
@@ -150,6 +151,26 @@ def app_interface(
 
     return confidences, display_image, misclassified_fig, gradcam_fig
 
+def resize_image_pil(image, new_width, new_height):
+
+    # Convert to PIL image
+    img = Image.fromarray(np.array(image))
+    
+    # Get original size
+    width, height = img.size
+
+    # Calculate scale
+    width_scale = new_width / width
+    height_scale = new_height / height 
+    scale = min(width_scale, height_scale)
+
+    # Resize
+    resized = img.resize((int(width*scale), int(height*scale)), Image.NEAREST)
+    
+    # Crop to exact size
+    resized = resized.crop((0, 0, new_width, new_height))
+
+    return resized
 
 
 
@@ -157,7 +178,7 @@ inference_app = gr.Interface(
     app_interface,
     inputs=[
         # This accepts the image after resizing it to 32x32 which is what our model expects
-        gr.Image(shape=(32, 32)),
+        gr.Image(width=256, height=256, label="Input Image"),
         gr.Number(value=3, maximum=10, minimum=1, step=1.0, precision=0, label="#Classes to show"),
         gr.Checkbox(True, label="Show GradCAM Image"),
         gr.Dropdown(model_layer_names, value="layer3_x", label="Visulalization Layer from Model"),