Removed optional plotting from model_training/data_setup.py. Matplotlib no longer required.

.gitignore

@@ -0,0 +1,5 @@
+__pycache__/
+.DS_Store
+.ipynb_checkpoints
+*.ipynb
+mnist_data/

model_training/data_setup.py

@@ -13,7 +13,6 @@ import io
 import base64
 from PIL import Image
 import numpy as np
-import matplotlib.pyplot as plt
 
 # Transformations applied to each image
 BASE_TRANSFORMS = v2.Compose([
@@ -80,7 +79,7 @@ def get_dataloaders(root: str,
 
     return train_dl, test_dl
 
-def mnist_preprocess(uri: str, plot: bool = False):
+def mnist_preprocess(uri: str):
     '''
     Preprocesses a data URI representing a handwritten digit image according to the pipeline used in the MNIST dataset.
     The pipeline includes:
@@ -93,8 +92,7 @@ def mnist_preprocess(uri: str, plot: bool = False):
 
     Args:
         uri (str): A string representing the full data URI.
-        plot (bool, optional): If True, the resized 20x20 image is plotted alongside the final 28x28 image (pre-normalization). 
-                               The red lines on these plots intersect at the COM position. Default is False.
+        
     Returns:
         Tensor: A tensor of shape (1, 28, 28) representing the preprocessed image, normalized using MNIST statistics.
     '''
@@ -134,22 +132,6 @@ def mnist_preprocess(uri: str, plot: bool = False):
 
     # Paste cropped image into 28x28 field such that the old COM (com_y, com_x), is at the center (14, 14)
     new_img[new_slice_y, new_slice_x] = img[old_slice_y, old_slice_x]
-    
-    if plot:
-        fig, axes = plt.subplots(nrows = 1, ncols = 2, figsize = (12, 6))
-
-        axes[0].imshow(img, cmap = 'grey')
-        axes[0].axhline(com_y, c = 'red')
-        axes[0].axvline(com_x, c = 'red')
-
-        axes[1].imshow(new_img, cmap = 'grey')
-        axes[1].axhline(new_com_y, c = 'red')
-        axes[1].axvline(new_com_x, c = 'red')
-        
-        axes[0].set_title(f'Original Resized {img.shape[0]}x{img.shape[1]} Image')
-        axes[1].set_title('New Centered 28x28 Image')
-        
-        plt.tight_layout()
 
     # Return transformed tensor of new image. This includes normalizing to MNIST stats
-    return BASE_TRANSFORMS(new_img)
+    return BASE_TRANSFORMS(new_img)

requirements.txt

@@ -1,5 +1,4 @@
 numpy==2.2.4
-matplotlib==3.10.1
 panel==1.4.5
 param==2.1.1
 plotly==6.0.1