Initial version.

app.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
+
+# %% auto 0
+__all__ = ['MODEL_PATH', 'model', 'image', 'label', 'processed_image', 'intf', 'predict']
+
+# %% app.ipynb 2
+import torch
+import numpy as np
+import gradio as gr
+from PIL import Image
+from pathlib import Path
+import sys
+np.set_printoptions(threshold=sys.maxsize)
+
+# %% app.ipynb 4
+from lenet import LeNet5
+# Allowlist the custom class
+MODEL_PATH = Path("models/lenet5.pt")
+model = torch.load(MODEL_PATH, weights_only=False)
+model = model.to('cpu')  # Move model to CPU
+model.eval()
+
+def predict(img):
+    # Create a new image with a white background
+    background = Image.new("L", (28, 28), 255)
+
+    # Resize the input image
+    img_pil = img["composite"].resize((28, 28))
+
+    # Paste the resized image onto the white background
+    background.paste(img_pil, (0, 0), img_pil)
+    
+    # Convert to numpy
+    img_array = np.array(background)
+    
+    # Invert colors (MNIST has white digits on black)
+    img_array = 255 - img_array
+
+    # Create a displayable version of the inverted image (what the model actually sees)
+    inverted_debug = img_array.astype(np.uint8)
+
+    img_tensor = torch.tensor(img_array, dtype=torch.float32)    
+    img_tensor = img_tensor.unsqueeze(0).unsqueeze(0)  # Add channel and batch dimensions
+
+    # Debug: Print the shape and values of the input tensor
+    print(f"Input tensor shape: {img_tensor.shape}")
+    print(f"Input tensor values: {img_tensor}")
+
+    with torch.no_grad():
+        output = model(img_tensor)
+        probabilities = torch.nn.functional.softmax(output, dim=1)[0]
+
+    print(f"Output shape: {output.shape}")
+    print(f"Probabilities shape: {probabilities.shape}")
+    print(f"Probabilities: {probabilities}")
+
+    # Create dictionary of label: probability for Gradio Label output
+    return {str(i): float(prob) for i, prob in enumerate(probabilities)}, inverted_debug
+
+image = gr.Sketchpad(type="pil", sources=(), canvas_size=(280,280), brush=gr.Brush(colors=["#000000"], color_mode="fixed", default_size=20), layers=False, transforms=[])
+label = gr.Label()
+processed_image = gr.Image(label="What the Model Sees (28x28)")
+intf = gr.Interface(title="Title", fn=predict, inputs=image, outputs=[label, processed_image], clear_btn=None)
+intf.launch(inline=False, debug=True)

lenet.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: simple_network.ipynb.
+
+# %% auto 0
+__all__ = ['LeNet5']
+
+# %% simple_network.ipynb 1
+#%matplotlib inline
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+
+import torchvision
+import torchvision.transforms as transforms
+
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+
+# %% simple_network.ipynb 4
+class LeNet5(nn.Module):
+    def __init__(self, num_classes):
+        super().__init__()
+        self.l1 = nn.Sequential(
+            nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=2),   # 28*28-->32*32-->28*28
+            nn.BatchNorm2d(6),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size = 2, stride = 2))
+            
+        self.l2 = nn.Sequential(
+            nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1, padding=0),  # 10*10
+            nn.BatchNorm2d(16),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size = 2, stride = 2))
+        
+        self.classifier = nn.Sequential(
+            nn.Flatten(),
+            nn.Linear(in_features=16*5*5, out_features=120),
+            nn.ReLU(),
+            nn.Linear(in_features=120, out_features=84),
+            nn.ReLU(),
+            nn.Linear(in_features=84, out_features=num_classes),
+        )
+        
+    def forward(self, x):
+        out = self.l1(x)
+        out = self.l2(out)
+        out = self.classifier(out)
+        return out