Upload app.py

app.py

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+import os
+import torch
+import torchvision.transforms as transforms
+import torch.nn.functional as F
+import gradio as gr
+from urllib.request import urlretrieve
+from model import Net
+
+# Loads latest model state from Github
+urlretrieve("https://github.com/equ1/mnist-interface/tree/main/saved_models")
+
+model_timestamps = [filename[10:-3]
+                    for filename in os.listdir("./saved_models")]
+latest_timestamp = max(model_timestamps)
+
+if torch.cuda.is_available():
+  dev = "cuda:0"
+else:
+  dev = "cpu"
+
+device = torch.device(dev)
+
+model = Net()
+model.load_state_dict(torch.load(f"./saved_models/mnist-cnn-{latest_timestamp}.pt", map_location=device))
+model.eval()
+
+# inference function
+def inference(img):
+    transform = transforms.Compose([transforms.ToTensor(), transforms.Resize((28, 28))])
+    img = transform(img).unsqueeze(0)  # transforms ndarray and adds batch dimension
+
+    with torch.no_grad():
+        output_probabilities = F.softmax(model(img), dim=1)[0]  # probability prediction for each label
+
+    return {labels[i]: float(output_probabilities[i]) for i in range(len(labels))}
+
+# Creates and launches gradio interface
+labels = range(10)  # 1-9 labels
+outputs = gr.outputs.Label(num_top_classes=5)
+gr.Interface(fn=inference, inputs='sketchpad', outputs=outputs, title="MNIST interface",
+             description="Draw a number from 0-9 in the box and click submit to see the model's predictions.").launch()