implement gradio app.py

app.py

@@ -1,7 +1,49 @@
 import gradio as gr
+import torch
+from model import *
 
-def greet(name):
-    return "Hello " + name + "!!"
+from PIL import Image
+import torchvision.transforms as transforms
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch(
+title = "Digit Classifier"
+description = (
+    "Multilayer-Perceptron built for the fast.ai 'Deep Learning' course "
+    "to classify handwritten digits from the MNIST dataset. "
+)
+inputs = gr.components.Image()
+outputs = gr.components.Label()
+examples = "examples"
+
+model = torch.load("model/digit_classifier.pt", map_location=torch.device("cpu"))
+labels = [str(i) for i in range(10)]
+
+transform = transforms.Compose(
+    [
+        transforms.Resize((28, 28)),
+        transforms.Grayscale(),
+        transforms.ToTensor(),
+        transforms.Lambda(lambda x: x[0]),
+        transforms.Lambda(lambda x: x.unsqueeze(0)),
+    ]
+)
+
+
+def predict_digit(img):
+    img = transform(Image.fromarray(img))
+    output = model(img)
+    probs = torch.nn.functional.softmax(output, dim=1)
+    return dict(zip(labels, map(float, probs.flatten()[:10])))
+
+
+with gr.Blocks() as demo:
+    with gr.Tab("Digit Prediction"):
+        gr.Interface(
+            fn=predict_digit,
+            inputs=inputs,
+            outputs=outputs,
+            examples=examples,
+            title=title,
+            description=description,
+        ).queue(default_concurrency_limit=5)
+
+demo.launch()