diff --git a/practical-deep-learning-for-coders/lesson-2/predict_app.py b/practical-deep-learning-for-coders/lesson-2/predict_app.py
new file mode 100644
index 0000000..371693b
--- /dev/null
+++ b/practical-deep-learning-for-coders/lesson-2/predict_app.py
@@ -0,0 +1,28 @@
+import gradio as gr
+import os
+
+from fastai.learner import load_learner
+from pathlib import Path
+
+path = Path(os.path.dirname(os.path.realpath(__file__)))
+
+# Make sure to export the model first via the notebook.
+# The exported model is not included in the repository.
+model = load_learner(path/'export.pkl')
+labels = model.dls.vocab
+
+def predict(img):
+    _, _, probabilities = model.predict(img)
+    return {labels[i]: float(probabilities[i]) for i in range(len(labels))}
+
+title = "Grizzly, Black, or Teddy Bear?"
+description = "This is a simple image classifier that can predict whether a given image contains a grizzly bear, black bear, or teddy bear."
+
+gr.Interface(
+    fn = predict,
+    inputs = gr.Image(width = 512, height = 512),
+    outputs = gr.Label(num_top_classes = 3),
+    title = title,
+    description = description,
+    allow_flagging = 'never'
+).launch()
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