Create app.py

app.py

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+import gradio
+from fastai.vision.all import *
+
+MODELS_PATH = Path('./models')
+EXAMPLES_PATH = Path('./examples')
+
+# Required function expected by fastai learn object
+# it wasn't exported as a part of the pickle
+# as it was defined externally to the learner object
+# during the training time dataloaders setup
+def label_func(filepath):
+    return filepath.parent.name
+
+LEARN = load_learner(MODELS_PATH/'Garbage_Classification.pkl')
+LABELS = LEARN.dls.vocab
+
+def gradio_predict(img):
+    img = PILImage.create(img)
+    _pred, _pred_idx, probs = LEARN.predict(img)
+    labels_probs = {LABELS[i]: float(probs[i]) for i, _ in enumerate(LABELS)}
+    return labels_probs
+
+with open('gradio_article.md') as f:
+    article = f.read()
+
+interface_options = {
+    "title": "Food Image Classifier (Food-101|ResNet50|fast.ai)",
+    "description": "",
+    "article": article,
+    "examples" : [f'{EXAMPLES_PATH}/{f.name}' for f in EXAMPLES_PATH.iterdir()],
+    "layout": "horizontal",
+    "theme": "default",
+}
+
+demo = gradio.Interface(fn=gradio_predict,
+                      inputs=gradio.inputs.Image(shape=(512, 512)),
+                      outputs=gradio.outputs.Label(num_top_classes=5),
+                      **interface_options)
+
+launch_options = {
+    "enable_queue": True,
+    "share": False, 
+}
+
+demo.launch(**launch_options)