Initial commit. Let's see if this works.

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,39 @@
+import gradio
+from fastai.vision.all import *
+
+MODELS_PATH = Path('./models')
+EXAMPLES_PATH = Path('./examples')
+
+# Required function used by fastai learner (at training setup)
+def label_func(filepath):
+    return filepath.parent.name
+
+learn = load_learner(MODELS_PATH/'food-101-resnet50.pkl')
+
+labels = learn.dls.vocab
+
+def predict(img):
+    img = PILImage.create(img)
+    pred,pred_idx,probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+with open('gradio_article.md') as f:
+    article = f.read()
+
+interface_options = {
+    "title": "Food-101 Classifier",
+    "description": "A food image classifier trained on the Food-101 (https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/) dataset with fastai with a ResNet50 CNN model.",
+    "article": article,
+    "examples" : [f'{EXAMPLES_PATH}/{f.name}' for f in EXAMPLES_PATH.iterdir()],
+    "interpretation": "default",
+    "layout": "horizontal",
+    "allow_flagging": "never",
+    "enable_queue": True 
+}
+
+demo = gradio.Interface(fn=predict,
+                      inputs=gradio.inputs.Image(shape=(512, 512)),
+                      outputs=gradio.outputs.Label(num_top_classes=5),
+                      **interface_options)
+
+demo.launch()

gradio_article.md

@@ -0,0 +1,7 @@
+# H1
+
+Some text here
+
+## H2
+
+Some more text here

models/food-101-resnet50.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b05abca6f36341d2da0cce628d15b65f544ce6ee6a09d39b659a35919826ac81
+size 104817109

requirements.txt

@@ -0,0 +1 @@
+fastai==2.6.0