Following example from fast.ai lesson 2.

app.py

@@ -1,7 +1,38 @@
+from fast.ai.vision.all import *
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+# def greet(name):
+#    return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+# iface.launch()
+
+
+
+
+# from:
+#  https://course.fast.ai/Lessons/lesson2.html
+#  https://github.com/fastai/fastbook/blob/master/02_production.ipynb
+
+
+#learn_inf = load_learner(path/'export.pkl')
+#learn_inf.predict('images/grizzly.jpg')
+#learn_inf.dls.vocab
+
+
+# model created from: https://www.kaggle.com/code/zachwormgoor/stock-photo-recognizer
+learn = load_learner('model.pkl')
+
+categories = ('stock', 'amateur')
+
+def classify(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float,probs)))
+    
+    
+image = gr.inputs.Image(shape=(192, 192))
+label = gr.outputs.Label()
+examples = ['stock.jpg', 'amateur.jpg', 'unsure.jpg']
+
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:880a9f0645a078cf543377f21f3385cf23a6271f680955084495c9956e3f09ae
+size 46964331