Rebasing for model.pkl

app.py

@@ -1,7 +1,26 @@
 import gradio as gr
+from fastai.vision.all import *
 
-def greet(name):
-    return "Hello " + name + "!!"
+categories = ("alpine", "desert", "humid continental", "humid subtropical", "ice cap", 
+"oceanic plant", "subarctic plant", "semi-arid plant", "mediterranean", "tropical monsoon", 
+"tropical rainforest plant", "tropical savanna plant", "tundra plant", "polar")
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+learn = load_learner("model.pkl")
+
+# Gradio needs a function
+def classify_image(img):
+	pred, idx, probs = learn.predict(img)
+	return dict(zip(categories, map(float, probs)))
+
+# Build the Gradio interface
+image = gr.inputs.Image(shape=(192,192))
+label = gr.outputs.label()
+examples = ["polar.jpg", "mediterranean.jpg", "humid_subtropical.jpg"]
+
+intf = gr.Interface(fn=classify_image, 
+										inputs=image, 
+										outputs=label, 
+										examples=examples)
+
+# This will give us a link to play with the model in app
+intf.launch(inline=False)