app.py

# You need a requirements.txt file for dependencies in Spaces ('pipreqs /path' or 'pipreqs' in CWD will
# automatically generate a requirements.txt file for you).

import gradio as gr
from fastai.vision.all import *

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")

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.Image(shape=(192,192))
label = gr.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)