app.py

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

# Load the model that has been trained previously
learn = load_learner('model.pkl')

# Define a function that maps the model's predicted probabilities to the possible categories
categories = ('cabbage white butterfly', 'common blue butterfly', 'monarch butterfly', 'red admiral butterfly')   # ALPHABETICAL
def classify_image(img):
    _,_,probs = learn.predict(img)
    return dict(zip(categories, map(float,probs)))

# Making the UI
# Makes a box where images can be dropped as inputs to the model
image = gr.inputs.Image(shape=(192,192))

# Makes an output box to display the output label predictions
label = gr.outputs.Label()

# Provide some example images 
examples = ['butterfly1.jpeg', 'butterfly2.jpeg', 'butterfly3.jpeg', 'butterfly4.jpeg', 'butterfly5.jpeg', 'butterfly6.jpeg',]

# Export the result to the UI
intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
intf.launch(inline=False)