app.py

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

# Load the trained model
learn = load_learner('export.pkl')

# Categories for the prediction
categories = ('black', 'grizzly', 'teddy')

# Image classification function
def classify_image(img):
    img = PILImage.create(img)  # Convert image to fastai's PILImage format
    img = img.resize((192, 192))
    pred, idx, probs = learn.predict(img)
    return dict(zip(categories, map(float,probs)))

# Define the Gradio interface
image = gr.Image()  # Define the image input with shape
label = gr.Label()  # Output label for the classification result
examples = ['grizzly.jpg', 'black.jpg', 'teddy.jpg', 'dunno.jpg']

# Create the interface
inft = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)

# Launch the interface
inft.launch(inline=False)