app.py

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

im1 = PILImage.create('Brittlebush-Encelia-Farinosa-desert-horizon-nursery.jpg')
im2 = PILImage.create('download.webp')

learn = load_learner('export.pkl')

pred_class,pred_idx,probabilities = learn.predict(im1)
pred_class, pred_idx, probabilities

pred_class,pred_idx,probabilities = learn.predict(im2)
pred_class, pred_idx, probabilities

categories = ('balsamroot', 'bladderpod', 'blazing star', 'bristlecone pine flowers', 'brittlebrush')
def classify_image(img):
    pred, idx, probs = learn.predict(img)
    return dict(zip(categories, map(float, probs)))


classify_image(im1), classify_image(im2)

image=gr.Image(height = 192, width = 192)
label = gr.Label()
examples = ['https://www.deserthorizonnursery.com/wp-content/uploads/2024/03/Brittlebush-Encelia-Farinosa-desert-horizon-nursery.jpg','https://cdn.mos.cms.futurecdn.net/VJE7gSuQ9KWbkqEsWgX5zS.jpg']
intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
intf.launch(inline=False)