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from fastai.vision.all import *
from fastai.vision.all import load_learner
import gradio as gr
import pathlib
temp = pathlib.PosixPath
pathlib.PosixPath = pathlib.WindowsPath
#!export
pollutant_labels = (
"antennas",
"billboard",
"broken roads",
"construction sites",
"electric pole",
"garbage can",
"graffiti",
"smog",
"street litter"
)
model = load_learner('models/vispol-1-recognizer-v0.pkl')
def recognize_image(image):
pred, idx, probs = model.predict(image)
return dict(zip(pollutant_labels, map(float, probs)))
#!export
image = gr.inputs.Image(shape=(256,256))
label = gr.outputs.Label()
examples = [
'test_images/unknown_00.jpg',
'test_images/unknown_01.jpg',
'test_images/unknown_02.jpg',
'test_images/unknown_03.jpg',
'test_images/unknown_04.jpg'
]
iface = gr.Interface(fn=recognize_image, inputs=image, outputs=label, examples=examples)
iface.launch(inline=False, share=True) |