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import gradio as gr
from glob import glob
from PIL import Image
from explain import get_results, reproduce
def classify_and_explain(image, object_detection=False):
reproduce()
# This function will classify the image and return a list of image paths
list_of_images = get_results(img_for_testing=image, od=object_detection)
return list_of_images
def get_examples():
od_off_examples = [
"samples/DSC_0315.jpg",
"samples/20210401_123624.jpg",
"samples/IMG_1299.jpg",
"samples/20210420_112400.jpg",
"samples/IMG_1300.jpg",
"samples/20210420_112406.jpg",
]
return [
[Image.open(i), True] for i in glob("samples/*") if i not in od_off_examples
] + [[Image.open(i), False] for i in glob("samples/*") if i in od_off_examples]
demo = gr.Interface(
fn=classify_and_explain,
inputs=[
"image",
gr.Checkbox(
label="Extract Leaves", info="What to extract leafs before classification"
),
],
outputs="gallery",
examples=get_examples(),
)
demo.launch()
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