app.py

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()