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| from fastai.vision.all import * | |
| import gradio as gr | |
| import pathlib | |
| plt = platform.system() | |
| if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath | |
| file = open('model.pkl', 'rb') | |
| temp = pickle.load(file) | |
| file.close() | |
| infer = temp['model'] | |
| data_map = [f'{item["name"]} ({item["botanical_name"]})' for item in temp['map']] | |
| def classify(img): | |
| _, _, probs = infer.predict(img) | |
| return dict(map(lambda x: (data_map[int(infer.dls.vocab[x[0]])], float(x[1])), enumerate(probs))) | |
| image = gr.inputs.Image(shape=(224, 224)) | |
| label = gr.outputs.Label(num_top_classes=3) | |
| iface = gr.Interface(fn=classify, title='Indoor Plant Classifier', inputs=image, outputs=label) | |
| iface.launch() | |