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| # Workaround to install the lib without "setup.py" | |
| import sys | |
| from git import Repo | |
| Repo.clone_from("https://github.com/dimitreOliveira/hub.git", "./hub") | |
| sys.path.append("/hub") | |
| import requests | |
| # Download human-readable labels for ImageNet. | |
| response = requests.get("https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt") | |
| labels = [x for x in response.text.split("\n") if x != ""] | |
| import gradio as gr | |
| import tensorflow as tf | |
| from hub.tensorflow_hub.hf_utils import pull_from_hub | |
| model = pull_from_hub(repo_id="Dimitre/mobilenet_v3_small") | |
| def preprocess(image): | |
| print(image) | |
| print("***********") | |
| image = image.reshape((-1, 224, 224, 3)) | |
| print(image) | |
| print("***********") | |
| print(image / 255.) | |
| return image / 255. | |
| def postprocess(prediction): | |
| return {labels[i]: prediction[i] for i in range(len(labels))} | |
| def predict_fn(image): | |
| image = preprocess(image) | |
| prediction = model([image]) | |
| scores = postprocess(prediction) | |
| return scores | |
| iface = gr.Interface(fn=predict_fn, | |
| inputs=gr.Image(shape=(224, 224)), | |
| outputs=gr.Label(num_top_classes=5)) | |
| iface.launch() |