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| import tarfile | |
| import gradio as gr | |
| from huggingface_hub import hf_hub_download | |
| from tensorflow.keras.models import load_model | |
| from tensorflow.keras.applications import resnet50 | |
| def load_model(tar_file: str='model.tar.gz'): | |
| tar_file = tarfile.open(tar_file) | |
| tar_file.extractall('./') | |
| tar_file.close() | |
| model_path = './model' | |
| return load_model(model_path) | |
| hf_hub_download(repo_id='chansung/', filename='outputs/model.tar.gz') | |
| model = load_model() | |
| labels = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] | |
| def classify_image(inp): | |
| inp = inp.reshape((-1, 224, 224, 3)) | |
| inp = resnet50.preprocess_input(inp) | |
| prediction = model.predict(inp).flatten() | |
| confidences = {labels[i]: float(prediction[i]) for i in range(1000)} | |
| return confidences | |
| gr.Interface(fn=classify_image, | |
| inputs=gr.inputs.Image(shape=(224, 224)), | |
| outputs=gr.outputs.Label(num_top_classes=3), | |
| examples=["banana.jpg", "car.jpg"]).launch() | |