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import tarfile | |
import gradio as gr | |
from huggingface_hub import hf_hub_download | |
from tensorflow import keras | |
from tensorflow.keras.applications import resnet50 | |
def load_model(tar_file: str='outputs/model.tar.gz'): | |
tar_file = tarfile.open(tar_file) | |
tar_file.extractall('./') | |
tar_file.close() | |
model_path = 'model' | |
return keras.models.load_model(model_path) | |
hf_hub_download(repo_id='chansung/testmodel2', 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(10)} | |
return confidences | |
iface = gr.Interface(fn=classify_image, | |
inputs=gr.inputs.Image(shape=(224, 224)), | |
outputs=gr.outputs.Label(num_top_classes=3)).launch() | |
iface.launch() |