Upload app.py

app.py

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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()