Create app.py

app.py

@@ -0,0 +1,49 @@
+import os
+
+import gradio as gr
+import tensorflow as tf
+from keras_tuner import HyperParameters
+from huggingface_hub import hf_hub_download
+
+from src.models import MakeHyperModel
+from src.preprocessing import get_data_augmentation
+from src.config import IMAGE_SIZE
+
+data_augmentation = get_data_augmentation()
+cache_dir = os.path.join('hf_hub')
+
+for f in ['checkpoint', 'checkpoint.data-00000-of-00001', 'checkpoint.index']:
+    print(f)
+    old_name = hf_hub_download(repo_id="eddydecena/cat-vs-dog", filename=f"tuner_model/cat-vs-dog/trial_0484d8d758a5ef7b91ca97d334ba7870/checkpoints/epoch_0/{f}", cache_dir=cache_dir)
+    temp_value = old_name.split('/')
+    temp_value.pop(-1)
+    path = '/'.join(temp_value)
+    os.rename(old_name, os.path.join(path, f))
+
+latest = tf.train.latest_checkpoint('./tuner_model/cat-vs-dog/trial_0484d8d758a5ef7b91ca97d334ba7870/checkpoints/epoch_0')
+hypermodel = MakeHyperModel(input_shape=IMAGE_SIZE + (3,), num_classes=2, data_augmentation=data_augmentation)
+model = hypermodel.build(hp=HyperParameters())
+model.load_weights(latest).expect_partial()
+
+def cat_vs_dog(image):
+    img_array = tf.constant(image, dtype=tf.float32)
+    img_array = tf.expand_dims(img_array, 0)
+    predictions = model.predict(img_array)
+    score = predictions[0]
+    return {'cat': float((1 - score)), 'dog': float(score)}
+
+iface = gr.Interface(
+    cat_vs_dog, 
+    gr.inputs.Image(shape=IMAGE_SIZE), 
+    gr.outputs.Label(num_top_classes=2),
+    capture_session=True,
+    interpretation="default",
+    examples=[
+        ["examples/cat1.jpg"],
+        ["examples/cat2.jpg"],
+        ["examples/dog1.jpeg"],
+        ["examples/dog2.jpeg"]
+    ])
+
+if __name__ == "__main__":
+    iface.launch()