Add model and Gradio app

app.py

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+import gradio as gr
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import img_to_array, load_img
+import numpy as np
+
+# Load your model (using a relative path)
+model = load_model('cats_and_dogs_classifier.h5')
+
+def predict(image):
+    img = image.resize((224, 224))
+    img = img_to_array(img)
+    img = np.expand_dims(img, axis=0)
+    prediction = model.predict(img)
+    class_label = 'cat' if prediction[0][0] < 0.5 else 'dog'
+    return class_label
+
+# Create a Gradio interface
+interface = gr.Interface(fn=predict, inputs=gr.inputs.Image(type="pil"), outputs="text")
+interface.launch()

cats_and_dogs_classifier.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2c80f0f5aae24df553ba40ce004ec8ff803d4ff42a5075b26a795f3c34340bc6
+size 41498088