add app file

app.py

@@ -0,0 +1,37 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+model_path = '/workspaces/animals-classifier-demo/animals-classifier-demo/model'
+model = tf.saved_model.load(model_path)
+
+labels = ['butterfly', 'cats', 'cow', 'dogs', 'elephant',
+          'horse', 'monkey', 'sheep', 'spider', 'squirrel']
+
+def predict_image(image):
+  image_resized = image.resize((224, 224))
+  image_array = np.array(image_resized).astype(np.float32) / 255.0
+  image_array = np.expand_dims(image_array, axis=0)
+
+  predictions = model.signatures['serving_default'](tf.convert_to_tensor(image_array, dtype=tf.float32))['output_0']
+
+  # Top 3 classes
+  top_3_indices = np.argsort(predictions.numpy(), axis=1)[0][-3:][::-1]
+  top_3_labels = [labels[i] for i in top_3_indices]
+  top_3_probabilities = [predictions.numpy()[0][i] * 100 for i in top_3_indices]
+
+  output_string = "\n".join([f"{label}: {probability:.2f}%" for label, probability in zip(top_3_labels, top_3_probabilities)])
+
+  return image_resized, output_string
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil"),
+    outputs=[gr.Image(type="pil"), "text"],
+    title="Animal Classifier",
+    description="Upload an image of an animal, and the model will predict it."
+)
+
+interface.launch(share=True)