Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import json
+from os.path import dirname, realpath, join
+
+# Load human-readable labels for ImageNet.
+current_dir = dirname(realpath(file))
+with open(join(current_dir, "imagenet_labels.json")) as labels_file:
+    labels = json.load(labels_file)
+
+mobile_net = tf.keras.applications.MobileNetV2()
+def image_classifier(im):
+    arr = np.expand_dims(im, axis=0)
+    arr = tf.keras.applications.mobilenet.preprocess_input(arr)
+    prediction = mobile_net.predict(arr).flatten()
+    return {labels[i]: float(prediction[i]) for i in range(1000)}
+
+iface = gr.Interface(
+    image_classifier, 
+    gr.inputs.Image(shape=(224, 224)), 
+    gr.outputs.Label(num_top_classes=3),
+    capture_session=True,
+    interpretation="default",
+    examples=[
+        ["cheetah1.jpg"],
+        ["lion.jpg"],
+        ["ma.jpg"]
+    ])
+
+
+if name == "main":
+    iface.launch(share=True)