Update app.py

app.py

@@ -1,26 +1,26 @@
-import gradio as gr
-import numpy as np
-import tensorflow as tf
-import json
-from os.path import dirname, realpath, join
-import matplotlib.pyplot as plt
-
-current_dir = dirname(realpath(__file__))
-with open(join(current_dir), 'image_labels.json') as labels_file:
-    labels=json.load(labels_file)
-
-mobile_net = tf.keras.applications.MobileNetV2()
-def image_classifier(img):
-    arr = np.expand_dims(img, axis=0)
-    arr = tf.keras.applications.mobilenet.preprocess_input(arr)
-    prediction = mobile_net.prediction(arr).flatten()
-    return {labels[i]:float(prediction[i]) for i in range(1000)}
-iface = gr.Interface(
-    image_classifier,
-    inputs=gr.Image(height=224, width=224),
-    outputs=gr.Label(num_top_classes=3),
-    capture_session=True,
-    interpretation='default',
-)
-if __name__ == '__main__':
+import gradio as gr
+import numpy as np
+import tensorflow as tf
+import json
+from os.path import dirname, realpath, join
+import matplotlib.pyplot as plt
+
+current_dir = dirname(realpath(__file__))
+with open(join(current_dir), 'image_labels.json') as labels_file:
+    labels=json.load(labels_file)
+
+mobile_net = tf.keras.applications.MobileNetV2()
+def image_classifier(img):
+    arr = np.expand_dims(img, axis=0)
+    arr = tf.keras.applications.mobilenet.preprocess_input(arr)
+    prediction = mobile_net.prediction(arr).flatten()
+    return {labels[i]:float(prediction[i]) for i in range(1000)}
+iface = gr.Interface(
+    image_classifier,
+    gr.inputs.Image(height=224, width=224),
+    gr.outputs.Label(num_top_classes=3),
+    capture_session=True,
+    interpretation='default',
+)
+if __name__ == '__main__':
     iface.launch(share=True)