Update app.py

app.py

@@ -1,33 +1,26 @@
-# Demo: (Image) -> (Label)
+import requests
+import tensorflow as tf
 
 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()
+
+inception_net = tf.keras.applications.MobileNetV2()  # load the model
+
+# Download human-readable labels for ImageNet.
+response = requests.get("https://git.io/JJkYN")
+labels = response.text.split("\n")
+
+
+def classify_image(inp):
+    inp = inp.reshape((-1, 224, 224, 3))
+    inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
+    prediction = inception_net.predict(inp).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=[
-        ["images/cheetah1.jpg"],
-        ["images/lion.jpg"]
-    ])
-
-if __name__ == "__main__":
-    iface.launch(share=True)
+
+image = gr.Image(shape=(224, 224))
+label = gr.Label(num_top_classes=3)
+
+title = "Gradio Image Classifiction + Interpretation Example"
+gr.Interface(
+    fn=classify_image, inputs=image, outputs=label, interpretation="default", title=title
+).launch()