Update app.py

app.py

@@ -1,15 +1,33 @@
+# Demo: (Image) -> (Label)
+
 import gradio as gr
-from transformers import pipeline
+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)
 
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+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)}
 
-def predict(image):
-  predictions = pipeline(image)
-  return {p["label"]: p["score"] for p in predictions}
+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"]
+    ])
 
-gr.Interface(
-    predict,
-    inputs=gr.inputs.Image(label="Upload hot dog candidate", type="filepath"),
-    outputs=gr.outputs.Label(num_top_classes=2),
-    title="Hot Dog? Or Not?",
-).launch()
+if __name__ == "__main__":
+    iface.launch(share=True)