Create app.py

app.py

@@ -0,0 +1,26 @@
+import requests
+import tensorflow as tf
+
+import gradio as gr
+
+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)}
+
+
+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()