Create app.py

app.py

@@ -0,0 +1,21 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import requests
+
+inception_net = tf.keras.applications.InceptionV3() # 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 = np.expand_dims(inp, axis=0)  # add a batch dimension
+  inp = tf.keras.applications.inception_v3.preprocess_input(inp)
+  prediction = inception_net.predict(inp).flatten()
+  return {labels[i]: float(prediction[i]) for i in range(1000)}
+
+image = gr.inputs.Image(shape=(299, 299, 3))
+label = gr.outputs.Label(num_top_classes=3)
+
+gr.Interface(fn=classify_image, inputs=image, outputs=label, capture_session=True).launch()