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import requests
import tensorflow as tf
inception_net = tf.keras.applications.MobileNetV2()
import requests
# 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()
confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
return confidences
import gradio as gr
gr.Interface(fn=classify_image,
inputs=gr.inputs.Image(shape=(224, 224)),
outputs=gr.outputs.Label(num_top_classes=3),
examples=["banana.jpg", "car.jpg"],
theme="default",
css=".footer{display:none !important}").launch()