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import os
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()
label = gr.Label(num_top_classes=3)
demo = gr.Interface(
fn=classify_image,
inputs=image,
outputs=label,
examples=[
os.path.join(os.path.dirname(__file__), "images/cheetah1.jpg"),
os.path.join(os.path.dirname(__file__), "images/lion.jpg")
]
)
if __name__ == "__main__":
demo.launch()
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