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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() | |