| from tensorflow import keras | |
| import gradio | |
| import requests | |
| inception_net = keras.applications.MobileNetV2() | |
| response = requests.get("https://git.io/JJkYN") | |
| labels = response.text.split("\n") | |
| def classify_image(inp): | |
| inp = inp.reshape((-1, 224, 224, 3)) | |
| inp = 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 | |
| gradio.Interface(fn=classify_image, | |
| inputs=gradio.Image(shape=(224, 224)), | |
| outputs=gradio.Label(num_top_classes=3),interpretation='default').launch(debug='False') |