| 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") | |
| model.load("./Pikachu_and_Raichu.h5") | |
| def classify_image(inp): | |
| inp = inp.reshape((-1, 224, 224, 3)) | |
| inp = model(inp) | |
| prediction = model(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.Image(shape=(224, 224)), | |
| outputs=gr.Label(num_top_classes=2)).launch() |