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from keras.models import load_model # TensorFlow is required for Keras to work from PIL import Image, ImageOps # Install pillow instead of PIL import numpy as np

import gradio as gr def image_classifier(image):

Disable scientific notation for clarity

np.set_printoptions(suppress=True)

Load the model

model = load_model("keras_model.h5", compile=False)

Load the labels

class_names = open("labels.txt", "r").readlines()

Create the array of the right shape to feed into the keras model

The 'length' or number of images you can put into the array is

determined by the first position in the shape tuple, in this case 1

data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)

Replace this with the path to your image

image = Image.fromarray(image.astype("uint8"),"RGB")

resizing the image to be at least 224x224 and then cropping from the center

size = (224, 224) image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)

turn the image into a numpy array

image_array = np.asarray(image) normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1 data[0] = normalized_image_array

Predicts the model

prediction = model.predict(data) index = np.argmax(prediction) class_name = class_names[index] confidence_score = prediction[0][index]

Print prediction and confidence score

result = class_name[2:] return result print("Confidence Score:", confidence_score)

iface = gr.Interface( fn=image_classifier, inputs=gr.Image(), # 画像を入力として受け取ります。 outputs="text" # 結果をテキストで表示します。 )

#インターフェースを起動します。 iface.launch()