用jupyter写一个简单的图片识别界面

首先需要安装所需的库：`tensorflow`, `keras`, `numpy`, `matplotlib` 和 `ipywidgets`。

可以使用以下命令来安装这些库：

!pip install tensorflow
!pip install keras
!pip install numpy
!pip install matplotlib
!pip install ipywidgets

接下来，我们可以创建一个Jupyter Notebook，并按以下步骤进行操作：

1. 导入必要的库

import tensorflow as tf
from keras.preprocessing import image
import numpy as np
import matplotlib.pyplot as plt
from ipywidgets import widgets
from IPython.display import display

2. 加载模型

model = tf.keras.models.load_model('model.h5')

此处的 `model.h5` 是我们训练好的模型文件。如果你没有模型文件，可以使用以下代码来训练一个简单的模型：

from keras.models import Sequential
from keras.layers import Dense, Conv2D, Flatten

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28,28,1)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# load data
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1) / 255.0
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1) / 255.0
y_train = tf.keras.utils.to_categorical(y_train, 10)
y_test = tf.keras.utils.to_categorical(y_test, 10)

# train model
model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=5, batch_size=64)

# save model
model.save('model.h5')

3. 创建GUI界面

# create GUI
image_upload = widgets.FileUpload()
output_image = widgets.Output()
output_label = widgets.Output()

button_predict = widgets.Button(description='Predict')

def on_button_predict_clicked(b):
    # get uploaded image
    image_string = image_upload.data[-1]
    image_bytes = image_string['content']
    img = image.img_to_array(image.load_img(io.BytesIO(image_bytes), target_size=(28, 28), color_mode='grayscale')) / 255.0
    
    # predict label
    with output_label:
        output_label.clear_output()
        predictions = model.predict(np.array([img]))
        label = np.argmax(predictions)
        plt.imshow(img.reshape(28,28), cmap='gray')
        plt.title('Predicted Label: {}'.format(label))
        plt.axis('off')
        plt.show()

button_predict.on_click(on_button_predict_clicked)

display(image_upload, button_predict, output_image, output_label)

这将创建一个界面，其中包含一个文件上传按钮和一个预测按钮。当用户上传一张图片并点击预测按钮时，该图片将被加载并传递给模型进行预测，预测结果将显示在界面上。