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Create app.py
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app.py
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import gradio as gr
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import tensorflow as tf
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from matplotlib import pyplot as plt
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import numpy as np
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num_objects = tf.keras.datasets.mnist
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(training_images, training_labels), (test_images, test_labels) = num_objects.load_data()
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for i in range(9):
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#define subplot
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plt.subplot(330 + 1 + i)
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#plot of raw pixel data
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plt.imshow(training_images[i])
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training_images = training_images / 255.0
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test_images = test_images / 255.0
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from tensorflow.keras.layers import Flatten, Dense
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model = tf.keras.models.Sequential([Flatten(input_shape=(28,28)),
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Dense(256, activation='relu'),
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Dense(256, activation='relu'),
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Dense(128, activation='relu'),
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Dense(10, activation=tf.nn.softmax)])
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model.compile(optimizer = 'adam',
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loss = 'sparse_categorical_crossentropy',
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metrics=['accuracy'])
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model.fit(training_images, training_labels, epochs=10) #how many times u go through the dataset
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test=test_images[0].reshape(-1,28,28)
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pred=model.predict(test)
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print(pred)
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def predict_image(img):
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img_3d=img.reshape(-1,28,28)
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im_resize=img_3d/255.0
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prediction=model.predict(im_resize)
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pred=np.argmax(prediction)
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return pred
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iface = gr.Interface(predict_image, inputs="sketchpad", outputs="label")
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iface.launch(debug='True')
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