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Runtime error
| import gradio as gr | |
| import tensorflow as tf | |
| import numpy as np | |
| from tensorflow.keras.preprocessing.image import img_to_array | |
| from keras.datasets import mnist | |
| (X_train, y_train), (X_test, y_test) = mnist.load_data() | |
| X_train = X_train/255.0 | |
| X_test = X_test/255.0 | |
| model = tf.keras.models.Sequential([ | |
| tf.keras.layers.Flatten(input_shape=(28, 28)), | |
| tf.keras.layers.Dense(units=128, activation='relu'), | |
| tf.keras.layers.Dropout(0.5), | |
| tf.keras.layers.Dense(units=10,activation="softmax") | |
| ]) | |
| model.compile(optimizer='adam', | |
| loss='sparse_categorical_crossentropy', | |
| metrics=['accuracy'] | |
| ) | |
| history = model.fit(X_train,y_train,epochs=10,validation_data=(X_test,y_test)) | |
| def predict(img): | |
| x = img_to_array(img) | |
| x = np.expand_dims(x,axis=0) | |
| target = model.predict(x) | |
| target = np.argmax(target) | |
| return target | |
| demo = gr.Interface(fn=predict, | |
| inputs="sketchpad", | |
| outputs="number", | |
| live=True, streaming=True) | |
| demo.launch() |