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| import os | |
| import cv2 | |
| import numpy as np | |
| import gradio as gr | |
| from PIL import Image | |
| import tensorflow as tf | |
| # model = tf.keras.models.Sequential([ | |
| # tf.keras.layers.Conv2D(filters=16, kernel_size=3, strides=1, padding='same', activation='relu', input_shape=(28, 28, 1)), | |
| # tf.keras.layers.Conv2D(filters=16, kernel_size=3, strides=1, padding='same', activation='relu'), | |
| # tf.keras.layers.BatchNormalization(), | |
| # tf.keras.layers.Conv2D(filters=32, kernel_size=3, strides=1, padding='same', activation='relu'), | |
| # tf.keras.layers.Conv2D(filters=32, kernel_size=3, strides=1, padding='same', activation='relu'), | |
| # tf.keras.layers.BatchNormalization(), | |
| # tf.keras.layers.Conv2D(filters=64, kernel_size=3, strides=2, padding='same', activation='relu'), | |
| # tf.keras.layers.Conv2D(filters=64, kernel_size=3, strides=2, padding='same', activation='relu'), | |
| # tf.keras.layers.BatchNormalization(), | |
| # tf.keras.layers.GlobalAveragePooling2D(), | |
| # tf.keras.layers.Dense(10, activation='softmax') | |
| # ]) | |
| # model.compile(optimizer=tf.keras.optimizers.Adam(), | |
| # loss=tf.keras.losses.CategoricalCrossentropy(), | |
| # metrics=[tf.keras.metrics.MeanSquaredError(), tf.keras.metrics.AUC(), tf.keras.metrics.CategoricalAccuracy()]) | |
| # model.load_model("my_model.keras") | |
| # def image_mod(image): | |
| # # img = Image.fromarray(image['composite']) | |
| # model = tf.keras.models.load_model('weights_1.h5') | |
| # test_img = np.array(image['composite']).reshape(1, 28, 28, 1) | |
| # # test_img = cv2.resize(np.array(image['composite']), (28, 28, 1)) | |
| # prediction = model.predict(test_img) | |
| # pred = np.argmax(prediction, axis=1)[0] | |
| # return pred | |
| # title = "Draw to Search" | |
| # description = "Using the power of AI to detect the number you draw!" | |
| # demo = gr.Interface( | |
| # fn=image_mod, | |
| # inputs='sketchpad', | |
| # outputs='text', | |
| # title=title, | |
| # description=description, | |
| # live=True) | |
| # demo.launch(share=False) | |
| # demo.launch(debug=True) | |
| model = tf.keras.models.Sequential([ | |
| tf.keras.layers.Input(shape=(28, 28, 1)), | |
| tf.keras.layers.Conv2D(filters=16, kernel_size=3, strides=1, padding='same', activation='relu', input_shape=(28, 28, 1)), | |
| tf.keras.layers.Conv2D(filters=16, kernel_size=3, strides=1, padding='same', activation='relu'), | |
| tf.keras.layers.BatchNormalization(), | |
| tf.keras.layers.Conv2D(filters=32, kernel_size=3, strides=1, padding='same', activation='relu'), | |
| tf.keras.layers.Conv2D(filters=32, kernel_size=3, strides=1, padding='same', activation='relu'), | |
| tf.keras.layers.BatchNormalization(), | |
| tf.keras.layers.Conv2D(filters=64, kernel_size=3, strides=2, padding='same', activation='relu'), | |
| tf.keras.layers.Conv2D(filters=64, kernel_size=3, strides=2, padding='same', activation='relu'), | |
| tf.keras.layers.BatchNormalization(), | |
| tf.keras.layers.GlobalAveragePooling2D(), | |
| tf.keras.layers.Dense(10, activation='softmax') | |
| ]) | |
| model.compile(optimizer=tf.keras.optimizers.Adam(), | |
| loss=tf.keras.losses.CategoricalCrossentropy(), | |
| metrics=[tf.keras.metrics.MeanSquaredError(), tf.keras.metrics.AUC(), tf.keras.metrics.CategoricalAccuracy()]) | |
| model = tf.keras.models.load_model('my_model.h5') | |
| def classify_image(image): | |
| if len(np.array(image).shape) == 3: | |
| image = tf.image.rgb_to_grayscale(image) | |
| image_tensor = tf.convert_to_tensor(image) | |
| image_tensor = tf.cast(image_tensor, tf.float32) | |
| image_tensor = tf.expand_dims(image_tensor, 0) | |
| image_tensor = image_tensor / 255.0 | |
| prediction = model.predict(image_tensor) | |
| prediction_label = str(prediction.argmax()) | |
| return prediction_label | |
| title = "Draw to Search" | |
| description = "Using the power of AI to detect the number you draw!" | |
| article = "for source code you can visit [my github](https://github.com/mralamdari)" | |
| example_list = [["examples/" + example] for example in os.listdir("examples")] | |
| interface = gr.Interface(fn=classify_image, | |
| inputs=gr.Image(type="pil"), | |
| outputs=gr.Label(num_top_classes=3, label="Predictions"), | |
| examples=example_list, | |
| title=title, | |
| description=description, | |
| article=article) | |
| interface.launch() |