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import os
import numpy as np
import gradio as gr
import tensorflow as tf
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', compile=False)
def classify_image(image):
if len(np.array(image).shape) == 3:
image = tf.image.rgb_to_grayscale(image)
# image = np.array(image['composite'])[:, :, 3] * 255
# image = image[..., np.newaxis]
image_tensor = tf.convert_to_tensor(image)
image_tensor = tf.image.resize(image_tensor, (28, 28)),
image_tensor = tf.cast(image_tensor, tf.float32)
image_tensor = image_tensor / 255.0
prediction = model.predict(image_tensor)
print(prediction)
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"),
# inputs=gr.Sketchpad(),
# outputs=gr.Label(num_top_classes=3, label="Predictions"),
outputs='text',
examples=example_list,
title=title,
description=description,
article=article)
interface.launch() |