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+FROM python:3.8-slim-buster
+
+WORKDIR /app
+
+COPY requirements.txt .
+
+RUN pip3 install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+CMD ["python3", "app.py"]

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.applications import MobileNetV2
+from tensorflow.keras.layers import Conv2DTranspose, Concatenate, Activation
+from tensorflow.keras.models import Model
+
+def load_model():
+    base_model = MobileNetV2(input_shape=(None, None, 3), include_top=False)
+    layer_names = [
+        'block_1_expand_relu',   # 64x64
+        'block_3_expand_relu',   # 32x32
+        'block_6_expand_relu',   # 16x16
+        'block_13_expand_relu',  # 8x8
+        'block_16_project',      # 4x4
+    ]
+    layers = [base_model.get_layer(name).output for name in layer_names]
+    down_stack = Model(inputs=base_model.input, outputs=layers)
+    down_stack.trainable = False
+
+    up_stack = [
+        Conv2DTranspose(512, 3, strides=2, padding='same'),
+        Concatenate(),
+        Activation('relu'),
+        Conv2DTranspose(256, 3, strides=2, padding='same'),
+        Concatenate(),
+        Activation('relu'),
+        Conv2DTranspose(128, 3, strides=2, padding='same'),
+        Concatenate(),
+        Activation('relu'),
+        Conv2DTranspose(64, 3, strides=2, padding='same'),
+        Concatenate(),
+        Activation('relu'),
+        Conv2DTranspose(3, 3, strides=2, padding='same'),
+        Activation('sigmoid'),
+    ]
+
+    inputs = tf.keras.layers.Input(shape=[None, None, 3])
+    x = inputs
+
+    skips = down_stack(x)
+    skips = reversed(skips[:-1])
+
+    for up, skip in zip(up_stack, skips):
+        x = up(x)
+        concat = Concatenate()
+        x = concat([x, skip])
+
+    model = Model(inputs=inputs, outputs=x)
+
+    return model
+
+model = load_model()
+
+def sketch_image(img):
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    img = img / 255.0
+    img = cv2.resize(img, (256, 256))
+    img = np.expand_dims(img, axis=0)
+    output = model.predict(img)
+    output = np.squeeze(output, axis=0)
+    output = cv2.resize(output, (img.shape[2], img.shape[1]))
+    output = np.clip(output * 255, 0, 255).astype('uint8')
+    return output
+
+title = "Picture to Drawing"
+description = "Turn your pictures into beautiful drawings!"
+inputs = gr.inputs.Image(label="Input Image")
+outputs = gr.outputs.Image(label="Output Image")
+examples = [['examples/1.jpg'], ['examples/2.jpg'], ['examples/3.jpg']]
+gr.Interface(sketch_image, inputs, outputs, title=title, description=description, examples=examples).launch()

requirements.txt

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+gradio
+opencv-python-headless
+numpy
+tensorflow==2.5.0

test.py

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+import app
+
+def test_sketch_image():
+    img = cv2.imread('examples/1.jpg')
+    output = app.sketch_image(img)
+    assert output.shape == img.shape
+
+test_sketch_image()