Create app.py

app.py

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+import gradio as gr
+import imgaug.augmenters as iaa
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+def augment_image(image, flip, rotate, brightness, noise_scale, elastic_alpha, elastic_sigma):
+    image = np.array(image)
+    
+    # apply augs based on user inputs
+    if flip:
+        flip_aug = iaa.Fliplr(1.0)  # flips horizontally 100% of the time
+        image = flip_aug.augment_image(image)
+
+    rotate_aug = iaa.Affine(rotate=rotate)  # rotate by specified num of degrees (if you pass in a touple it will select a random value between options)
+    image = rotate_aug.augment_image(image)
+
+    brightness_aug = iaa.Multiply(brightness)  # adjust brightness
+    image = brightness_aug.augment_image(image)
+
+    noise_aug = iaa.AdditiveGaussianNoise(scale=(noise_scale))  # eaussian noise
+    image = noise_aug.augment_image(image)
+
+    elastic_aug = iaa.ElasticTransformation(alpha=elastic_alpha, sigma=elastic_sigma)  # elastic transformation
+    image = elastic_aug.augment_image(image)
+    
+    return image
+
+def gradio_interface(image, flip, rotate, brightness, noise_scale, elastic_alpha, elastic_sigma):
+    augmented_image = augment_image(image, flip, rotate, brightness, noise_scale, elastic_alpha, elastic_sigma)
+    return augmented_image
+
+inputs = [
+    gr.Image(type="pil"),  # Image input
+    gr.Checkbox(label="Flip Image Horizontally"),  # Flip input
+    gr.Slider(minimum=-180, maximum=180, step=1, value=0, label="Rotate Image (degrees)"),  # Rotation input
+    gr.Slider(minimum=0.1, maximum=2.0, step=0.1, value=1.0, label="Adjust Brightness"),  # Brightness input
+    gr.Slider(minimum=0, maximum=100, step=1, value=10, label="Gaussian Noise Scale"),  # Noise input
+    gr.Slider(minimum=0, maximum=200, step=10, value=100, label="Elastic Transformation Alpha"),  # Elastic Alpha input
+    gr.Slider(minimum=0.1, maximum=10.0, step=0.1, value=3.0, label="Elastic Transformation Sigma")  # Elastic Sigma input
+]
+
+iface = gr.Interface(
+    fn=gradio_interface, 
+    inputs=inputs, 
+    outputs=gr.Image(type="numpy"),
+    title="Image Augmentation Demo",
+    description="Try out different data augmentation techniques on your image.",
+)
+
+iface.launch()