the simplest version

.gitignore

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+.ipynb_checkpoints

configs/augmentations.json

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+{
+    "ToGray": [],
+    "ToSepia": [],
+    "Blur": [{"name" : "blur_limit",
+              "type" : "int_interval",
+              "limits" : [3, 100],
+              "defaults" : [3, 7]}
+            ]
+}

src/main.py

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+import numpy as np
+import os
+import matplotlib.pyplot as plt
+from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
+import cv2
+import json
+
+import albumentations as A
+import streamlit as st
+
+
+def load_image(image_name, path_to_folder = '../images'):
+    path_to_image = os.path.join(path_to_folder, image_name)
+    image = cv2.imread(path_to_image)
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    return image
+
+
+def show_int_interval(name, limits, defaults, **kwargs):
+    min_max_interval = st.sidebar.slider(name, limits[0], limits[1], defaults)
+    return min_max_interval
+
+
+
+# dict from param name to function showing this param
+param2func = {
+    'int_interval': show_int_interval
+}
+
+
+
+st.title('Demo of Albumentations transforms')
+
+
+# selecting the image
+path_to_images = 'images'
+image_names_list = [x for x in os.listdir(path_to_images) if x[-3:] in ['jpg', 'peg', 'png']]
+image_name = st.sidebar.selectbox('Select an image:', image_names_list)
+image = load_image(image_name, path_to_images)
+
+
+# selecting the transformation
+path_to_config = 'configs/augmentations.json'
+with open(path_to_config, 'r') as config_file:
+    augmentations = json.load(config_file)
+transform_name = st.sidebar.selectbox('Select a transformation:', list(augmentations.keys()))
+transform_params = augmentations[transform_name]
+
+
+# show the transform options
+if len(transform_params) == 0:
+    st.sidebar.text(transform_name + ' transform has no parameters')
+else:
+    for param in transform_params:
+        param['value'] = param2func[param['type']](**param)
+        
+        
+params_string = ','.join([param['name'] + '=' + str(param['value']) for param in transform_params] + ['p=1.0'])
+params_string = '(' + params_string + ')'
+
+st.text('Press R to update')
+exec('transform = A.' + transform_name + params_string)
+st.image([image, transform(image = image)['image']], 
+         caption = ['Original image', 'Transformed image'],
+         width = 320)
+st.text(str(transform.__doc__))