Finalized the toy description

app.py

@@ -126,10 +126,10 @@ def create_gradient_vector_field(gx, gy, image_shape, step=20, reverse=False):
 def transform_image(image, func_choice, radius, center_x, center_y, strength, edge_smoothness, reverse_gradient=True, spiral_frequency=1):
     I = np.asarray(Image.open(image))    
 
-    def zoom(x, y):
+    def pinch(x, y):
         return x**2 + y**2
 
-    def rotation(x, y):
+    def shift(x, y):
         return np.arctan2(y, x)
 
     def bulge(x, y):
@@ -141,10 +141,10 @@ def transform_image(image, func_choice, radius, center_x, center_y, strength, ed
         theta = np.arctan2(y, x)
         return r * np.sin(theta - frequency * r)
 
-    if func_choice == "Zoom":
-        func = zoom
-    elif func_choice == "Rotation":
-        func = rotation
+    if func_choice == "Pinch":
+        func = pinch
+    elif func_choice == "Shift":
+        func = shift 
     elif func_choice == "Bulge":
         func = bulge
     elif func_choice == "Spiral":
@@ -159,7 +159,7 @@ demo = gr.Interface(
     fn=transform_image,
     inputs=[
         gr.Image(type="filepath"),
-        gr.Dropdown(["Bulge", "Spiral", "Zoom", "Rotation"], value="Bulge", label="Function"), 
+        gr.Dropdown(["Pinch", "Spiral", "Shift", "Bulge"], value="Bulge", label="Function"), 
         gr.Slider(0, 0.5, value=0.25, label="Radius (as fraction of image size)"),
         gr.Slider(0, 1, value=0.5, label="Center X"),
         gr.Slider(0, 1, value=0.5, label="Center Y"),
@@ -172,10 +172,7 @@ demo = gr.Interface(
         gr.Image(label="Gradient Vector Field")
     ],
     title="Image Transformation Demo!",
-    description="This is a demo of the tool that I will be using to generate images to train a machine learning model on! This tool allows you to play around with the simple bulge effect primarily, I will be attempting to impliment manual function input within the app next!"
+    description="This is the baseline function that will be used to generate the database for a machine learning model I am working on called 'DistortionMl'! The goal of this model is to detect and then reverse image transformations that can be generated here! You can read more about the project at this repository link : https://github.com/nick-leland/DistortionML. The main function that I was working on is the 'Bulge' function, I can't really guarantee that the others work well (;"
 )
 
-def update_spiral_frequency(func_choice):
-    return gr.Slider(visible=(func_choice == "Spiral"))
-
 demo.launch(share=True)