update ⚒️

backend/src/example/examples.py

@@ -7,19 +7,11 @@ import torch
 from torch import nn
 import numpy as np
 import PIL
-<<<<<<< HEAD
 
 sys.path.insert(0, "../resources")
 from resources.module import GradioModule, register
 
 
-=======
-
-sys.path.insert(0, "../resources")
-from resources.module import GradioModule, register
-
-"""
->>>>>>> origin/main
 @GradioModule
 class Pictionary:
 
@@ -111,41 +103,6 @@ class FSD:
         carr = np.array(pixels / np.max(pixels, axis=(0, 1)) * 255, dtype=np.uint8)
         return [PIL.Image.fromarray(carr), self.palette_reduce(img, nc) ]
 
-<<<<<<< HEAD
-=======
-    @register(inputs=[gr.Image(), gr.Image(), gr.Slider(0.00, 16)], outputs=gr.Gallery())
-    def examples(self, img, img2, nc, ) -> 'list[PIL.Image.Image]':
-        pixels = np.array(img, dtype=float) / 255
-        new_height, new_width, _ = img.shape 
-        for row in range(new_height):
-            for col in range(new_width):
-                old_val = pixels[row, col].copy()
-                new_val = self.get_new_val(old_val, nc)
-                pixels[row, col] = new_val
-                err = old_val - new_val
-                if col < new_width - 1:
-                    pixels[row, col + 1] += err * 7 / 16
-                if row < new_height - 1:
-                    if col > 0:
-                        pixels[row + 1, col - 1] += err * 3 / 16
-                    pixels[row + 1, col] += err * 5 / 16
-                    if col < new_width - 1:
-                        pixels[row + 1, col + 1] += err * 1 / 16
-        carr = np.array(pixels / np.max(pixels, axis=(0, 1)) * 255, dtype=np.uint8)
-        return [PIL.Image.fromarray(carr), self.palette_reduce(img, nc) ]
-
-
-@GradioModule
-class C:
-
-    def Hello(self):
-        return "Hello"
-    
-    @register(inputs="text", outputs="text")
-    def Greeting(self, name):
-        return self.Hello() + " " + name
-
->>>>>>> origin/main
 @GradioModule
 class stock_forecast: