Update app.py

app.py

@@ -1,5 +1,4 @@
 import os
-
 import sys
 from torchvision.transforms import functional
 sys.modules["torchvision.transforms.functional_tensor"] = functional
@@ -12,8 +11,7 @@ import torch
 import cv2
 import gradio as gr
 
-
-#Download Required Models
+# Baixar Modelos Necessários
 if not os.path.exists('realesr-general-x4v3.pth'):
     os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
 if not os.path.exists('GFPGANv1.2.pth'):
@@ -25,20 +23,14 @@ if not os.path.exists('GFPGANv1.4.pth'):
 if not os.path.exists('RestoreFormer.pth'):
     os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P .")
 
-
 model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
 model_path = 'realesr-general-x4v3.pth'
 half = True if torch.cuda.is_available() else False
 upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
 
-
-# Save Image to the Directory
-# os.makedirs('output', exist_ok=True)
-
+# Função de Upscaling e Restauração de Imagem
 def upscaler(img, version, scale):
-
     try:
-        
         img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
         if len(img.shape) == 3 and img.shape[2] == 4:
             img_mode = 'RGBA'
@@ -48,26 +40,22 @@ def upscaler(img, version, scale):
         else:
             img_mode = None
 
-
         h, w = img.shape[0:2]
         if h < 300:
             img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
 
-        
         face_enhancer = GFPGANer(
-            model_path=f'{version}.pth', 
-            upscale=2, 
-            arch='RestoreFormer' if version=='RestoreFormer' else 'clean',
+            model_path=f'{version}.pth',
+            upscale=2,
+            arch='RestoreFormer' if version == 'RestoreFormer' else 'clean',
             channel_multiplier=2,
             bg_upsampler=upsampler
         )
 
-
         try:
             _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
         except RuntimeError as error:
-            print('Error', error)
-
+            print('Erro', error)
 
         try:
             if scale != 2:
@@ -75,40 +63,25 @@ def upscaler(img, version, scale):
                 h, w = img.shape[0:2]
                 output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
         except Exception as error:
-            print('wrong scale input.', error)
-
-        # Save Image to the Directory
-        # ext = os.path.splitext(os.path.basename(str(img)))[1]
-        # if img_mode == 'RGBA':
-        #     ext = 'png'
-        # else:
-        #     ext = 'jpg'
-        #
-        # save_path = f'output/out.{ext}'
-        # cv2.imwrite(save_path, output)
-        # return output, save_path
+            print('Erro de escala.', error)
 
         output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
         return output
     except Exception as error:
-        print('global exception', error)
+        print('Exceção global', error)
         return None, None
 
 if __name__ == "__main__":
-
-    title = "Image Upscaler & Restoring [GFPGAN Algorithm]"
-
     demo = gr.Interface(
             upscaler, [
-                gr.Image(type="filepath", label="Input"),
-                gr.Radio(['GFPGANv1.2', 'GFPGANv1.3', 'GFPGANv1.4', 'RestoreFormer'], type="value", label='version'),
-                gr.Number(label="Rescaling factor"),
+                gr.Image(type="filepath", label="Entrada"),
+                gr.Radio(['GFPGANv1.2', 'GFPGANv1.3', 'GFPGANv1.4', 'RestoreFormer'], type="value", label='Versão'),
+                gr.Number(label="Fator de Redimensionamento"),
             ], [
-                gr.Image(type="numpy", label="Output"),
+                gr.Image(type="numpy", label="Saída"),
             ],
-            title=title,
             allow_flagging="never"
         )
 
     demo.queue()
-    demo.launch()
+    demo.launch()