Update app.py

app.py

@@ -1,5 +1,4 @@
 import os
-
 import cv2
 import gradio as gr
 import torch
@@ -8,44 +7,41 @@ from gfpgan.utils import GFPGANer
 from realesrgan.utils import RealESRGANer
 import spaces
 
-os.system("pip freeze")
-# download weights
-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'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .")
-if not os.path.exists('GFPGANv1.3.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .")
-if not os.path.exists('GFPGANv1.4.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P .")
-if not os.path.exists('RestoreFormer.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P .")
-if not os.path.exists('CodeFormer.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.pth -P .")
-
-
-# background enhancer with RealESRGAN
+# Download model weights if not already present
+weights = {
+    'realesr-general-x4v3.pth': 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth',
+    'GFPGANv1.2.pth': 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth',
+    'GFPGANv1.3.pth': 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth',
+    'GFPGANv1.4.pth': 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth',
+    'RestoreFormer.pth': 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth',
+    'CodeFormer.pth': 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.pth'
+}
+
+for filename, url in weights.items():
+    if not os.path.exists(filename):
+        os.system(f"wget {url} -P .")
+
+# Initialize background enhancer (RealESRGAN)
 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
+half = torch.cuda.is_available()
 upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
 
 os.makedirs('output', exist_ok=True)
 
-
-# def inference(img, version, scale, weight):
 @spaces.GPU(enable_queue=True)
 def inference(img, version, scale):
-    # weight /= 100
     print(img, version, scale)
     if scale > 4:
-        scale = 4  # avoid too large scale value
+        scale = 4
+
     try:
         extension = os.path.splitext(os.path.basename(str(img)))[1]
         img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
+
         if len(img.shape) == 3 and img.shape[2] == 4:
             img_mode = 'RGBA'
-        elif len(img.shape) == 2:  # for gray inputs
+        elif len(img.shape) == 2:
             img_mode = None
             img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
         else:
@@ -53,70 +49,62 @@ def inference(img, version, scale):
 
         h, w = img.shape[0:2]
         if h > 3500 or w > 3500:
-            print('too large size')
+            print('Too large size')
             return None, None
-        
+
         if h < 300:
             img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
 
+        # Load selected model
         if version == 'v1.2':
-            face_enhancer = GFPGANer(
-            model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+            face_enhancer = GFPGANer(model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
         elif version == 'v1.3':
-            face_enhancer = GFPGANer(
-            model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+            face_enhancer = GFPGANer(model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
         elif version == 'v1.4':
-            face_enhancer = GFPGANer(
-            model_path='GFPGANv1.4.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+            face_enhancer = GFPGANer(model_path='GFPGANv1.4.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
         elif version == 'RestoreFormer':
-            face_enhancer = GFPGANer(
-            model_path='RestoreFormer.pth', upscale=2, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler)
-        # elif version == 'CodeFormer':
-        #     face_enhancer = GFPGANer(
-        #     model_path='CodeFormer.pth', upscale=2, arch='CodeFormer', channel_multiplier=2, bg_upsampler=upsampler)
+            face_enhancer = GFPGANer(model_path='RestoreFormer.pth', upscale=2, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler)
 
         try:
-            # _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True, weight=weight)
             _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
         except RuntimeError as error:
-            print('Error', error)
+            print('Enhancement error:', error)
+            return None, None
 
         try:
             if scale != 2:
                 interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
-                h, w = img.shape[0:2]
+                h, w = img.shape[:2]
                 output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
         except Exception as error:
-            print('wrong scale input.', error)
-        if img_mode == 'RGBA':  # RGBA images should be saved in png format
-            extension = 'png'
-        else:
-            extension = 'jpg'
+            print('Rescale error:', error)
+
+        extension = 'png' if img_mode == 'RGBA' else 'jpg'
         save_path = f'output/out.{extension}'
         cv2.imwrite(save_path, output)
 
         output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
         return output, save_path
+
     except Exception as error:
-        print('global exception', error)
+        print('Global exception:', error)
         return None, None
 
+# UI Description
+description = "⚠ Currently running on CPU — expect slower performance. Thank you for your patience."
 
-description = "⚠ Sorry for the inconvenience. The Space is currently running on the CPU, which might affect performance. We appreciate your understanding."
-
+# Gradio Interface
 demo = gr.Interface(
-    inference, [
+    fn=inference,
+    inputs=[
         gr.Image(type="filepath", label="Input"),
-        # gr.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer', 'CodeFormer'], type="value", value='v1.4', label='version'),
-        gr.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'], type="value", value='v1.4', label='version'),
+        gr.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'], type="value", value='v1.4', label='Model Version'),
         gr.Number(label="Rescaling factor", value=2),
-        # gr.Slider(0, 100, label='Weight, only for CodeFormer. 0 for better quality, 100 for better identity', value=50)
-    ], [
-        gr.Image(type="numpy", label="Output (The whole image)"),
-        gr.File(label="Download the output image")
+    ],
+    outputs=[
+        gr.Image(type="numpy", label="Enhanced Output"),
+        gr.File(label="Download Enhanced Image")
     ],
     description=description,
-    # examples=[['AI-generate.jpg', 'v1.4', 2, 50], ['lincoln.jpg', 'v1.4', 2, 50], ['Blake_Lively.jpg', 'v1.4', 2, 50],
-    #           ['10045.png', 'v1.4', 2, 50]]).launch()
-
-demo.queue(max_size=50).launch()
+    # examples=[['AI-generate.jpg', 'v1.4', 2], ['lincoln.jpg', 'v1.4', 2], ['Blake_Lively.jpg', 'v1.4', 2], ['10045.png', 'v1.4', 2]]
+).queue(max_size=50).launch()