Update app.py

app.py

@@ -1,4 +1,5 @@
 import os
+
 import cv2
 import gradio as gr
 import torch
@@ -7,38 +8,40 @@ from gfpgan.utils import GFPGANer
 from realesrgan.utils import RealESRGANer
 import spaces
 
-# 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)
+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
 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 = torch.cuda.is_available()
+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)
 
 os.makedirs('output', exist_ok=True)
 
+
+# def inference(img, version, scale, weight):
 @spaces.GPU(enable_queue=True)
 def inference(img, version, scale):
     print(img, version, scale)
     if scale > 4:
         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:
@@ -49,62 +52,63 @@ 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)
-
+            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)
         except RuntimeError as error:
-            print('Enhancement error:', error)
-            return None, None
+            print('Error', error)
 
         try:
             if scale != 2:
                 interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
-                h, w = img.shape[:2]
+                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('Rescale error:', error)
-
-        extension = 'png' if img_mode == 'RGBA' else 'jpg'
+            print('wrong scale input.', error)
+        if img_mode == 'RGBA':
+            extension = 'png'
+        else:
+            extension = '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."
 
-# Gradio Interface
+description = "⚠ Sorry for the inconvenience. The Space is currently running on the CPU, which might affect performance. We appreciate your understanding."
+
 demo = gr.Interface(
-    fn=inference,
-    inputs=[
+    inference, [
         gr.Image(type="filepath", label="Input"),
-        gr.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'], type="value", value='v1.4', label='Model Version'),
+        gr.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'], type="value", value='v1.4', label='version'),
         gr.Number(label="Rescaling factor", value=2),
-    ],
-    outputs=[
-        gr.Image(type="numpy", label="Enhanced Output"),
-        gr.File(label="Download Enhanced Image")
+
+    ], [
+        gr.Image(type="numpy", label="Output (The whole image)"),
+        gr.File(label="Download the output image")
     ],
     description=description,
-    
-).queue(max_size=50).launch()
+
+
+demo.queue(max_size=50).launch()