change output from numpy to filepath

app.py

@@ -9,6 +9,7 @@ import os
 import cv2
 import torch
 import torch.nn.functional as F
+import uuid, threading, time
 import gradio as gr
 
 from torchvision.transforms.functional import normalize
@@ -66,6 +67,14 @@ def imread(img_path):
     img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
     return img
 
+def delayed_remove(path, delay=90):
+    time.sleep(delay)
+    try:
+        if os.path.exists(path):
+            os.remove(path)
+    except Exception as e:
+        print(f"[WARN] Failed to remove {path}: {e}")
+
 # set enhancer with RealESRGAN
 def set_realesrgan():
     half = True if torch.cuda.is_available() else False
@@ -124,7 +133,7 @@ def inference(image, face_align, background_enhance, face_upsample, upscale, cod
         img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
         
         if img is None:
-            raise ValueError(f"Failed to read image from {image_path}")
+            raise ValueError("Input image is None")
     
         # print('\timage size:', img.shape)
 
@@ -213,14 +222,28 @@ def inference(image, face_align, background_enhance, face_upsample, upscale, cod
             restored_img = restored_face
 
         # save restored img
-        save_path = f'output/out.png'
-        imwrite(restored_img, str(save_path))
+        # save_path = f'output/out.png'
+        # imwrite(restored_img, str(save_path))
+
+        # restored_img = cv2.cvtColor(restored_img, cv2.COLOR_BGR2RGB)
+        # return restored_img
+
 
-        restored_img = cv2.cvtColor(restored_img, cv2.COLOR_BGR2RGB)
-        return restored_img
+        #save restored img
+        save_path = f"output/{uuid.uuid4().hex}.png"
+        imwrite(restored_img, save_path)
+        
+        threading.Thread(
+            target=delayed_remove,
+            args=(save_path,10),
+            daemon=True
+        ).start()
+
+        return save_path
+        
     except Exception as error:
         print('Global exception', error)
-        return None, None
+        return None
 
 
 title = "CodeFormer: Robust Face Restoration and Enhancement Network"
@@ -291,7 +314,7 @@ demo = gr.Interface(
         gr.Number(value=2, label="Rescaling_Factor (up to 4)"),
         gr.Slider(0, 1, value=0.5, step=0.01, label='Codeformer_Fidelity (0 for better quality, 1 for better identity)')
     ], [
-        gr.Image(type="numpy", label="Output")
+        gr.Image(type="filepath", label="Output")
     ],
     title=title,
     description=description,
@@ -304,7 +327,7 @@ demo = gr.Interface(
         ['05.jpg', True, True, True, 2, 0.1],
         ['06.png', False, True, True, 1, 0.5]
       ],
-    concurrency_limit=2
+    concurrency_limit=1
     )
 
 DEBUG = os.getenv('DEBUG') == '1'