update

app.py

@@ -214,49 +214,54 @@ print("✅ Image processing functions ready!")
 
 # Main inference function
 def generate_emoji_fixed(input_image):
-    """Fixed inference with correct architecture"""
-
+    """Fixed inference with consistent return type"""
+    
+    # Create default error image
+    def create_error_image(text="Error"):
+        error_img = Image.new('RGB', (256, 256), color='lightgray')
+        return error_img
+    
     if input_image is None:
-        return None, "⚠️ Please upload an image first!"
-
+        print("⚠️ Please upload an image first!")
+        return create_error_image("No Image")
+    
     try:
         print("🚀 Processing image with correct architecture...")
-
+        
         # Preprocess for FaceNet (160x160)
         input_tensor = preprocess_image_for_facenet(input_image, target_size=160).to(device)
         print(f"📊 Input tensor shape: {input_tensor.shape}")
-
+        
         # Inference
         with torch.no_grad():
             print("🧠 Extracting features with FaceNet...")
-            features = face_extractor(input_tensor)  # Should output 512D
+            features = face_extractor(input_tensor)
             print(f"📊 Features shape: {features.shape}")
-
+            
             if torch.isnan(features).any():
-                return None, "❌ NaN detected in features!"
-
+                print("❌ NaN detected in features!")
+                return create_error_image("NaN Features")
+            
             print("🎭 Generating emoji...")
-            emoji_tensor = generator(features)  # Should work with correct architecture
+            emoji_tensor = generator(features)
             print(f"🎨 Generated shape: {emoji_tensor.shape}")
-
+            
             if torch.isnan(emoji_tensor).any():
-                return None, "❌ NaN detected in generated image!"
-
+                print("❌ NaN detected in generated image!")
+                return create_error_image("NaN Output")
+        
         # Postprocess
         emoji_image = postprocess_image(emoji_tensor)
         emoji_upscaled = upscale_image(emoji_image, scale_factor=4)
-
-        status = f"✅ Success! Generated {emoji_image.size[0]}×{emoji_image.size[1]} emoji, upscaled to {emoji_upscaled.size}×{emoji_upscaled.size[1]}"
-        print(status)
-
-        return emoji_upscaled
-
+        
+        print("✅ Success!")
+        return emoji_upscaled  # ✅ Always return single image
+        
     except Exception as e:
-        error_msg = f"❌ Error: {str(e)}"
-        print(error_msg)
+        print(f"❌ Error: {str(e)}")
         import traceback
         traceback.print_exc()
-        return None, error_msg
+        return create_error_image("Processing Error")  # ✅ Always return single image
 
 print("✅ Main inference function ready!")
 
@@ -278,10 +283,7 @@ if __name__ == "__main__":
     demo = gr.Interface(
         fn=generate_emoji_fixed,
         inputs=gr.Image(type="pil", label="Upload Image"),
-        outputs=[
-            gr.Image(type="pil", label="Generated Emoji"),
-            gr.Textbox(label="Status")   # Nếu muốn hiển thị thông báo
-        ],
+        outputs=gr.Image(type="pil", label="Generated Emoji"),
         title="DTN Face-to-Emoji Generator"
     )