Update app.py

app.py

@@ -311,8 +311,13 @@ def remove_objects(image, object_name, confidence_threshold, mask_expansion, inp
         # Step 2: Create mask with adaptive expansion
         mask = create_mask_from_detections(image, detections, mask_expansion)
         
-        # Step 3: Use SDXL for inpainting
-        inpaint_api_url = "https://api-inference.huggingface.co/models/diffusers/stable-diffusion-xl-1.0-inpainting-0.1"
+        # Step 3: Use reliable inpainting model
+        # Try multiple inpainting models in order of preference
+        inpaint_models = [
+            "https://api-inference.huggingface.co/models/runwayml/stable-diffusion-inpainting",
+            "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-2-inpainting",
+            "https://api-inference.huggingface.co/models/diffusers/stable-diffusion-xl-1.0-inpainting-0.1"
+        ]
         
         headers = {"Authorization": f"Bearer {token}"}
         
@@ -342,26 +347,46 @@ def remove_objects(image, object_name, confidence_threshold, mask_expansion, inp
             'strength': 0.99
         }
         
-        try:
-            response = requests.post(inpaint_api_url, headers=headers, files=files, data=data, timeout=90)
-            
-            if response.status_code == 200:
-                result_image = Image.open(io.BytesIO(response.content))
-                detected_labels = [d.get('label', 'unknown') for d in detections]
-                status_msg = f"✅ Successfully removed {len(detections)} '{object_name}' object(s)\n"
-                status_msg += f"🎯 Detected as: {', '.join(detected_labels)}\n"
-                status_msg += f"🔧 Used: Advanced YOLO + SDXL Inpainting"
-            else:
-                # Fallback: return original with mask overlay for debugging
-                result_image = create_mask_overlay(image, mask)
-                status_msg = f"⚠️ SDXL inpainting failed (HTTP {response.status_code}). Showing detected areas in red.\n"
-                status_msg += f"🎯 Found {len(detections)} '{object_name}' object(s) - detection was successful"
+        # Try multiple inpainting models
+        inpainting_success = False
+        last_error = ""
+        
+        for i, inpaint_api_url in enumerate(inpaint_models):
+            try:
+                print(f"Trying inpainting model {i+1}/{len(inpaint_models)}: {inpaint_api_url.split('/')[-1]}")
                 
-        except Exception as e:
+                response = requests.post(inpaint_api_url, headers=headers, files=files, data=data, timeout=120)
+                
+                if response.status_code == 503:
+                    # Model is loading, wait and retry once
+                    import time
+                    time.sleep(10)
+                    response = requests.post(inpaint_api_url, headers=headers, files=files, data=data, timeout=120)
+                
+                if response.status_code == 200:
+                    result_image = Image.open(io.BytesIO(response.content))
+                    detected_labels = [d.get('label', 'unknown') for d in detections]
+                    status_msg = f"✅ Successfully removed {len(detections)} '{object_name}' object(s)\n"
+                    status_msg += f"🎯 Detected as: {', '.join(detected_labels)}\n"
+                    status_msg += f"🔧 Used: {inpaint_api_url.split('/')[-1]} for inpainting"
+                    inpainting_success = True
+                    break
+                else:
+                    last_error = f"HTTP {response.status_code}: {response.text[:200]}"
+                    print(f"Model {i+1} failed: {last_error}")
+                    continue
+                    
+            except Exception as e:
+                last_error = str(e)
+                print(f"Model {i+1} error: {last_error}")
+                continue
+        
+        if not inpainting_success:
             # Fallback: return original with mask overlay for debugging
             result_image = create_mask_overlay(image, mask)
-            status_msg = f"⚠️ SDXL inpainting failed: {str(e)}. Showing detected areas in red.\n"
-            status_msg += f"🎯 Found {len(detections)} '{object_name}' object(s) - detection was successful"
+            status_msg = f"⚠️ All inpainting models failed. Last error: {last_error}\n"
+            status_msg += f"🎯 Found {len(detections)} '{object_name}' object(s) - detection was successful\n"
+            status_msg += f"📍 Showing detected areas in red overlay"
         
         return result_image, mask, status_msg
         
@@ -534,16 +559,16 @@ with gr.Blocks(
             - **And literally thousands more!**
             
             ### ⚠️ System Info:
-            - **🔍 Models**: YOLOS-Small + DETR + OWL-ViT (auto-fallback)
-            - **🎨 Inpainting**: SDXL for photorealistic results
-            - **⏱️ Processing**: 30-90 seconds (first request may be slower)
+            - **🔍 Detection**: YOLOS-Small + DETR + OWL-ViT (auto-fallback)
+            - **🎨 Inpainting**: Multiple SD models with auto-fallback
+            - **⏱️ Processing**: 30-120 seconds (first request may be slower)
             - **🔧 Auto-retry**: Tries multiple models if one is busy
             - **Token Required**: HF token needed for API access
             
             **If you get "models unavailable" error:**
             - Wait 2-3 minutes and try again (models loading)
-            - Check your HF token is valid
-            - Try during off-peak hours
+            - Check your HF token is valid and has API quota
+            - Try during off-peak hours for better performance
             """)
 
 if __name__ == "__main__":