Update app.py

app.py

@@ -161,6 +161,8 @@ def generate_mask_from_video_first_frame(video_path, sam2_predictor):
 def stage1_create_transparent_video(input_file):
     """STAGE 1: Create transparent video using SAM2 + MatAnyone."""
     
+    logger.info("Starting Stage 1: Create transparent video")
+    
     memory_info = get_memory_usage()
     if memory_info.get('gpu_free', 0) < 2.0:
         st.warning("⚠️ Low GPU memory detected. Processing may be slower.")
@@ -174,17 +176,26 @@ def update_progress(progress, message):
             progress = max(0, min(1, progress))
             progress_bar.progress(progress)
             status_text.text(f"Stage 1: {message} | GPU: {get_memory_usage().get('gpu_allocated', 0):.1f}GB")
+            logger.info(f"Stage 1 Progress: {progress:.2f} - {message}")
         
         # Load models
         update_progress(0.05, "Loading SAM2 model...")
+        logger.info("Attempting to load SAM2 predictor...")
         sam2_predictor = load_sam2_predictor()
         if sam2_predictor is None:
+            logger.error("SAM2 predictor failed to load")
+            st.error("❌ Failed to load SAM2 model")
             return None
+        logger.info("SAM2 predictor loaded successfully")
         
         update_progress(0.1, "Loading MatAnyone model...")
+        logger.info("Attempting to load MatAnyone processor...")
         matanyone_processor = load_matanyone_processor()
         if matanyone_processor is None:
+            logger.error("MatAnyone processor failed to load")
+            st.error("❌ Failed to load MatAnyone model")
             return None
+        logger.info("MatAnyone processor loaded successfully")
             
         # Process video to create transparent version
         with tempfile.TemporaryDirectory() as temp_dir:
@@ -242,12 +253,22 @@ def update_progress(progress, message):
                 
     except Exception as e:
         logger.error(f"Error in Stage 1 processing: {str(e)}", exc_info=True)
-        st.error(f"Stage 1 failed: {str(e)}")
+        st.error(f"❌ Stage 1 failed: {str(e)}")
+        
+        # Show additional debug info
+        try:
+            memory_info = get_memory_usage()
+            st.info(f"Memory at failure - GPU: {memory_info.get('gpu_allocated', 0):.1f}GB, RAM: {memory_info.get('ram_used', 0):.1f}GB")
+        except:
+            pass
+            
         return None
     finally:
+        logger.info("Stage 1 cleanup starting...")
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
         gc.collect()
+        logger.info("Stage 1 cleanup completed")
 
 def create_transparent_mov(foreground_path, alpha_path, temp_dir):
     """Create a .mov file with alpha channel from foreground and alpha videos."""
@@ -433,6 +454,25 @@ def show_memory_info():
                      f"Free: {memory_info['gpu_free']:.1f}GB")
         st.metric("RAM Usage", f"{memory_info['ram_used']:.1f}GB",
                  f"Available: {memory_info['ram_available']:.1f}GB")
+        
+        # Test model loading
+        if st.button("🧪 Test Models", help="Test if SAM2 and MatAnyone can load"):
+            with st.spinner("Testing model loading..."):
+                try:
+                    sam2_test = load_sam2_predictor()
+                    if sam2_test:
+                        st.success("✅ SAM2 loads successfully")
+                    else:
+                        st.error("❌ SAM2 failed to load")
+                    
+                    matanyone_test = load_matanyone_processor()
+                    if matanyone_test:
+                        st.success("✅ MatAnyone loads successfully")
+                    else:
+                        st.error("❌ MatAnyone failed to load")
+                except Exception as e:
+                    st.error(f"Model test failed: {e}")
+        
         if st.button("🧹 Clear Cache", help="Free up memory by clearing model cache"):
             clear_model_cache()
             st.success("Cache cleared!")