Update models/model_loaders.py

models/model_loaders.py

@@ -4,14 +4,12 @@
 Handles lazy loading of SAM2 and MatAnyone models with caching
 (Enhanced logging, error handling, and memory safety)
 """
-
 import os
 import gc
 import logging
 import streamlit as st
 import torch
 import psutil
-import mediapipe as mp
 from contextlib import contextmanager
 
 logging.basicConfig(level=logging.INFO)
@@ -33,7 +31,7 @@ def get_memory_usage():
     if torch.cuda.is_available():
         memory_info['gpu_allocated'] = torch.cuda.memory_allocated() / 1e9
         memory_info['gpu_reserved'] = torch.cuda.memory_reserved() / 1e9
-        memory_info['gpu_free'] = (torch.cuda.get_device_properties(0).total_memory - 
+        memory_info['gpu_free'] = (torch.cuda.get_device_properties(0).total_memory -
                                   torch.cuda.memory_allocated()) / 1e9
     memory_info['ram_used'] = psutil.virtual_memory().used / 1e9
     memory_info['ram_available'] = psutil.virtual_memory().available / 1e9
@@ -55,13 +53,10 @@ def load_sam2_predictor():
         logger.info("[load_sam2_predictor] Loading SAM2 image predictor...")  # [LOG+SAFETY PATCH]
         from sam2.build_sam import build_sam2
         from sam2.sam2_image_predictor import SAM2ImagePredictor
-
         device = "cuda" if torch.cuda.is_available() else "cpu"
         logger.info(f"[load_sam2_predictor] Using device: {device}")
-
         checkpoint_path = "/home/user/app/checkpoints/sam2.1_hiera_large.pt"
         model_cfg = "/home/user/app/configs/sam2.1/sam2.1_hiera_l.yaml"
-
         if not os.path.exists(checkpoint_path) or not os.path.exists(model_cfg):
             logger.warning("[load_sam2_predictor] Local checkpoints not found, using Hugging Face.")
             predictor = SAM2ImagePredictor.from_pretrained(
@@ -88,15 +83,12 @@ def load_sam2_predictor():
                 logger.info("[load_sam2_predictor] Using local large model")
                 sam2_model = build_sam2(model_cfg, checkpoint_path, device=device)
                 predictor = SAM2ImagePredictor(sam2_model)
-
         if hasattr(predictor, 'model'):
             predictor.model.to(device)
             predictor.model.eval()
             logger.info(f"[load_sam2_predictor] SAM2 model moved to {device} and set to eval mode")
-
         logger.info(f"✅ SAM2 loaded successfully on {device}!")
         return predictor, device
-
     except Exception as e:
         logger.error(f"❌ Failed to load SAM2 predictor: {e}", exc_info=True)
         import traceback
@@ -112,23 +104,18 @@ def load_matanyone_processor():
     try:
         logger.info("[load_matanyone_processor] Loading MatAnyone processor...")  # [LOG+SAFETY PATCH]
         from matanyone import InferenceCore
-
         device = "cuda" if torch.cuda.is_available() else "cpu"
         logger.info(f"[load_matanyone_processor] MatAnyone using device: {device}")
-
         processor = InferenceCore("PeiqingYang/MatAnyone", device=device)
         if hasattr(processor, 'model'):
             processor.model.to(device)
             processor.model.eval()
             logger.info(f"[load_matanyone_processor] MatAnyone model explicitly moved to {device}")
-
         if not hasattr(processor, 'device'):
             processor.device = device
             logger.info(f"[load_matanyone_processor] Set processor.device to {device}")
-
         logger.info(f"✅ MatAnyone loaded successfully on {device}!")
         return processor, device
-
     except Exception as e:
         logger.error(f"❌ Failed to load MatAnyone: {e}", exc_info=True)
         import traceback
@@ -139,15 +126,6 @@ def load_matanyone():
     processor, device = load_matanyone_processor()
     return processor
 
-mp_pose = mp.solutions.pose
-pose = mp_pose.Pose(
-    static_image_mode=False,
-    model_complexity=1,
-    enable_segmentation=True,
-    min_detection_confidence=0.5
-)
-logger.info("✅ MediaPipe Pose initialized",)  # [LOG+SAFETY PATCH]
-
 def test_models():
     results = {
         'sam2': {'loaded': False, 'error': None, 'device': None},