Update utils/memory_manager.py

utils/memory_manager.py

@@ -5,8 +5,7 @@ import gc
 logger = logging.getLogger("webtapi.memory")
 
 class MemoryManager:
-    def __init__(self, memory_limit_mb=512):
-        self.memory_limit = memory_limit_mb * 1024 * 1024  # Convert to bytes
+    def __init__(self):
         self.warning_threshold = 0.8  # 80% of limit
     
     def check_memory_usage(self):
@@ -15,12 +14,6 @@ class MemoryManager:
         memory_info = process.memory_info()
         return memory_info.rss  # Resident Set Size in bytes
     
-    def is_memory_available(self, required_mb=100):
-        """Check if enough memory is available"""
-        current_usage = self.check_memory_usage()
-        available_memory = self.memory_limit - current_usage
-        return available_memory >= (required_mb * 1024 * 1024)
-    
     def optimize_memory(self):
         """Free up memory by cleaning up and garbage collection"""
         logger.info("Optimizing memory usage...")
@@ -28,25 +21,10 @@ class MemoryManager:
         # Force garbage collection
         gc.collect()
         
-        # Clear CUDA cache if available
-        try:
-            import torch
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-        except ImportError:
-            pass
-        
         current_usage_mb = self.check_memory_usage() / (1024 * 1024)
         logger.info(f"Current memory usage: {current_usage_mb:.2f} MB")
         
         return current_usage_mb
-    
-    def should_use_ai(self, operation_mb=200):
-        """Determine if AI operations should be attempted based on available memory"""
-        if not self.is_memory_available(operation_mb):
-            logger.warning(f"Insufficient memory for AI operation (needed: {operation_mb}MB)")
-            return False
-        return True
 
 # Global instance
 memory_manager = MemoryManager()