Update evaluate.py

evaluate.py

@@ -20,8 +20,8 @@ from translator import get_asr_model, get_asr_processor, LANGUAGE_CODES
 # Configure logging
 logger = logging.getLogger("speech_api")
 
-if not hasattr(handle_evaluation_request, 'cache'):
-    handle_evaluation_request.cache = {}
+# Initialize cache at module level instead
+EVALUATION_CACHE = {}
 
 def calculate_similarity(text1, text2):
     """Calculate text similarity percentage."""
@@ -66,6 +66,7 @@ def setup_reference_patterns(reference_dir, sample_rate=16000):
                 continue
     
     return created_dirs, 0  # Return 0 created files since we're not creating dummy files anymore
+
 def search_reference_directories():
     """Search for possible reference directories in various locations"""
     possible_locations = [
@@ -222,6 +223,7 @@ def init_reference_audio(reference_dir, output_dir):
         except:
             logger.critical("💥 CRITICAL: Failed to create even a fallback directory")
             return reference_dir
+
 def handle_upload_reference(request, reference_dir, sample_rate):
     """Handle upload of reference audio files"""
     try:
@@ -349,10 +351,10 @@ def handle_evaluation_request(request, reference_dir, output_dir, sample_rate):
         audio_hash = hashlib.md5(audio_content).hexdigest()
         cache_key = f"{audio_hash}_{reference_locator}_{language}"
         
-        # Check in-memory cache (define EVALUATION_CACHE at module level)
-        if hasattr(handle_evaluation_request, 'cache') and cache_key in handle_evaluation_request.cache:
+        # Check in-memory cache using the module-level cache
+        if cache_key in EVALUATION_CACHE:
             logger.info(f"[{request_id}] ✅ Using cached evaluation result")
-            return handle_evaluation_request.cache[cache_key]
+            return EVALUATION_CACHE[cache_key]
 
         # Construct full reference directory path
         reference_dir_path = os.path.join(reference_dir, reference_locator)
@@ -571,16 +573,12 @@ def handle_evaluation_request(request, reference_dir, output_dir, sample_rate):
             "quick_evaluation": True
         })
         
-        # OPTIMIZATION 6: Cache the result for future requests
-        if not hasattr(handle_evaluation_request, 'cache'):
-            handle_evaluation_request.cache = {}
-        
-        # Store in cache (limit cache size to avoid memory issues)
+        # OPTIMIZATION 6: Cache the result for future requests using module-level cache
         MAX_CACHE_SIZE = 50
-        handle_evaluation_request.cache[cache_key] = response
-        if len(handle_evaluation_request.cache) > MAX_CACHE_SIZE:
+        EVALUATION_CACHE[cache_key] = response
+        if len(EVALUATION_CACHE) > MAX_CACHE_SIZE:
             # Remove oldest entry (simplified approach)
-            handle_evaluation_request.cache.pop(next(iter(handle_evaluation_request.cache)))
+            EVALUATION_CACHE.pop(next(iter(EVALUATION_CACHE)))
         
         return response