update

app.py

@@ -15,27 +15,34 @@ logging.basicConfig(
     level=logging.DEBUG,
     format='%(asctime)s - %(levelname)s - %(message)s'
 )
-
-checkpoint_dir = "facebook/final_m2m100"
-# Initialize translation model
+# Update the model loading section
 try:
-    logging.info(f"Attempting to load M2M100 from {checkpoint_dir}")
+    # Try to load custom model
+    checkpoint_dir = "bishaltwr/final_m2m100"
+    logging.info(f"Attempting to load custom M2M100 from {checkpoint_dir}")
     tokenizer = M2M100Tokenizer.from_pretrained(checkpoint_dir)
-    logging.info("M2M100 tokenizer loaded successfully")
-    
     model_m2m = M2M100ForConditionalGeneration.from_pretrained(checkpoint_dir)
-    logging.info("M2M100 model loaded successfully")
-    
+    logging.info("Custom M2M100 model loaded successfully")
+except Exception as e:
+    logging.error(f"Error loading custom M2M100 model: {e}")
+    try:
+        # Fall back to official model
+        checkpoint_dir = "facebook/m2m100_418M"
+        logging.info(f"Attempting to load official M2M100 from {checkpoint_dir}")
+        tokenizer = M2M100Tokenizer.from_pretrained(checkpoint_dir)
+        model_m2m = M2M100ForConditionalGeneration.from_pretrained(checkpoint_dir)
+        logging.info("Official M2M100 model loaded successfully")
+        m2m_available = True
+    except Exception as e2:
+        logging.error(f"Error loading official M2M100 model: {e2}")
+        m2m_available = False
+        logging.info("Setting m2m_available to False")
+
+# Set device after model loading
+if m2m_available:
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     logging.info(f"Using device: {device}")
-    
     model_m2m.to(device)
-    m2m_available = True
-    logging.info("M2M100 model ready for use")
-except Exception as e:
-    logging.error(f"Error loading M2M100 model: {e}")
-    m2m_available = False
-    logging.info("Setting m2m_available to False")
     
 # Initialize ASR model
 model_id = "bishaltwr/wav2vec2-large-mms-1b-nepali"