update

app.py

@@ -25,48 +25,28 @@ try:
     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")
+    # 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")
 
 # 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)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+logging.info(f"Using device: {device}")
+model_m2m.to(device)
     
 # Initialize ASR model
 model_id = "bishaltwr/wav2vec2-large-mms-1b-nepali"
-try:
-    processor = AutoProcessor.from_pretrained(model_id)
-    model_asr = Wav2Vec2ForCTC.from_pretrained(model_id, ignore_mismatched_sizes=True)
-    asr_available = True
-except Exception as e:
-    logging.error(f"Error loading ASR model: {e}")
-    asr_available = False
+processor = AutoProcessor.from_pretrained(model_id)
+model_asr = Wav2Vec2ForCTC.from_pretrained(model_id, ignore_mismatched_sizes=True)
 
 # Initialize X-Transformer model
-try:
-    from inference import translate as xtranslate
-    xtransformer_available = True
-except Exception as e:
-    logging.error(f"Error loading XTransformer model: {e}")
-    xtransformer_available = False
+from inference import translate as xtranslate
 
 def m2m_translate(text, source_lang, target_lang):
     """Translation using M2M100 model"""
-    if not m2m_available:
-        return "M2M100 model not available"
-    
     tokenizer.src_lang = source_lang
     inputs = tokenizer(text, return_tensors="pt").to(device)
     translated_tokens = model_m2m.generate(
@@ -78,9 +58,6 @@ def m2m_translate(text, source_lang, target_lang):
 
 def transcribe_audio(audio_path, language="npi"):
     """Transcribe audio using ASR model"""
-    if not asr_available:
-        return "ASR model not available"
-    
     import librosa
     audio, sr = librosa.load(audio_path, sr=16000)
     processor.tokenizer.set_target_lang(language)
@@ -130,9 +107,9 @@ def translate_text(text, model_choice, source_lang=None, target_lang=None):
         target_lang = "ne" if source_lang == "en" else "en"
     
     # Choose the translation model
-    if model_choice == "XTransformer" and xtransformer_available:
+    if model_choice == "XTransformer":
         return xtranslate(text)
-    elif model_choice == "M2M100" and m2m_available:
+    elif model_choice == "M2M100":
         return m2m_translate(text, source_lang=source_lang, target_lang=target_lang)
     else:
         return "Selected model is not available"