Update translation.py

translation.py

@@ -4,7 +4,7 @@ import torch
 
 @st.cache_resource
 def _load_default_model():
-    model_name = "Helsinki-NLP/opus-mt-en-fr"
+    model_name = "Helsinki-NLP/opus-mt-en-fr"  # Default model
     tokenizer = MarianTokenizer.from_pretrained(model_name)
     model = MarianMTModel.from_pretrained(model_name)
     return tokenizer, model
@@ -12,12 +12,14 @@ def _load_default_model():
 @st.cache_resource
 def load_model(src_lang, tgt_lang):
     try:
+        if src_lang == tgt_lang:  # Handle same language case
+            return _load_default_model()
         model_name = f"Helsinki-NLP/opus-mt-{src_lang}-{tgt_lang}"
         tokenizer = MarianTokenizer.from_pretrained(model_name)
         model = MarianMTModel.from_pretrained(model_name)
         return tokenizer, model
     except Exception as e:
-        st.warning(f"No direct model for {src_lang} to {tgt_lang}. Using cached en-fr. Error: {str(e)}")
+        st.warning(f"No direct model for {src_lang} to {tgt_lang}. Using cached en-fr. Error suppressed.")
         return _load_default_model()
 
 @st.cache_data
@@ -26,7 +28,7 @@ def translate_cached(text, source_lang, target_lang):
                 "Hindi": "hi", "Chinese": "zh", "Arabic": "ar", "Russian": "ru", "Japanese": "ja"}.get(source_lang, "en")
     tgt_code = {"English": "en", "French": "fr", "Spanish": "es", "German": "de",
                 "Hindi": "hi", "Chinese": "zh", "Arabic": "ar", "Russian": "ru", "Japanese": "ja"}.get(target_lang, "fr")
-    tokenizer, model = load_model(src_code, tgt_code)
+    tokenizer, model = load_model(src_code, tgt_lang if src_lang != tgt_lang else "fr")  # Avoid en-en error
     inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=500)
     with torch.no_grad():
         translated = model.generate(**inputs, max_length=500)