Update translation.py

translation.py

@@ -45,22 +45,18 @@ def load_model(source_lang, target_lang):
     tokenizer_model_pair = all_models.get(model_key)
     if tokenizer_model_pair and tokenizer_model_pair[0] and tokenizer_model_pair[1]:
         return tokenizer_model_pair
-    # Use direct English pivot with defined combined_translate
-    if source_lang != "en" and target_lang != "en":
-        en_pivot_pair = all_models.get((source_lang, "en")) or _load_model_pair(source_lang, "en")
-        if en_pivot_pair[0] and en_pivot_pair[1]:
-            return en_pivot_pair
-    default_tokenizer, _ = _load_default_model()
+    # Simplified pivot through English
+    default_tokenizer, default_model = _load_default_model()
     def combined_translate(text):
         with torch.no_grad():
             if source_lang != "en":
-                src_to_en_tokenizer, src_to_en_model = all_models.get((source_lang, "en"), _load_default_model())
+                src_to_en_tokenizer, src_to_en_model = all_models.get((source_lang, "en"), (default_tokenizer, default_model))
                 en_text = src_to_en_tokenizer.decode(src_to_en_model.generate(**src_to_en_tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=500))[0], skip_special_tokens=True)
             else:
                 en_text = text
             if target_lang != "en":
-                en_to_tgt_tokenizer, en_to_tgt_model = all_models.get(("en", target_lang), _load_default_model())
-                return en_to_tgt_tokenizer.decode(en_to_tgt_model.generate(**en_to_tgt_tokenizer(en_text, return_tensors="pt", padding=True, truncation=True, max_length=1000))[0], skip_special_tokens=True)  # Increased max_length
+                en_to_tgt_tokenizer, en_to_tgt_model = all_models.get(("en", target_lang), (default_tokenizer, default_model))
+                return en_to_tgt_tokenizer.decode(en_to_tgt_model.generate(**en_to_tgt_tokenizer(en_text, return_tensors="pt", padding=True, truncation=True, max_length=1000))[0], skip_special_tokens=True)
             return en_text
     return default_tokenizer, CombinedModel()
 
@@ -81,7 +77,7 @@ def translate(text, source_lang, target_lang):
         tokenizer, model = load_model(source_lang, target_lang)
         inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=500)
         with torch.no_grad():
-            translated = model.generate(**inputs, max_length=1000 if target_lang == "hi" else 500, num_beams=6 if target_lang == "hi" else 4, early_stopping=True)  # Adjusted for Hindi
+            translated = model.generate(**inputs, max_length=1000 if target_lang == "hi" else 500, num_beams=6 if target_lang == "hi" else 4, early_stopping=True)
         result = tokenizer.decode(translated[0], skip_special_tokens=True)
         return result if result.strip() else text
     except Exception as e: