Update app.py

app.py

@@ -1,5 +1,5 @@
 import streamlit as st
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
 import evaluate
 
 # Page configuration
@@ -12,9 +12,9 @@ st.set_page_config(
 # Load model and tokenizer
 @st.cache_resource
 def load_model():
-    model_name = "facebook/nllb-200-distilled-600M"
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    model_name = "facebook/m2m100_418M"
+    tokenizer = M2M100Tokenizer.from_pretrained(model_name)
+    model = M2M100ForConditionalGeneration.from_pretrained(model_name)
     return tokenizer, model
 
 tokenizer, model = load_model()
@@ -23,7 +23,6 @@ tokenizer, model = load_model()
 bleu = evaluate.load("bleu")
 bertscore = evaluate.load("bertscore")
 comet = evaluate.load("comet", module_type="metric")
-# For BERTurk, use Turkish BERT for BERTScore
 bertturk = evaluate.load("bertscore")
 
 # UI
@@ -37,7 +36,10 @@ if st.button("Translate & Evaluate"):
     else:
         # Tokenize and generate
         inputs = tokenizer(input_text, return_tensors="pt")
-        outputs = model.generate(**inputs, forced_bos_token_id=tokenizer.get_lang_id("tur_TUR"))
+        outputs = model.generate(
+            **inputs,
+            forced_bos_token_id=tokenizer.get_lang_id("tur")
+        )
         translation = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
 
         # Display translation
@@ -55,7 +57,7 @@ if st.button("Translate & Evaluate"):
         else:
             st.info("No reference provided: skipping BLEU.")
 
-        # Compute BERTScore (multilingual)
+        # Compute BERTScore (general multilingual)
         bs = bertscore.compute(
             predictions=predictions,
             references=[ref_text] if ref_text.strip() else [translation],
@@ -71,7 +73,7 @@ if st.button("Translate & Evaluate"):
         )
         st.metric("BERTurk (f1)", f"{bt['f1'][0]*100:.2f}")
 
-        # Compute COMET if reference
+        # Compute COMET if reference provided
         if references:
             comet_score = comet.compute(
                 model="Unbabel/wmt22-comet-da",