New push to update preview to latest

app.py

@@ -1,31 +1,51 @@
+from transformers import MarianMTModel, MarianTokenizer
 import gradio as gr
-from transformers import T5ForConditionalGeneration, T5Tokenizer
-
-MODEL_NAME = "victorachede/tiv-translator"  # IMPORTANT FIX LATER
-
-tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME)
-model = T5ForConditionalGeneration.from_pretrained(MODEL_NAME)
-
-def translate(text):
-    text = "translate English to Tiv: " + text
-
-    inputs = tokenizer(text, return_tensors="pt", truncation=True).input_ids
 
+MODEL_NAME = "victorachede/tiv-translator"
+
+print("Loading tokenizer...")
+tokenizer = MarianTokenizer.from_pretrained(MODEL_NAME)
+print("Loading model...")
+model = MarianMTModel.from_pretrained(MODEL_NAME)
+print("Model ready.")
+
+def translate(text: str) -> str:
+    if not text or not text.strip():
+        return ""
+    
+    inputs = tokenizer(
+        text.strip(),
+        return_tensors="pt",
+        padding=True,
+        truncation=True,
+        max_length=512
+    )
+    
     outputs = model.generate(
-        inputs,
+        **inputs,
         max_length=128,
         num_beams=5,
         repetition_penalty=1.3,
-        no_repeat_ngram_size=3
+        no_repeat_ngram_size=3,
+        early_stopping=True
     )
-
+    
     return tokenizer.decode(outputs[0], skip_special_tokens=True)
 
 demo = gr.Interface(
     fn=translate,
-    inputs="text",
-    outputs="text",
-    title="English → Tiv Translator"
+    inputs=gr.Textbox(
+        label="English",
+        placeholder="Enter English text...",
+        lines=3
+    ),
+    outputs=gr.Textbox(
+        label="Tiv",
+        lines=3
+    ),
+    title="English → Tiv Translator",
+    description="TRANSLTR — by Black Sheep Co.",
+    allow_flagging="never"
 )
 
 demo.launch()