Update app.py

app.py

@@ -7,30 +7,30 @@ import os
 os.environ["HF_TOKEN"] = os.getenv('hf')
 
 # Load the tokenizer and model from Hugging Face
-tokenizer = AutoTokenizer.from_pretrained("Darwin29/lumina-translate")
-model = AutoModelForSeq2SeqLM.from_pretrained("Darwin29/lumina-translate")
+tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
+model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")
 
 # Translation function
 def translate_text(text, translation_direction):
     if translation_direction == "English to Iban":
-        input_text = f"en->iba: {text}"  # Explicitly set source and target languages
-        lang_code = "en-to-iba"
+        src_lang = "eng_Latn"
+        tgt_lang = "iba_Latn"
     elif translation_direction == "Iban to English":
-        input_text = f"iba->en: {text}"
-        lang_code = "iba-to-en"
+        src_lang = "iba_Latn"
+        tgt_lang = "eng_Latn"
     else:
         return json.dumps({"error": "Invalid translation direction"})
 
     # Tokenize and translate
-    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True, max_length=512)
-    outputs = model.generate(**inputs)
+    inputs = tokenizer(text, return_tensors="pt", src_lang=src_lang)
+    outputs = model.generate(**inputs, forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang])
     translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
     # Return JSON response
     return json.dumps({
         "input_text": text,
         "translated_text": translated_text,
-        "language": lang_code
+        "language": f"{src_lang} to {tgt_lang}"
     }, indent=4)
 
 # Define the Gradio interface
@@ -45,5 +45,5 @@ iface = gr.Interface(
     description="This app translates between English and Iban. Choose the translation direction and enter your text."
 )
 
-# Launch the app
-iface.launch()
+# Launch the app with shareable link
+iface.launch(share=True)