Update app.py

app.py

@@ -1,33 +1,10 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
-import torch
 
-# this model was loaded from https://hf.co/models
-model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")
-tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
-device = 0 if torch.cuda.is_available() else -1
-LANGS = ["ace_Arab", "eng_Latn", "fra_Latn", "spa_Latn"]
+def greet(name):
+    onmt_translate -src name -model NOS-MT-en-gl.pt --output ./output_file.txt --replace_unk -gpu 0
+    return ./output_file.txt
 
-def translate(text, src_lang, tgt_lang):
-    """
-    Translate the text from source lang to target lang
-    """
-    translation_pipeline = pipeline("translation", model=model, tokenizer=tokenizer, src_lang=src_lang, tgt_lang=tgt_lang, max_length=400, device=device)
-    result = translation_pipeline(text)
-    return result[0]['translation_text']
-
-demo = gr.Interface(
-    fn=translate,
-    inputs=[
-        gr.components.Textbox(label="Text"),
-        gr.components.Dropdown(label="Source Language", choices=LANGS),
-        gr.components.Dropdown(label="Target Language", choices=LANGS),
-    ],
-    outputs=["text"],
-    examples=[["Building a translation demo with Gradio is so easy!", "eng_Latn", "spa_Latn"]],
-    cache_examples=False,
-    title="Translation Demo",
-    description="This demo is a simplified version of the original [NLLB-Translator](https://huggingface.co/spaces/Narrativaai/NLLB-Translator) space"
-)
-
-demo.launch()
+demo = gr.Interface(fn=greet, inputs="textbox", outputs="textbox")
+    
+if __name__ == "__main__":
+    demo.launch()