initial commit

app.py

@@ -1,16 +1,31 @@
-import gradio
-
-import numpy as np
 import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-ko-en")
+model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-ko-en")
+
+device = 0 if torch.cuda.is_available() else -1
+
+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"),
 
-def sepia(input_img):
-    sepia_filter = np.array([
-        [0.393, 0.769, 0.189], 
-        [0.349, 0.686, 0.168], 
-        [0.272, 0.534, 0.131]
-    ])
-    sepia_img = input_img.dot(sepia_filter.T)
-    sepia_img /= sepia_img.max()
-    return sepia_img
+    ],
+    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"
+)
 
-gradio.Interface(sepia, gr.Image(shape=(200, 200)), "image").launch(share=True)
+demo.launch()