Create app.py

app.py

@@ -0,0 +1,53 @@
+import gradio as gr
+
+
+from translation import Translator, LANGUAGES
+LANGUAGES_LIST = list(LANGUAGES.keys())
+
+
+def translate_wrapper(text, src, trg, by_sentence=True, preprocess=True, random=False, num_beams=4):
+    src_lang = LANGUAGES.get(src)
+    tgt_lang = LANGUAGES.get(trg)
+    # if src == trg:
+    #     return 'Please choose two different languages'
+    result = translator.translate(
+        text=text,
+        src_lang=src_lang,
+        tgt_lang=tgt_lang,
+        do_sample=random,
+        num_beams=int(num_beams),
+        by_sentence=by_sentence,
+        preprocess=preprocess,
+    )
+    return result
+
+
+article = """
+This is a NLLB-200-600M model fine-tuned for translation between Russian and Tyvan (Tuvan) languages,
+using the data from https://tyvan.ru/.
+This model is described in https://cointegrated.medium.com/a37fc706b865.
+If you want to host in on your own backend, consider running this dockerized app: https://github.com/slone-nlp/nllb-docker-demo.
+"""
+
+
+interface = gr.Interface(
+    translate_wrapper,
+    [
+        gr.Textbox(label="Text", lines=2, placeholder='text to translate '),
+        gr.Dropdown(LANGUAGES_LIST, type="value", label='source language', value=LANGUAGES_LIST[0]),
+        gr.Dropdown(LANGUAGES_LIST, type="value", label='target language', value=LANGUAGES_LIST[1]),
+        gr.Checkbox(label="by sentence", value=True),
+        gr.Checkbox(label="text preprocesing", value=True),
+        gr.Checkbox(label="randomize", value=False),
+        gr.Dropdown([1, 2, 3, 4, 5], label="number of beams", value=4),
+    ],
+    "text",
+    title='Tyvan-Russian translaton',
+    article=article,
+)
+
+
+if __name__ == '__main__':
+    translator = Translator()
+
+    interface.launch()