Make it thicc

app.py

@@ -1,54 +1,30 @@
 import gradio as gr
-
-
+import threading
 from translation import Translator, LANGUAGES, MODEL_URL
-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 = f"""
-This is the demo for a NLLB-200-600M model fine-tuned for a few (mostly new) languages.
-
-The model itself is available at https://huggingface.co/{MODEL_URL}
-
-If you want to host in on your own backend, consider running this dockerized app: https://github.com/slone-nlp/nllb-docker-demo.
-"""
+translator = Translator()
+lang_list = list(LANGUAGES.keys())
 
+@gr.cache
+def translate(text, src, trg, **kwargs):
+    src_lang, tgt_lang = LANGUAGES.get(src), LANGUAGES.get(trg)
+    return translator.translate(text, src_lang, tgt_lang, **kwargs)
 
 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),
+    fn=translate,
+    inputs=[
+        gr.Textbox(label="Text", lines=2, description="Enter the text you want to translate."),
+        gr.Dropdown(lang_list, type="value", label='Source Language', value=lang_list[0], description="Select the language of the input text."),
+        gr.Dropdown(lang_list, type="value", label='Target Language', value=lang_list[1], description="Select the language you want to translate to."),
+        gr.Checkbox(label="By Sentence", value=True, description="Translate the text sentence by sentence."),
+        gr.Checkbox(label="Preprocess", value=True, description="Preprocess the text before translation."),
+        gr.Checkbox(label="Randomize", value=False, description="Randomize the translation output."),
+        gr.Slider(value=4, label="Number of Beams", minimum=1, maximum=5, step=1, description="Select the number of beams for the translation process.")
     ],
-    "text",
-    title='Erzya-Russian translation',
-    article=article,
+    outputs="text",
+    title='Erzya-Russian Translation',
+    article=f"Model: https://huggingface.co/{MODEL_URL}"
 )
 
-
 if __name__ == '__main__':
-    translator = Translator()
-
-    interface.launch()
+    threading.Thread(target=interface.launch, kwargs=dict(share=True, server_name="0.0.0.0", server_port=7860)).start()