Update app.py

app.py

@@ -1,15 +1,12 @@
 import gradio as gr
 
-
 from translation import Translator, LANGUAGES, MODEL_URL
-LANGUAGES_LIST = list(LANGUAGES.keys())
 
+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,
@@ -21,34 +18,28 @@ def translate_wrapper(text, src, trg, by_sentence=True, preprocess=True, random=
     )
     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.
 """
 
-
 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),
+        gr.Textbox(label="Text to Translate", lines=2, placeholder='Enter text to translate'),
+        gr.Dropdown(LANGUAGES_LIST, type="value", label='Source Language', value=LANGUAGES_LIST[0], description='Select the source language'),
+        gr.Dropdown(LANGUAGES_LIST, type="value", label='Target Language', value=LANGUAGES_LIST[1], description='Select the target language'),
+        gr.Checkbox(label="Translate by Sentence", value=True, description='If checked, the text will be translated sentence by sentence'),
+        gr.Checkbox(label="Apply Text Preprocessing", value=True, description='If checked, the text will be preprocessed before translation'),
+        gr.Checkbox(label="Randomize", value=False, description='If checked, the translation will use random sampling'),
+        gr.Slider(minimum=1, maximum=5, step=1, label="Number of Beams", value=4, description='Select the number of beams for the translation'),
     ],
     "text",
-    title='Erzya-Russian translation',
+    title='Erzya-Russian Translation',
     article=article,
 )
 
-
 if __name__ == '__main__':
     translator = Translator()
-
-    interface.launch()
+    interface.launch()