Update app.py

app.py

@@ -7,28 +7,23 @@ langs = """Afrikaans (af), Amharic (am), Arabic (ar), Asturian (ast), Azerbaijan
 Korean (ko), Luxembourgish; Letzeburgesch (lb), Ganda (lg), Lingala (ln), Lao (lo), Lithuanian (lt), Latvian (lv), Malagasy (mg), Macedonian (mk), Malayalam (ml), Mongolian (mn), Marathi (mr), Malay (ms), Burmese (my), Nepali (ne), Dutch; Flemish (nl), Norwegian (no), Northern Sotho (ns), Occitan (post 1500) (oc), Oriya (or), Panjabi; Punjabi (pa), Polish (pl), Pushto; Pashto (ps), Portuguese (pt), Romanian; Moldavian; Moldovan (ro), Russian (ru), Sindhi (sd), Sinhala; Sinhalese (si), Slovak (sk), 
 Slovenian (sl), Somali (so), Albanian (sq), Serbian (sr), Swati (ss), Sundanese (su), Swedish (sv), Swahili (sw), Tamil (ta), Thai (th), Tagalog (tl), Tswana (tn), 
 Turkish (tr), Ukrainian (uk), Urdu (ur), Uzbek (uz), Vietnamese (vi), Wolof (wo), Xhosa (xh), Yiddish (yi), Yoruba (yo), Chinese (zh), Zulu (zu)"""
-lang_list = [lang.strip() for lang in langs.split(',')]
+lang_list = [lang.strip().split(" ")[-1][1:-1] for lang in langs.split(',')]
 
 model = M2M100ForConditionalGeneration.from_pretrained("alirezamsh/small100")
 tokenizer = SMALL100Tokenizer.from_pretrained("alirezamsh/small100")
 
-description = """This is a demo for the paper [*SMaLL-100: Introducing Shallow Multilingual Machine Translation Model for Low-Resource Languages*](https://arxiv.org/abs/2210.11621) by Alireza Mohammadshahi, Vassilina Nikoulina, Alexandre Berard, Caroline Brun, James Henderson, Laurent Besacier
-
-In this paper, they propose a compact and shallow massively multilingual MT model, and achieve competitive results with M2M-100, while being super smaller and faster. More details are provided [here](https://huggingface.co/alirezamsh/small100). Currently running on 2 vCPU - 16GB RAM."""
-
-def small100_tr(lang, text):
-
-    lang = lang.split(" ")[-1][1:-1]
-    
+def translate(lang, text):
     tokenizer.tgt_lang = lang
     encoded_text = tokenizer(text, return_tensors="pt")
     generated_tokens = model.generate(**encoded_text)
     return tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
 
-examples = [["French (fr)", "Life is like a box of chocolates."]]
-
-output_text = gr.outputs.Textbox()
-gr.Interface(small100_tr, inputs=[gr.inputs.Dropdown(lang_list, label=" Target Language"), 'text'], outputs=output_text, title="SMaLL100: Translate much faster between 100 languages",
-            description=description,
-            examples=examples
-            ).launch()
+Output = gr.outputs.Textbox()
+gr.Interface(
+    translate,
+    inputs=[
+        gr.inputs.Dropdown( lang_list, label="To Language" ),
+        'text'
+    ],
+    outputs=[ Output ],
+).launch()