Update app.py

app.py

@@ -6,14 +6,28 @@ os.system("pip install transformers sentencepiece torch")
 from transformers import M2M100ForConditionalGeneration
 from tokenization_small100 import SMALL100Tokenizer
 
+langs = """Afrikaans (af), Amharic (am), Arabic (ar), Asturian (ast), Azerbaijani (az), Bashkir (ba), Belarusian (be), Bulgarian (bg), Bengali (bn), Breton (br), Bosnian (bs), Catalan; Valencian (ca), Cebuano (ceb), Czech (cs), Welsh (cy), Danish (da), German (de), Greeek (el), English (en), Spanish (es), Estonian (et), Persian (fa), Fulah (ff), Finnish (fi), French (fr), Western Frisian (fy), Irish (ga), Gaelic; Scottish Gaelic (gd), Galician (gl), Gujarati (gu), Hausa (ha), Hebrew (he), Hindi (hi), Croatian (hr), Haitian; Haitian Creole (ht), Hungarian (hu), Armenian (hy), Indonesian (id), Igbo (ig), Iloko (ilo), Icelandic (is), Italian (it), Japanese (ja), Javanese (jv), Georgian (ka), Kazakh (kk), Central Khmer (km), Kannada (kn), 
+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(',')]
+
 model = M2M100ForConditionalGeneration.from_pretrained("alirezamsh/small100")
 tokenizer = SMALL100Tokenizer.from_pretrained("alirezamsh/small100")
 
-def fn(text, lang):
+def small100_tr(text, lang):
     tokenizer.tgt_lang = lang
-    encoded = tokenizer(text, return_tensors="pt")
-    generated_tokens = model.generate(**encoded)
-    return tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+    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)", "엄마판다는 새끼가 있네."]]
+
+demo = gr.Interface(fn=small100_tr, inputs=["text", "text"], outputs="text")
+demo.launch()
 
-demo = gr.Interface(fn=fn, inputs=["text", "text"], outputs="text")
-demo.launch()
+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 Between 100 languages much faster",
+            description="Demo page for SMaLL100 model",
+            examples=examples
+            ).launch()