~ app.py

app.py

@@ -1,45 +1,45 @@
-# import gradio as gr
+import gradio as gr
 
-# from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
-# model_path = "anzorq/m2m100_418M_ft_ru-kbd_44K"  
-# src_lang="ru" 
-# tgt_lang="zu"
+model_path = "anzorq/m2m100_418M_ft_ru-kbd_44K"  
+src_lang="ru" 
+tgt_lang="zu"
 
-# tokenizer = AutoTokenizer.from_pretrained(model_path, src_lang=src_lang)
-# model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path, src_lang=src_lang)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
 
-# def translate(text):
-#   inputs = tokenizer(text, return_tensors="pt")
+def translate(text):
+  inputs = tokenizer(text, return_tensors="pt")
 
-#   translated_tokens = model.generate(
-#       **inputs, forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang], num_beams=4, num_return_sequences=4
-#   )
+  translated_tokens = model.generate(
+      **inputs, forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang], num_beams=4, num_return_sequences=4
+  )
 
-#   translations = []
-#   for translation in tokenizer.batch_decode(translated_tokens, skip_special_tokens=True):
-#       translations.append(translation)
+  translations = []
+  for translation in tokenizer.batch_decode(translated_tokens, skip_special_tokens=True):
+      translations.append(translation)
 
-#   return translations
+  return "\n • ".join(translations)
 
-# output = gr.outputs.Textbox()
-# iface = gr.Interface(fn=translate, inputs="text", outputs=output)
-# iface.launch()
+output = gr.outputs.Textbox()
+iface = gr.Interface(fn=translate, inputs="text", outputs=output)
+iface.launch()
 
-import gradio as gr
+# import gradio as gr
 
-title = "Русско-черкесский переводчик"
-description = "Demo of a Russian-Circassian (Kabardian dialect) translator. <br>It is based on Facebook's <a href=\"https://about.fb.com/news/2020/10/first-multilingual-machine-translation-model/\">M2M-100 model</a> machine learning model, and has been trained on 45,000 Russian-Circassian sentence pairs. <br>It can also translate from 100 other languages to Circassian (English, French, Spanish, etc.), but less accurately. <br>The data corpus is constantly being expanded, and we need help in finding sentence sources, OCR, data cleaning, etc. <br>If you are interested in helping out with this project, please contact me at the link below.<br><br>This is only a demo, not a finished product. Translation quality is still low and will improve with time and more data.<br>45,000 sentence pairs is not enough to create an accurate machine translation model, and more data is needed.<br>You can help by finding sentence sources (books, web pages, etc.), scanning books, OCRing documents, data cleaning, and other tasks.<br><br>If you are interested in helping out with this project, contact me at the link below."
-article = """<p style='text-align: center'><a href='https://arxiv.org/abs/1806.00187'>Scaling Neural Machine Translation</a> | <a href='https://github.com/pytorch/fairseq/'>Github Repo</a></p>"""
-
-examples = [
-    ["Мы идем домой"],
-    ["Сегодня хорошая погода"],
-    ["Дети играют во дворе"],
-    ["We live in a big house"],
-    ["Tu es une bonne personne."],
-    ["أين تعيش؟"],
-    ["Bir şeyler yapmak istiyorum."],
-]
-
-gr.Interface.load("models/anzorq/m2m100_418M_ft_ru-kbd_44K", title=title, description=description, article=article, examples=examples).launch()
+# title = "Русско-черкесский переводчик"
+# description = "Demo of a Russian-Circassian (Kabardian dialect) translator. <br>It is based on Facebook's <a href=\"https://about.fb.com/news/2020/10/first-multilingual-machine-translation-model/\">M2M-100 model</a> machine learning model, and has been trained on 45,000 Russian-Circassian sentence pairs. <br>It can also translate from 100 other languages to Circassian (English, French, Spanish, etc.), but less accurately. <br>The data corpus is constantly being expanded, and we need help in finding sentence sources, OCR, data cleaning, etc. <br>If you are interested in helping out with this project, please contact me at the link below.<br><br>This is only a demo, not a finished product. Translation quality is still low and will improve with time and more data.<br>45,000 sentence pairs is not enough to create an accurate machine translation model, and more data is needed.<br>You can help by finding sentence sources (books, web pages, etc.), scanning books, OCRing documents, data cleaning, and other tasks.<br><br>If you are interested in helping out with this project, contact me at the link below."
+# article = """<p style='text-align: center'><a href='https://arxiv.org/abs/1806.00187'>Scaling Neural Machine Translation</a> | <a href='https://github.com/pytorch/fairseq/'>Github Repo</a></p>"""
+
+# examples = [
+#     ["Мы идем домой"],
+#     ["Сегодня хорошая погода"],
+#     ["Дети ��грают во дворе"],
+#     ["We live in a big house"],
+#     ["Tu es une bonne personne."],
+#     ["أين تعيش؟"],
+#     ["Bir şeyler yapmak istiyorum."],
+# ]
+
+# gr.Interface.load("models/anzorq/m2m100_418M_ft_ru-kbd_44K", title=title, description=description, article=article, examples=examples).launch()