app.py

# import gradio as gr

# from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# 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)

# 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
#   )

#   translations = []
#   for translation in tokenizer.batch_decode(translated_tokens, skip_special_tokens=True):
#       translations.append(translation)

#   return translations

# output = gr.outputs.Textbox()
# iface = gr.Interface(fn=translate, inputs="text", outputs=output)
# iface.launch()

import gradio as gr

title = "Русско-черкесский переводчик"
description = """
              Demo of a Russian-Circassian (Kabardian dialect) translator. 

              The translator is based on a machine learning model that has been trained on 45,000 Russian-Circassian sentence pairs. 

              It is based on Facebook's <a href="https://about.fb.com/news/2020/10/first-multilingual-machine-translation-model/">M2M-100 model</a>, and can also translate from 100 other languages to Circassian (English, French, Spanish, etc.), but less accurately.

              The data corpus is constantly being expanded, and we need help in finding sentence sources, OCR, data cleaning, etc. 

              If you are interested in helping out with this project, please 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 = [
  ["Hello world!"],
  ["PyTorch Hub is a pre-trained model repository designed to facilitate research reproducibility."]
]

gr.Interface.load("models/anzorq/m2m100_418M_ft_ru-kbd_44K", title=title, description=description, article=article, examples=examples).launch()