import os
import gradio as gr

download="wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1IpcnaQ2ScX_zodt2aLlXa_5Kkntl0nue' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\\n/p')&id=1IpcnaQ2ScX_zodt2aLlXa_5Kkntl0nue\" -O en-indic.zip && rm -rf /tmp/cookies.txt"
os.system(download)
os.system('unzip /home/user/app/en-indic.zip')

from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils
import gradio as gr
from inference.engine import Model
indic2en_model = Model(expdir='/home/user/app/en-indic')

INDIC = {"Assamese": "as", "Bengali": "bn", "Gujarati": "gu", "Hindi": "hi","Kannada": "kn","Malayalam": "ml", "Marathi": "mr", "Odia": "or","Punjabi": "pa","Tamil": "ta", "Telugu" : "te"}


def translate(text, lang):
  return indic2en_model.translate_paragraph(text, 'en', INDIC[lang])


languages = list(INDIC.keys())
drop_down = gr.inputs.Dropdown(languages, type="value", default="Hindi", label="Select Target Language")
text = gr.inputs.Textbox(lines=5, placeholder="Enter Text to translate", default="", label="Enter Text in English")
text_ouptut = gr.outputs.Textbox(type="auto", label="Translated text in Target Language")

# example=[['I want to translate this sentence in Hindi','Hindi'],
#         ['I am feeling very good today.', 'Bengali']]

supported_lang = ', '.join(languages)
iface = gr.Interface(fn=translate, inputs=[text,drop_down] , outputs=text_ouptut, title='IndicTrans NMT System', description = 'Currently the model supports ' + supported_lang, article = 'Original repository can be found [here](https://github.com/AI4Bharat/indicTrans)' , examples=None)
iface.launch(enable_queue=True)