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import torch
import utils
from utils import camel_tokenizer, spacy_tokenizer
from Vocabulary import Vocabulary
from models import Seq2seq_with_attention, Encoder, Decoder, Attention
import gradio as gr
device = 'cuda' if torch.cuda.is_available() else 'cpu'
en_tokenizer = spacy_tokenizer()
en_vocab = Vocabulary(en_tokenizer, max_freq=2, unk=True, sos=True, eos=True)
ar_tokenizer = camel_tokenizer()
ar_vocab = Vocabulary(ar_tokenizer, max_freq=2, unk=True, sos=True, eos=True)
vocabs = torch.load('./seq2seq_with_attention_states.pt', weights_only=False, map_location=device)
en_vocab.set_vocabulary(vocabs['en_vocabulary'])
ar_vocab.set_vocabulary(vocabs['ar_vocabulary'])
seq2seq_with_attention = torch.load("./seq2seq_with_attention.bin", map_location=device, weights_only=False)
def pre_processor(text):
preprocessed = utils.preprocess_en(text)
en_tokens = torch.tensor(en_vocab(preprocessed)).unsqueeze(0).to(device)
return en_tokens
def post_processor(raw_output):
return ar_vocab.tokens_to_text(raw_output[1:-1])
@torch.no_grad
def lunch(raw_input, maxtries=30):
en_tokens = pre_processor(raw_input)
output = seq2seq_with_attention.translate(en_tokens, maxtries)
return post_processor(output)
custom_css ='.gr-button {background-color: #bf4b04; color: white;}'
with gr.Blocks(css=custom_css) as demo:
with gr.Row():
with gr.Column():
input_text = gr.Textbox(label='English Sentence')
gr.Examples(['how are you?',
'she is a good girl.',
'who is a better?'],
inputs=input_text, label="Examples: ")
with gr.Column():
output = gr.Textbox(label="Arabic Translation")
start_btn = gr.Button(value='Submit', elem_classes=["gr-button"])
start_btn.click(fn=lunch, inputs=input_text, outputs=output)
demo.launch()
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