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