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import torch |
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from utils import Callable_tokenizer, preprocess_en |
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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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seq2seq_with_attention = torch.load("./seq2seq_with_attention_df-CoVoST2_df-opus_seed-123_subword.bin", map_location=device, weights_only=False) |
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en_sp = Callable_tokenizer('./tokenizers/NEW_en_vocab_df-CoVoST2_df-opus_seed-123_vocab-16K_FULL.model') |
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ar_sp = Callable_tokenizer('./tokenizers/NEW_ar_vocab_df-CoVoST2_df-opus_seed-123_vocab-32K_FULL.model') |
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def pre_processor(text): |
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preprocessed = preprocess_en(text) |
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en_tokens = torch.tensor(en_sp.user_tokenization(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_sp.decode(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 better than me?!'], |
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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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