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import torch |
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import torch.onnx |
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from transformer import Transformer |
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import torch |
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from huggingface_hub import hf_hub_download |
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import torch |
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import numpy as np |
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import gradio as gr |
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START_TOKEN = '<START>' |
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PADDING_TOKEN = '<PADDING>' |
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END_TOKEN = '<END>' |
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english_vocabulary = [START_TOKEN, ' ', '!', '"', '#', '$', '%', '&', "'", '(', ')', '*', '+', ',', '-', '.', '/', |
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'0', '1', '2', '3', '4', '5', '6', '7', '8', '9', |
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':', '<', '=', '>', '?', '@', |
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'[', '\\', ']', '^', '_', '`', |
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'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', |
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'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', |
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'y', 'z', |
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'{', '|', '}', '~', PADDING_TOKEN, END_TOKEN] |
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gujarati_vocabulary = [ |
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START_TOKEN, ' ', '!', '"', '#', '$', '%', '&', "'", '(', ')', '*', '+', ',', '-', '.', '/', |
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'૦', '૧', '૨', '૩', '૪', '૫', '૬', '૭', '૮', '૯', |
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':', '<', '=', '>', '?', '@', '[', '\\', ']', '^', '_', '`', |
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'અ', 'આ', 'ઇ', 'ઈ', 'ઉ', 'ઊ', 'ઋ', 'એ', 'ઐ', 'ઓ', 'ઔ', |
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'ક', 'ખ', 'ગ', 'ઘ', 'ઙ', 'ચ', 'છ', 'જ', 'ઝ', 'ઞ', |
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'ટ', 'ઠ', 'ડ', 'ઢ', 'ણ', 'ત', 'થ', 'દ', 'ધ', 'ન', |
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'પ', 'ફ', 'બ', 'ભ', 'મ', 'ય', 'ર', 'લ', 'વ', 'શ', |
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'ષ', 'સ', 'હ', 'ળ', 'ક્ષ', 'જ્ઞ', 'ં', 'ઃ', 'ઁ', 'ા', |
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'િ', 'ી', 'ુ', 'ૂ', 'ે', 'ૈ', 'ો', 'ૌ', '્', 'ૐ', |
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'{', '|', '}', '~', PADDING_TOKEN, END_TOKEN |
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] |
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index_to_gujarati = {k:v for k,v in enumerate(gujarati_vocabulary)} |
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gujarati_to_index = {v:k for k,v in enumerate(gujarati_vocabulary)} |
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index_to_english = {k:v for k,v in enumerate(english_vocabulary)} |
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english_to_index = {v:k for k,v in enumerate(english_vocabulary)} |
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d_model = 512 |
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ffn_hidden = 2048 |
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num_heads = 8 |
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drop_prob = 0.1 |
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num_layers = 6 |
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max_sequence_length = 200 |
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kn_vocab_size = len(gujarati_vocabulary) |
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device = torch.device('cpu') |
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transformer = Transformer(d_model, |
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ffn_hidden, |
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num_heads, |
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drop_prob, |
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num_layers, |
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max_sequence_length, |
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kn_vocab_size, |
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english_to_index, |
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gujarati_to_index, |
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START_TOKEN, |
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END_TOKEN, |
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PADDING_TOKEN) |
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model_file = hf_hub_download(repo_id="yashAI007/English_to_Gujarati_Translation", filename="model.pth") |
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model = torch.load(model_file,map_location='cpu') |
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transformer.load_state_dict(model['model_state_dict']) |
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transformer.to(device) |
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transformer.eval() |
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NEG_INFTY = -1e9 |
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def create_masks(eng_batch, kn_batch): |
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num_sentences = len(eng_batch) |
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look_ahead_mask = torch.full([max_sequence_length, max_sequence_length] , True) |
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look_ahead_mask = torch.triu(look_ahead_mask, diagonal=1) |
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encoder_padding_mask = torch.full([num_sentences, max_sequence_length, max_sequence_length] , False) |
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decoder_padding_mask_self_attention = torch.full([num_sentences, max_sequence_length, max_sequence_length] , False) |
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decoder_padding_mask_cross_attention = torch.full([num_sentences, max_sequence_length, max_sequence_length] , False) |
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for idx in range(num_sentences): |
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eng_sentence_length, kn_sentence_length = len(eng_batch[idx]), len(kn_batch[idx]) |
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eng_chars_to_padding_mask = np.arange(eng_sentence_length + 1, max_sequence_length) |
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kn_chars_to_padding_mask = np.arange(kn_sentence_length + 1, max_sequence_length) |
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encoder_padding_mask[idx, :, eng_chars_to_padding_mask] = True |
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encoder_padding_mask[idx, eng_chars_to_padding_mask, :] = True |
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decoder_padding_mask_self_attention[idx, :, kn_chars_to_padding_mask] = True |
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decoder_padding_mask_self_attention[idx, kn_chars_to_padding_mask, :] = True |
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decoder_padding_mask_cross_attention[idx, :, eng_chars_to_padding_mask] = True |
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decoder_padding_mask_cross_attention[idx, kn_chars_to_padding_mask, :] = True |
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encoder_self_attention_mask = torch.where(encoder_padding_mask, NEG_INFTY, 0) |
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decoder_self_attention_mask = torch.where(look_ahead_mask + decoder_padding_mask_self_attention, NEG_INFTY, 0) |
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decoder_cross_attention_mask = torch.where(decoder_padding_mask_cross_attention, NEG_INFTY, 0) |
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return encoder_self_attention_mask, decoder_self_attention_mask, decoder_cross_attention_mask |
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transformer.eval() |
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def translate(eng_sentence): |
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print("English Sentence:",eng_sentence) |
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eng_sentence = (eng_sentence.lower(),) |
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kn_sentence = ("",) |
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for word_counter in range(max_sequence_length): |
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encoder_self_attention_mask, decoder_self_attention_mask, decoder_cross_attention_mask= create_masks(eng_sentence, kn_sentence) |
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predictions = transformer(eng_sentence, |
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kn_sentence, |
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encoder_self_attention_mask.to(device), |
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decoder_self_attention_mask.to(device), |
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decoder_cross_attention_mask.to(device), |
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enc_start_token=False, |
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enc_end_token=False, |
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dec_start_token=True, |
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dec_end_token=False) |
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next_token_prob_distribution = predictions[0][word_counter] |
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next_token_index = torch.argmax(next_token_prob_distribution).item() |
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next_token = index_to_gujarati[next_token_index] |
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kn_sentence = (kn_sentence[0] + next_token, ) |
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if next_token == END_TOKEN: |
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break |
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print("Gujarati Sentence:",kn_sentence[0][:-5],'\n') |
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return kn_sentence[0][:-5] |
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examples = [ |
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["Hello, how are you?"], |
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["What is your name?"], |
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["I love programming."], |
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["This is a beautiful day."], |
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["Can you help me with this?"], |
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["What time is it?"], |
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["I am learning data science."], |
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["Where is the nearest bus stop?"], |
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["I enjoy reading books."], |
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["Thank you for your help."] |
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] |
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description = "This tool translates English sentences into Gujarati. Please enter your text above to get started!" |
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iface = gr.Interface(fn=translate, |
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inputs="text", |
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outputs="text", |
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title="English to Gujarati Translation", |
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examples=examples, |
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description=description, |
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) |
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if __name__ == "__main__": |
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iface.launch() |
