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| from transformers import T5ForConditionalGeneration, T5TokenizerFast | |
| from torch.utils.data import DataLoader | |
| import streamlit as st | |
| import pandas as pd | |
| import torch | |
| import os | |
| # Let us define the main page | |
| st.markdown("Translation page 🔠") | |
| # Dropdown for the translation type | |
| translation_type = st.sidebar.selectbox("Translation Type", options=["French ➡️ Wolof", "Wolof ➡️ French"]) | |
| # define a dictionary of versions | |
| models = { | |
| "Version ✌️": { | |
| "French ➡️ Wolof": { | |
| "checkpoints": "wolof-translate/wolof_translate/checkpoints/t5_small_custom_train_results_fw_v4", | |
| "tokenizer": "wolof-translate/wolof_translate/tokenizers/t5_tokenizers/tokenizer_v4.json", | |
| "max_len": None | |
| } | |
| }, | |
| "Version ☝️": { | |
| "French ➡️ Wolof": { | |
| "checkpoints": "wolof-translate/wolof_translate/checkpoints/t5_small_custom_train_results_fw_v3", | |
| "tokenizer": "wolof-translate/wolof_translate/tokenizers/t5_tokenizers/tokenizer_v3.json", | |
| "max_len": 51 | |
| }, | |
| "Wolof ➡️ French": { | |
| "checkpoints": "wolof-translate/wolof_translate/checkpoints/t5_small_custom_train_results_wf_v3", | |
| "tokenizer": "wolof-translate/wolof_translate/trokenizers/t5_tokenizers/tokenizer_v3.json", | |
| "max_len": 51 | |
| } | |
| } | |
| } | |
| # add special characters from Wolof | |
| sp_wolof_chars = pd.read_csv('wolof-translate/wolof_translate/data/wolof_writing/wolof_special_chars.csv') | |
| # add definitions | |
| sp_wolof_words = pd.read_csv('wolof-translate/wolof_translate/data/wolof_writing/definitions.csv') | |
| # let us add a callback functions to change the input text | |
| def add_symbol_to_text(): | |
| st.session_state.input_text += st.session_state.symbol | |
| def add_word_to_text(): | |
| word = st.session_state.word.split('/')[0].strip() | |
| st.session_state.input_text += word | |
| # Dropdown for introducing wolof special characters | |
| if translation_type == "Wolof ➡️ French": | |
| symbol = st.sidebar.selectbox("Wolof characters", key="symbol", options = sp_wolof_chars['wolof_special_chars'], on_change=add_symbol_to_text) | |
| word = st.sidebar.selectbox("Wolof words/Definitions", key="word", options = [sp_wolof_words.loc[i, 'wolof']+" / "+sp_wolof_words.loc[i, 'french'] for i in range(sp_wolof_words.shape[0])], on_change=add_word_to_text) | |
| # Dropdown for the model version | |
| version = st.sidebar.selectbox("Model version", options=["Version ☝️", "Version ✌️"]) | |
| # Recuperate the number of sentences to provide | |
| temperature = st.sidebar.slider("How randomly need you the translated sentences to be from 0% to 100%", min_value = 0, | |
| max_value = 100) | |
| # make the process | |
| try: | |
| # recuperate the max length | |
| max_len = models[version][translation_type]['max_len'] | |
| # let us get the best model | |
| def get_modelfw_v3(): | |
| # recuperate checkpoints | |
| checkpoints = torch.load(os.path.join('wolof-translate/wolof_translate/checkpoints/t5_small_custom_train_results_fw_v3', "best_checkpoints.pth"), map_location=torch.device('cpu')) | |
| # recuperate the tokenizer | |
| tokenizer_file = "wolof-translate/wolof_translate/tokenizers/t5_tokenizers/tokenizer_v3.json" | |
| # initialize the tokenizer | |
| tokenizer = T5TokenizerFast(tokenizer_file=tokenizer_file) | |
| model = T5ForConditionalGeneration.from_pretrained('t5-small') | |
| # resize the token embeddings | |
| model.resize_token_embeddings(len(tokenizer)) | |
| model.load_state_dict(checkpoints['model_state_dict']) | |
| return model, tokenizer | |
| def get_modelwf_v3(): | |
| # recuperate checkpoints | |
| checkpoints = torch.load(os.path.join('wolof-translate/wolof_translate/checkpoints/t5_small_custom_train_results_wf_v3', "best_checkpoints.pth"), map_location=torch.device('cpu')) | |
| # recuperate the tokenizer | |
| tokenizer_file = "wolof-translate/wolof_translate/tokenizers/t5_tokenizers/tokenizer_v3.json" | |
| # initialize the tokenizer | |
| tokenizer = T5TokenizerFast(tokenizer_file=tokenizer_file) | |
| model = T5ForConditionalGeneration.from_pretrained('t5-small') | |
| # resize the token embeddings | |
| model.resize_token_embeddings(len(tokenizer)) | |
| model.load_state_dict(checkpoints['model_state_dict']) | |
| return model, tokenizer | |
| if version == "Version ☝️": | |
| if translation_type == "French ➡️ Wolof": | |
| model, tokenizer = get_modelfw_v3() | |
| elif translation_type == "Wolof ➡️ French": | |
| model, tokenizer = get_modelwf_v3() | |
| # set the model to eval mode | |
| _ = model.eval() | |
| language = "Wolof" if translation_type == "French ➡️ Wolof" else "French" | |
| # Add a title | |
| st.header(f"Translate French sentences to {language} 👌") | |
| # Recuperate two columns | |
| left, right = st.columns(2) | |
| if translation_type == "French ➡️ Wolof": | |
| # recuperate sentences | |
| left.subheader('Give me some sentences in French: ') | |
| else: | |
| # recuperate sentences | |
| left.subheader('Give me some sentences in Wolof: ') | |
| # for i in range(number): | |
| left.text_input(f"- Sentence", key = f"input_text") | |
| # run model inference on all test data | |
| original_translations, predicted_translations, original_texts, scores = [], [], [], {} | |
| if translation_type == "French ➡️ Wolof": | |
| # print a sentence recuperated from the session | |
| right.subheader("Translation to Wolof:") | |
| else: | |
| # print a sentence recuperated from the session | |
| right.subheader("Translation to French:") | |
| # for i in range(number): | |
| sentence = st.session_state[f"input_text"] + tokenizer.eos_token | |
| if not sentence == tokenizer.eos_token: | |
| # Let us encode the sentences | |
| encoding = tokenizer([sentence], return_tensors='pt', max_length=max_len, padding='max_length', truncation=True) | |
| # Let us recuperate the input ids | |
| input_ids = encoding.input_ids | |
| # Let us recuperate the mask | |
| mask = encoding.attention_mask | |
| # Let us recuperate the pad token id | |
| pad_token_id = tokenizer.pad_token_id | |
| # perform prediction | |
| predictions = model.generate(input_ids, do_sample = False, top_k = 50, max_length = max_len, top_p = 0.90, | |
| temperature = temperature/100, num_return_sequences = 0, attention_mask = mask, pad_token_id = pad_token_id) | |
| # decode the predictions | |
| predicted_sentence = tokenizer.batch_decode(predictions, skip_special_tokens = True) | |
| # provide the prediction | |
| right.write(f"Translation: {predicted_sentence[0]}") | |
| else: | |
| # provide the prediction | |
| right.write(f"Translation: ") | |
| except Exception as e: | |
| st.warning("The chosen model is not available yet !", icon = "⚠️") | |
| st.write(e) | |