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| import streamlit as st | |
| import pandas as pd | |
| import plotly.express as px | |
| import plotly.graph_objects as go | |
| from st_aggrid import AgGrid | |
| from st_aggrid.grid_options_builder import GridOptionsBuilder | |
| from st_aggrid.shared import JsCode | |
| from st_aggrid.shared import GridUpdateMode | |
| from transformers import T5Tokenizer, BertForSequenceClassification | |
| import torch | |
| import numpy as np | |
| st.set_page_config(layout="wide") | |
| st.title("Project - Japanese Natural Language Processing (自然言語処理) using Transformers") | |
| st.sidebar.subheader("自然言語処理 トピック") | |
| topic = st.sidebar.radio(label="Select the NLP project topics", options=["Sentiment Analysis","Text Summarization"]) | |
| st.write("-" * 5) | |
| jp_review_text = None | |
| #JAPANESE_SENTIMENT_PROJECT_PATH = './Japanese Amazon reviews sentiments/' | |
| if topic == "Sentiment Analysis": | |
| st.markdown( | |
| "<h2 style='text-align: left; color:#EE82EE; font-size:25px;'><b>Transfer Learning based Japanese Sentiments Analysis using BERT<b></h2>", | |
| unsafe_allow_html=True) | |
| st.markdown( | |
| "<h3 style='text-align: center; color:#F63366; font-size:18px;'><b>Japanese Amazon Reviews Data (日本のAmazonレビューデータ)<b></h3>", | |
| unsafe_allow_html=True) | |
| amazon_jp_reviews = pd.read_csv("review_val.csv").sample(frac=1,random_state=10).iloc[:16000] | |
| cellstyle_jscode = JsCode( | |
| """ | |
| function(params) { | |
| if (params.value.includes('positive')) { | |
| return { | |
| 'color': 'black', | |
| 'backgroundColor': '#32CD32' | |
| } | |
| } else { | |
| return { | |
| 'color': 'black', | |
| 'backgroundColor': '#FF7F7F' | |
| } | |
| } | |
| }; | |
| """ | |
| ) | |
| st.write('<style>div.row-widget.stRadio > div{flex-direction:row;justify-content: center;} </style>', | |
| unsafe_allow_html=True) | |
| st.write('<style>div.st-bf{flex-direction:column;} div.st-ag{font-weight:bold;padding-left:2px;}</style>', | |
| unsafe_allow_html=True) | |
| choose = st.radio("", ("Choose a review from the dataframe below", "Manually write review")) | |
| SELECT_ONE_REVIEW = "Choose a review from the dataframe below" | |
| WRITE_REVIEW = "Manually write review" | |
| gb = GridOptionsBuilder.from_dataframe(amazon_jp_reviews) | |
| gb.configure_column("sentiment", cellStyle=cellstyle_jscode) | |
| gb.configure_pagination() | |
| if choose == SELECT_ONE_REVIEW: | |
| gb.configure_selection(selection_mode="single", use_checkbox=True, suppressRowDeselection=False) | |
| gridOptions = gb.build() | |
| if choose == SELECT_ONE_REVIEW: | |
| jp_review_choice = AgGrid(amazon_jp_reviews, gridOptions=gridOptions, theme='material', | |
| enable_enterprise_modules=True, | |
| allow_unsafe_jscode=True, update_mode=GridUpdateMode.SELECTION_CHANGED) | |
| st.info("Select any one the Japanese Reviews by clicking the checkbox. Reviews can be navigated from each page.") | |
| if len(jp_review_choice['selected_rows']) != 0: | |
| jp_review_text = jp_review_choice['selected_rows'][0]['review'] | |
| st.markdown( | |
| "<h3 style='text-align: center; color:#F63366; font-size:18px;'><b>Selected Review in JSON (JSONで選択されたレビュー)<b></h3>", | |
| unsafe_allow_html=True) | |
| st.write(jp_review_choice['selected_rows']) | |
| if choose == WRITE_REVIEW: | |
| AgGrid(amazon_jp_reviews, gridOptions=gridOptions, theme='material', | |
| enable_enterprise_modules=True, | |
| allow_unsafe_jscode=True) | |
| with open("test_reviews_jp.csv", "rb") as file: | |
| st.download_button(label="Download Additional Japanese Reviews", data=file, | |
| file_name="Additional Japanese Reviews.csv") | |
| st.info("Additional subset of Japanese Reviews can be downloaded and any review can be copied & pasted in text area.") | |
| sample_japanese_review_input = "子供のレッスンバッグ用に購入。 思ったより大きく、ピアノ教本を入れるには充分でした。中は汚れてました。 何より驚いたのは、商品の梱包。 ２つ折は許せるが、透明ビニール袋の底思いっきり空いてますけど？ 何これ？包むっていうか挟んで終わり？底が全開している。 引っ張れば誰でも中身の注文書も、商品も見れる状態って何なの？ 個人情報が晒されて、商品も粗末な扱いで嫌な気持ちでした。 郵送で中身が無事のが奇跡じゃないでしょうか？ ありえない" | |
| jp_review_text = st.text_area(label="Press 'Ctrl+Enter' after writing review in below text area", | |
| value=sample_japanese_review_input) | |
| if len(jp_review_text) == 0: | |
| st.error("Input text cannot empty. Either write the japanese review in text area manually or select the review from the grid.") | |
| if jp_review_text: | |
| st.markdown( | |
| "<h3 style='text-align: center; color:#F63366; font-size:18px;'><b>Sentence-Piece based Japanese Tokenizer using RoBERTA<b></h3>", | |
| unsafe_allow_html=True) | |
| tokens_column, tokenID_column = st.columns(2) | |
| tokenizer = T5Tokenizer.from_pretrained('rinna/japanese-roberta-base') | |
| tokens = tokenizer.tokenize(jp_review_text) | |
| token_ids = tokenizer.convert_tokens_to_ids(tokens) | |
| with tokens_column: | |
| token_expander = st.expander("Expand to see the tokens", expanded=False) | |
| with token_expander: | |
| st.write(tokens) | |
| with tokenID_column: | |
| tokenID_expander = st.expander("Expand to see the token IDs", expanded=False) | |
| with tokenID_expander: | |
| st.write(token_ids) | |
| st.markdown( | |
| "<h3 style='text-align: center; color:#F63366; font-size:18px;'><b>Encoded Japanese Review Text to get Input IDs and attention masks as PyTorch Tensor<b></h3>", | |
| unsafe_allow_html=True) | |
| encoded_data = tokenizer.batch_encode_plus(np.array([jp_review_text]).astype('object'), | |
| add_special_tokens=True, | |
| return_attention_mask=True, | |
| padding=True, | |
| max_length=200, | |
| return_tensors='pt', | |
| truncation=True) | |
| input_ids = encoded_data['input_ids'] | |
| attention_masks = encoded_data['attention_mask'] | |
| input_ids_column, attention_masks_column = st.columns(2) | |
| with input_ids_column: | |
| input_ids_expander = st.expander("Expand to see the input IDs tensor") | |
| with input_ids_expander: | |
| st.write(input_ids) | |
| with attention_masks_column: | |
| attention_masks_expander = st.expander("Expand to see the attention mask tensor") | |
| with attention_masks_expander: | |
| st.write(attention_masks) | |
| st.markdown( | |
| "<h3 style='text-align: center; color:#F63366; font-size:18px;'><b>Predict Sentiment of review using Fine-Tuned Japanese BERT<b></h3>", | |
| unsafe_allow_html=True) | |
| label_dict = {'positive': 1, 'negative': 0} | |
| if st.button("Predict Sentiment"): | |
| with st.spinner("Wait.."): | |
| predictions = [] | |
| model = BertForSequenceClassification.from_pretrained("shubh2014shiv/jp_review_sentiments_amzn", | |
| num_labels=len(label_dict), | |
| output_attentions=False, | |
| output_hidden_states=False) | |
| #model.load_state_dict( | |
| # torch.load(JAPANESE_SENTIMENT_PROJECT_PATH + 'FineTuneJapaneseBert_AmazonReviewSentiments.pt', | |
| # map_location=torch.device('cpu'))) | |
| model.load_state_dict( | |
| torch.load('reviewSentiments_jp.pt', | |
| map_location=torch.device('cpu'))) | |
| inputs = { | |
| 'input_ids': input_ids, | |
| 'attention_mask': attention_masks | |
| } | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| logits = outputs.logits | |
| logits = logits.detach().cpu().numpy() | |
| scores = 1 / (1 + np.exp(-1 * logits)) | |
| result = {"TEXT (文章)": jp_review_text,'NEGATIVE (ネガティブ)': scores[0][0], 'POSITIVE (ポジティブ)': scores[0][1]} | |
| result_col,graph_col = st.columns(2) | |
| with result_col: | |
| st.write(result) | |
| with graph_col: | |
| fig = px.bar(x=['NEGATIVE (ネガティブ)','POSITIVE (ポジティブ)'],y=[result['NEGATIVE (ネガティブ)'],result['POSITIVE (ポジティブ)']]) | |
| fig.update_layout(title="Probability distribution of Sentiment for the given text",\ | |
| yaxis_title="Probability (確率)") | |
| fig.update_traces(marker_color=['#FF7F7F','#32CD32']) | |
| st.plotly_chart(fig) | |
| elif topic == "Text Summarization": | |
| st.markdown( | |
| "<h2 style='text-align: left; color:#EE82EE; font-size:25px;'><b>Summarizing Japanese News Article using multi-Lingual T5 (mT5)<b></h2>", | |
| unsafe_allow_html=True) | |
| st.markdown( | |
| "<h3 style='text-align: center; color:#F63366; font-size:18px;'><b>Japanese News Article Data<b></h3>", | |
| unsafe_allow_html=True) | |
| news_articles = pd.read_csv("jp_news_articles.csv").sample(frac=0.75, | |
| random_state=42) | |
| gb = GridOptionsBuilder.from_dataframe(news_articles) | |
| gb.configure_pagination() | |
| gb.configure_selection(selection_mode="single", use_checkbox=True, suppressRowDeselection=False) | |
| gridOptions = gb.build() | |
| jp_article = AgGrid(news_articles, gridOptions=gridOptions, theme='material', | |
| enable_enterprise_modules=True, | |
| allow_unsafe_jscode=True, update_mode=GridUpdateMode.SELECTION_CHANGED) | |
| # WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip())) | |
| if len(jp_article['selected_rows']) == 0: | |
| st.info("Pick any one Japanese News Article by selecting the checkbox. News articles can be navigated by clicking on page navigator at right-bottom") | |
| else: | |
| article_text = jp_article['selected_rows'][0]['News Articles'] | |
| text = st.text_area(label="Text from selected Japanese News Article(ニュース記事)", value=article_text, height=500) | |
| summary_length = st.slider(label="Select the maximum length of summary (要約の最大長を選択します )", min_value=120,max_value=160,step=5) | |
| if text and st.button("Summarize it! (要約しよう)"): | |
| waitPlaceholder = st.image("wait.gif") | |
| summarization_model_name = "csebuetnlp/mT5_multilingual_XLSum" | |
| tokenizer = AutoTokenizer.from_pretrained(summarization_model_name ) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(summarization_model_name ) | |
| input_ids = tokenizer( | |
| article_text, | |
| return_tensors="pt", | |
| padding="max_length", | |
| truncation=True, | |
| max_length=512 | |
| )["input_ids"] | |
| output_ids = model.generate( | |
| input_ids=input_ids, | |
| max_length=summary_length, | |
| no_repeat_ngram_size=2, | |
| num_beams=4 | |
| )[0] | |
| summary = tokenizer.decode( | |
| output_ids, | |
| skip_special_tokens=True, | |
| clean_up_tokenization_spaces=False | |
| ) | |
| waitPlaceholder.empty() | |
| st.markdown( | |
| "<h2 style='text-align: left; color:#32CD32; font-size:25px;'><b>Summary （要約文）<b></h2>", | |
| unsafe_allow_html=True) | |
| st.write(summary) | |