Update app.py

Added comment while creating new folder for downloading the Japanese English translation model

app.py

@@ -1,361 +1,361 @@
-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,AutoTokenizer, AutoModelForSeq2SeqLM
-import torch
-import numpy as np
-import json
-from transformers import AutoTokenizer, BertTokenizer, AutoModelWithLMHead
-import pytorch_lightning as pl
-from pathlib import Path
-
-# Defining some functions for caching purpose by streamlit
-class TranslationModel(pl.LightningModule):
-    def __init__(self):
-        super().__init__()
-        self.model = AutoModelWithLMHead.from_pretrained("Helsinki-NLP/opus-mt-ja-en", return_dict=True)
-
-
-@st.experimental_singleton
-def loadFineTunedJaEn_NMT_Model():
-    save_dest = Path('model')
-    save_dest.mkdir(exist_ok=True)
-
-    f_checkpoint = Path("model/best-checkpoint.ckpt")
-
-    if not f_checkpoint.exists():
-        with st.spinner("Downloading model.This may take a while! \n Don't refresh or close this page!"):
-            from GD_download import download_file_from_google_drive
-            download_file_from_google_drive('1CZQKGj9hSqj7kEuJp_jm7bNVXrbcFsgP', f_checkpoint)
-
-    trained_model = TranslationModel.load_from_checkpoint(f_checkpoint)
-
-    return trained_model
-
-@st.experimental_singleton
-def getJpEn_Tokenizers():
-    try:
-        with st.spinner("Downloading English and Japanese Transformer Tokenizers"):
-            ja_tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-ja-en")
-            en_tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-    except:
-        st.error("Issue with downloading tokenizers")
-
-    return ja_tokenizer, en_tokenizer
-
-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","Japanese to English Translation"])
-
-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_val.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)
-elif topic == "Japanese to English Translation":
-    st.markdown(
-        "<h2 style='text-align: left; color:#EE82EE; font-size:25px;'><b>Japanese to English translation (for short sentences)<b></h2>",
-        unsafe_allow_html=True)
-    st.markdown(
-        "<h3 style='text-align: center; color:#F63366; font-size:18px;'><b>Business Scene Dialog Japanese-English Corpus<b></h3>",
-        unsafe_allow_html=True)
-
-    st.write("Below given Japanese-English pair is from 'Business Scene Dialog Corpus' by the University of Tokyo")
-    link = '[Corpus GitHub Link](https://github.com/tsuruoka-lab/BSD)'
-    st.markdown(link, unsafe_allow_html=True)
-
-    bsd_more_info = st.expander(label="Expand to get more information on data and training report")
-    with bsd_more_info:
-        st.markdown(
-            "<h3 style='text-align: left; color:#F63366; font-size:12px;'><b>Training Dataset<b></h3>",
-            unsafe_allow_html=True)
-        st.write("The corpus has total 20,000 Japanese-English Business Dialog pairs. The fined-tuned Transformer model is validated on 670 Japanese-English Business Dialog pairs")
-
-        st.markdown(
-            "<h3 style='text-align: left; color:#F63366; font-size:12px;'><b>Training Report<b></h3>",
-            unsafe_allow_html=True)
-        st.write(
-            "The Dashboard for training result on Tensorboard is [here](https://tensorboard.dev/experiment/eWhxt1i2RuaU64krYtORhw/)")
-
-    with open("./BSD_ja-en_val.json", encoding='utf-8') as f:
-        bsd_sample_data = json.load(f)
-
-    en, ja = [], []
-    for i in range(len(bsd_sample_data)):
-        for j in range(len(bsd_sample_data[i]['conversation'])):
-            en.append(bsd_sample_data[i]['conversation'][j]['en_sentence'])
-            ja.append(bsd_sample_data[i]['conversation'][j]['ja_sentence'])
-
-    df = pd.DataFrame.from_dict({'Japanese': ja, 'English': en})
-    gb = GridOptionsBuilder.from_dataframe(df)
-    gb.configure_pagination()
-    gb.configure_selection(selection_mode="single", use_checkbox=True, suppressRowDeselection=False)
-    gridOptions = gb.build()
-    translation_text = AgGrid(df, gridOptions=gridOptions, theme='material',
-                              enable_enterprise_modules=True,
-                              allow_unsafe_jscode=True, update_mode=GridUpdateMode.SELECTION_CHANGED)
-    if len(translation_text['selected_rows']) != 0:
-        bsd_jp = translation_text['selected_rows'][0]['Japanese']
-        st.markdown(
-            "<h2 style='text-align: left; color:#32CD32; font-size:25px;'><b>Business Scene Dialog in Japanese (日本語でのビジネスシーンダイアログ)<b></h2>",
-            unsafe_allow_html=True)
-        st.write(bsd_jp)
-
-        if st.button("Translate"):
-            ja_tokenizer, en_tokenizer = getJpEn_Tokenizers()
-            trained_model = loadFineTunedJaEn_NMT_Model()
-            trained_model.freeze()
-
-
-            def translate(text):
-                text_encoding = ja_tokenizer(
-                    text,
-                    max_length=100,
-                    padding="max_length",
-                    truncation=True,
-                    return_attention_mask=True,
-                    add_special_tokens=True,
-                    return_tensors='pt'
-                )
-
-                generated_ids = trained_model.model.generate(
-                    input_ids=text_encoding['input_ids'],
-                    attention_mask=text_encoding['attention_mask'],
-                    max_length=100,
-                    num_beams=2,
-                    repetition_penalty=2.5,
-                    length_penalty=1.0,
-                    early_stopping=True
-                )
-
-                preds = [en_tokenizer.decode(gen_id, skip_special_tokens=True, clean_up_tokenization_spaces=True) for
-                         gen_id in generated_ids]
-
-                return "".join(preds)[5:]
-
-
-            st.markdown(
-                "<h2 style='text-align: left; color:#32CD32; font-size:25px;'><b>Translated Dialog in English (英語の翻訳されたダイアログ)<b></h2>",
-                unsafe_allow_html=True)
-            st.write(translate(bsd_jp))
+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,AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+import numpy as np
+import json
+from transformers import AutoTokenizer, BertTokenizer, AutoModelWithLMHead
+import pytorch_lightning as pl
+from pathlib import Path
+
+# Defining some functions for caching purpose by streamlit
+class TranslationModel(pl.LightningModule):
+    def __init__(self):
+        super().__init__()
+        self.model = AutoModelWithLMHead.from_pretrained("Helsinki-NLP/opus-mt-ja-en", return_dict=True)
+
+
+@st.experimental_singleton
+def loadFineTunedJaEn_NMT_Model():
+    save_dest = Path('model')
+    save_dest.mkdir(exist_ok=True)
+    st.write("Creating new folder for downloading the Japanese to English Translation Model. ")
+    f_checkpoint = Path("model/best-checkpoint.ckpt")
+    st.write("'Folder: model/best-checkpoint.ckpt' created.")
+    if not f_checkpoint.exists():
+        with st.spinner("Downloading model.This may take a while! \n Don't refresh or close this page!"):
+            from GD_download import download_file_from_google_drive
+            download_file_from_google_drive('1CZQKGj9hSqj7kEuJp_jm7bNVXrbcFsgP', f_checkpoint)
+
+    trained_model = TranslationModel.load_from_checkpoint(f_checkpoint)
+
+    return trained_model
+
+@st.experimental_singleton
+def getJpEn_Tokenizers():
+    try:
+        with st.spinner("Downloading English and Japanese Transformer Tokenizers"):
+            ja_tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-ja-en")
+            en_tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+    except:
+        st.error("Issue with downloading tokenizers")
+
+    return ja_tokenizer, en_tokenizer
+
+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","Japanese to English Translation"])
+
+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_val.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)
+elif topic == "Japanese to English Translation":
+    st.markdown(
+        "<h2 style='text-align: left; color:#EE82EE; font-size:25px;'><b>Japanese to English translation (for short sentences)<b></h2>",
+        unsafe_allow_html=True)
+    st.markdown(
+        "<h3 style='text-align: center; color:#F63366; font-size:18px;'><b>Business Scene Dialog Japanese-English Corpus<b></h3>",
+        unsafe_allow_html=True)
+
+    st.write("Below given Japanese-English pair is from 'Business Scene Dialog Corpus' by the University of Tokyo")
+    link = '[Corpus GitHub Link](https://github.com/tsuruoka-lab/BSD)'
+    st.markdown(link, unsafe_allow_html=True)
+
+    bsd_more_info = st.expander(label="Expand to get more information on data and training report")
+    with bsd_more_info:
+        st.markdown(
+            "<h3 style='text-align: left; color:#F63366; font-size:12px;'><b>Training Dataset<b></h3>",
+            unsafe_allow_html=True)
+        st.write("The corpus has total 20,000 Japanese-English Business Dialog pairs. The fined-tuned Transformer model is validated on 670 Japanese-English Business Dialog pairs")
+
+        st.markdown(
+            "<h3 style='text-align: left; color:#F63366; font-size:12px;'><b>Training Report<b></h3>",
+            unsafe_allow_html=True)
+        st.write(
+            "The Dashboard for training result on Tensorboard is [here](https://tensorboard.dev/experiment/eWhxt1i2RuaU64krYtORhw/)")
+
+    with open("./BSD_ja-en_val.json", encoding='utf-8') as f:
+        bsd_sample_data = json.load(f)
+
+    en, ja = [], []
+    for i in range(len(bsd_sample_data)):
+        for j in range(len(bsd_sample_data[i]['conversation'])):
+            en.append(bsd_sample_data[i]['conversation'][j]['en_sentence'])
+            ja.append(bsd_sample_data[i]['conversation'][j]['ja_sentence'])
+
+    df = pd.DataFrame.from_dict({'Japanese': ja, 'English': en})
+    gb = GridOptionsBuilder.from_dataframe(df)
+    gb.configure_pagination()
+    gb.configure_selection(selection_mode="single", use_checkbox=True, suppressRowDeselection=False)
+    gridOptions = gb.build()
+    translation_text = AgGrid(df, gridOptions=gridOptions, theme='material',
+                              enable_enterprise_modules=True,
+                              allow_unsafe_jscode=True, update_mode=GridUpdateMode.SELECTION_CHANGED)
+    if len(translation_text['selected_rows']) != 0:
+        bsd_jp = translation_text['selected_rows'][0]['Japanese']
+        st.markdown(
+            "<h2 style='text-align: left; color:#32CD32; font-size:25px;'><b>Business Scene Dialog in Japanese (日本語でのビジネスシーンダイアログ)<b></h2>",
+            unsafe_allow_html=True)
+        st.write(bsd_jp)
+
+        if st.button("Translate"):
+            ja_tokenizer, en_tokenizer = getJpEn_Tokenizers()
+            trained_model = loadFineTunedJaEn_NMT_Model()
+            trained_model.freeze()
+
+
+            def translate(text):
+                text_encoding = ja_tokenizer(
+                    text,
+                    max_length=100,
+                    padding="max_length",
+                    truncation=True,
+                    return_attention_mask=True,
+                    add_special_tokens=True,
+                    return_tensors='pt'
+                )
+
+                generated_ids = trained_model.model.generate(
+                    input_ids=text_encoding['input_ids'],
+                    attention_mask=text_encoding['attention_mask'],
+                    max_length=100,
+                    num_beams=2,
+                    repetition_penalty=2.5,
+                    length_penalty=1.0,
+                    early_stopping=True
+                )
+
+                preds = [en_tokenizer.decode(gen_id, skip_special_tokens=True, clean_up_tokenization_spaces=True) for
+                         gen_id in generated_ids]
+
+                return "".join(preds)[5:]
+
+
+            st.markdown(
+                "<h2 style='text-align: left; color:#32CD32; font-size:25px;'><b>Translated Dialog in English (英語の翻訳されたダイアログ)<b></h2>",
+                unsafe_allow_html=True)
+            st.write(translate(bsd_jp))