app.py

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"])

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')))

                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)