Upload app.py

app.py

@@ -0,0 +1,172 @@
+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": jp_review_text,'NEGATIVE': scores[0][0], 'POSITIVE': scores[0][1]}
+
+                result_col,graph_col = st.columns(2)
+                with result_col:
+                    st.write(result)
+                with graph_col:
+                    fig = px.bar(x=['NEGATIVE','POSITIVE'],y=[result['NEGATIVE'],result['POSITIVE']])
+                    fig.update_layout(title="Probability distribution of Sentiment for the given text",\
+                                      yaxis_title="Probability")
+                    fig.update_traces(marker_color=['#FF7F7F','#32CD32'])
+                    st.plotly_chart(fig)
+