Create 6 Keywords Stem.py

pages/6 Keywords Stem.py

@@ -0,0 +1,231 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import re
+import nltk
+nltk.download('wordnet')
+from nltk.stem import WordNetLemmatizer
+nltk.download('stopwords')
+from nltk.corpus import stopwords
+from pprint import pprint
+import pickle
+import streamlit.components.v1 as components
+from io import StringIO
+from nltk.stem.snowball import SnowballStemmer
+import csv
+import sys
+
+#===config===
+st.set_page_config(
+    page_title="Coconut",
+    page_icon="🥥",
+    layout="wide",
+    initial_sidebar_state="collapsed"
+)
+
+hide_streamlit_style = """
+            <style>
+            #MainMenu 
+            {visibility: hidden;}
+            footer {visibility: hidden;}
+            [data-testid="collapsedControl"] {display: none}
+            </style>
+            """
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)
+
+with st.popover("🔗 Menu"):
+    st.page_link("Home.py", label="Home", icon="🏠")
+    st.page_link("pages/1 Scattertext.py", label="Scattertext", icon="1️⃣")
+    st.page_link("pages/2 Topic Modeling.py", label="Topic Modeling", icon="2️⃣")
+    st.page_link("pages/3 Bidirected Network.py", label="Bidirected Network", icon="3️⃣")
+    st.page_link("pages/4 Sunburst.py", label="Sunburst", icon="4️⃣")
+    st.page_link("pages/5 Burst Detection.py", label="Burst Detection", icon="5️⃣")
+    st.page_link("pages/6 Keywords Stem.py", label="Keywords Stem", icon="6️⃣")
+    
+
+st.header("Keywords Stem", anchor=False)
+st.subheader('Put your file here...', anchor=False)
+
+def reset_data():
+     st.cache_data.clear()
+
+#===check filetype===
+@st.cache_data(ttl=3600)
+def get_ext(extype):
+    extype = uploaded_file.name
+    return extype
+     
+#===upload===
+@st.cache_data(ttl=3600)
+def upload(extype):
+    keywords = pd.read_csv(uploaded_file)
+    return keywords
+
+@st.cache_data(ttl=3600)
+def conv_txt(extype):
+    col_dict = {'TI': 'Title',
+            'SO': 'Source title',
+            'DE': 'Author Keywords',
+            'ID': 'Keywords Plus'}
+    keywords = pd.read_csv(uploaded_file, sep='\t', lineterminator='\r')
+    keywords.rename(columns=col_dict, inplace=True)
+    return keywords
+
+@st.cache_data(ttl=3600)
+def rev_conv_txt(extype):
+    col_dict_rev = {'Title': 'TI',
+            'Source title': 'SO',
+            'Author Keywords': 'DE',
+            'Keywords Plus': 'ID'}
+    keywords.rename(columns=col_dict_rev, inplace=True)
+    return keywords
+
+@st.cache_data(ttl=3600)
+def get_data(extype):
+    list_of_column_key = list(keywords.columns)
+    list_of_column_key = [k for k in list_of_column_key if 'Keyword' in k]
+    return list_of_column_key
+
+uploaded_file = st.file_uploader('', type=['csv','txt'], on_change=reset_data)
+
+if uploaded_file is not None:
+     extype = get_ext(uploaded_file)
+     if extype.endswith('.csv'):
+         keywords = upload(extype) 
+                  
+     elif extype.endswith('.txt'):
+         keywords = conv_txt(extype)
+         
+     list_of_column_key = get_data(extype)
+
+     col1, col2 = st.columns(2)
+     with col1:
+        method = st.selectbox(
+             'Choose method',
+           ('Lemmatization', 'Stemming'), on_change=reset_data)
+     with col2:
+        keyword = st.selectbox(
+            'Choose column',
+           (list_of_column_key), on_change=reset_data)
+
+     @st.cache_data(ttl=3600)
+     def clean_keyword(extype):      
+        global keyword, keywords
+        try:
+            key = keywords[keyword]
+        except KeyError:
+            st.error('Error: Please check your Author/Index Keywords column.')
+            sys.exit(1)
+        keywords = keywords.replace(np.nan, '', regex=True)
+        keywords[keyword] = keywords[keyword].astype(str)
+        keywords[keyword] = keywords[keyword].map(lambda x: re.sub('-', ' ', x))
+        keywords[keyword] = keywords[keyword].map(lambda x: re.sub('; ', ' ; ', x))
+        keywords[keyword] = keywords[keyword].map(lambda x: x.lower())
+        
+        #===Keywords list===
+        key = key.dropna()
+        key = pd.concat([key.str.split('; ', expand=True)], axis=1)
+        key = pd.Series(np.ravel(key)).dropna().drop_duplicates().sort_values().reset_index()
+        key[0] = key[0].map(lambda x: re.sub('-', ' ', x))
+        key['new']=key[0].map(lambda x: x.lower())
+
+        return keywords, key
+     
+     #===stem/lem===
+     @st.cache_data(ttl=3600)
+     def Lemmatization(extype):
+        lemmatizer = WordNetLemmatizer()
+        def lemmatize_words(text):
+            words = text.split()
+            words = [lemmatizer.lemmatize(word) for word in words]
+            return ' '.join(words)
+        keywords[keyword] = keywords[keyword].apply(lemmatize_words)
+        key['new'] = key['new'].apply(lemmatize_words)
+        keywords[keyword] = keywords[keyword].map(lambda x: re.sub(' ; ', '; ', x))
+        return keywords, key
+                
+     @st.cache_data(ttl=3600)
+     def Stemming(extype):
+        stemmer = SnowballStemmer("english")
+        def stem_words(text):
+            words = text.split()
+            words = [stemmer.stem(word) for word in words]
+            return ' '.join(words)
+        keywords[keyword] = keywords[keyword].apply(stem_words)
+        key['new'] = key['new'].apply(stem_words)
+        keywords[keyword] = keywords[keyword].map(lambda x: re.sub(' ; ', '; ', x))
+        return keywords, key
+     
+     keywords, key = clean_keyword(extype) 
+     
+     if method is 'Lemmatization':
+         keywords, key = Lemmatization(extype)
+     else:
+         keywords, key = Stemming(extype)
+            
+     st.write('Congratulations! 🤩 You choose',keyword ,'with',method,'method. Now, you can easily download the result by clicking the button below')
+     st.divider()
+          
+     #===show & download csv===
+     tab1, tab2, tab3, tab4 = st.tabs(["📥 Result", "📥 List of Keywords", "📃 Reference", "📃 Recommended Reading"])
+     
+     with tab1:
+         st.dataframe(keywords, use_container_width=True, hide_index=True)
+         @st.cache_data(ttl=3600)
+         def convert_df(extype):
+            return keywords.to_csv(index=False).encode('utf-8')
+         
+         @st.cache_data(ttl=3600)
+         def convert_txt(extype):
+             return keywords.to_csv(index=False, sep='\t', lineterminator='\r').encode('utf-8')
+         
+         if extype.endswith('.csv'):
+             csv = convert_df(extype)
+             st.download_button(
+                "Press to download result 👈",
+                csv,
+                "scopus.csv",
+                "text/csv")
+  
+         elif extype.endswith('.txt'):
+             keywords = rev_conv_txt(extype)
+             txt = convert_txt(extype)
+             st.download_button(
+                "Press to download result 👈",
+                txt,
+                "savedrecs.txt",
+                "text/csv")    
+         
+     with tab2:
+         @st.cache_data(ttl=3600)
+         def table_keyword(extype):
+             keytab = key.drop(['index'], axis=1).rename(columns={0: 'label'})
+             return keytab
+         #===coloring the same keywords===
+         @st.cache_data(ttl=3600)
+         def highlight_cells(value):
+             if keytab['new'].duplicated(keep=False).any() and keytab['new'].duplicated(keep=False)[keytab['new'] == value].any():
+                 return 'background-color: yellow'
+             return '' 
+         keytab = table_keyword(extype) 
+         st.dataframe(keytab.style.applymap(highlight_cells, subset=['new']), use_container_width=True, hide_index=True)
+                  
+         @st.cache_data(ttl=3600)
+         def convert_dfs(extype):
+             return key.to_csv(index=False).encode('utf-8')
+                
+         csv = convert_dfs(extype)
+
+         st.download_button(
+             "Press to download keywords 👈",
+             csv,
+             "keywords.csv",
+             "text/csv")
+             
+     with tab3:
+         st.markdown('**Santosa, F. A. (2023). Prior steps into knowledge mapping: Text mining application and comparison. Issues in Science and Technology Librarianship, 102.** https://doi.org/10.29173/istl2736')
+     
+     with tab4:
+         st.markdown('**Beri, A. (2021, January 27). Stemming vs Lemmatization. Medium.** https://towardsdatascience.com/stemming-vs-lemmatization-2daddabcb221')
+         st.markdown('**Khyani, D., Siddhartha B S, Niveditha N M, &amp; Divya B M. (2020). An Interpretation of Lemmatization and Stemming in Natural Language Processing. Journal of University of Shanghai for Science and Technology , 22(10), 350–357.**  https://jusst.org/an-interpretation-of-lemmatization-and-stemming-in-natural-language-processing/')
+         st.markdown('**Lamba, M., & Madhusudhan, M. (2021, July 31). Text Pre-Processing. Text Mining for Information Professionals, 79–103.** https://doi.org/10.1007/978-3-030-85085-2_3')