Update pages/6 Keywords Stem.py

pages/6 Keywords Stem.py

@@ -89,143 +89,150 @@ def get_data(extype):
 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)
+    try:
+        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())
             
-     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')
+            #===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
          
-         @st.cache_data(ttl=3600)
-         def convert_txt(extype):
-             return keywords.to_csv(index=False, sep='\t', lineterminator='\r').encode('utf-8')
+        #===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
          
-         if extype.endswith('.csv'):
-             csv = convert_df(extype)
-             st.download_button(
-                "Press to download result 👈",
+        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,
-                "scopus.csv",
+                "keywords.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 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 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)
+        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, & 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')
 
-         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, & 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')
+
+    except:
+        st.error("Please ensure that your file is correct. Please contact us if you find that this is an error.", icon="🚨")
+        st.stop()