Update pages/2 Topic Modeling.py

pages/2 Topic Modeling.py

@@ -87,14 +87,14 @@ def get_ext(uploaded_file):
 #===clear cache===
 
 def reset_biterm():
-     try:
-          biterm_map.clear()
-          biterm_bar.clear()
-     except NameError:
-          biterm_topic.clear()
+    try:
+        biterm_map.clear()
+        biterm_bar.clear()
+    except NameError:
+        biterm_topic.clear()
 
 def reset_all():
-     st.cache_data.clear()
+    st.cache_data.clear()
 
 #===avoiding deadlock===
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
@@ -121,321 +121,325 @@ def conv_txt(extype):
 uploaded_file = st.file_uploader('', type=['csv', 'txt'], on_change=reset_all)
 
 if uploaded_file is not None:
-    extype = get_ext(uploaded_file)
-
-    if extype.endswith('.csv'):
-         papers = upload(extype) 
-    elif extype.endswith('.txt'):
-         papers = conv_txt(extype)
-
-    coldf = sorted(papers.select_dtypes(include=['object']).columns.tolist())
-        
-    c1, c2 = st.columns([3,4])
-    method = c1.selectbox(
-            'Choose method',
-            ('Choose...', 'pyLDA', 'Biterm', 'BERTopic'), on_change=reset_all)
-    num_cho = c1.number_input('Choose number of topics', min_value=2, max_value=30, value=5)
-    ColCho = c2.selectbox(
-            'Choose column',
-            (coldf), on_change=reset_all)
-    words_to_remove = c2.text_input("Remove specific words. Separate words by semicolons (;)")
-    rem_copyright = c1.toggle('Remove copyright statement', value=True, on_change=reset_all)
-    rem_punc = c2.toggle('Remove punctuation', value=True, on_change=reset_all)
-     
-    #===clean csv===
-    @st.cache_data(ttl=3600, show_spinner=False)
-    def clean_csv(extype):
-        paper = papers.dropna(subset=[ColCho])
-                 
-        #===mapping===
-        paper['Abstract_pre'] = paper[ColCho].map(lambda x: x.lower())
-        if rem_punc:
-             paper['Abstract_pre'] = paper['Abstract_pre'].map(lambda x: re.sub('[,:;\.!-?•=]', ' ', x))
-             paper['Abstract_pre'] = paper['Abstract_pre'].str.replace('\u201c|\u201d', '', regex=True) 
-        if rem_copyright:  
-             paper['Abstract_pre'] = paper['Abstract_pre'].map(lambda x: re.sub('©.*', '', x))
+    try:
+        extype = get_ext(uploaded_file)
+    
+        if extype.endswith('.csv'):
+             papers = upload(extype) 
+        elif extype.endswith('.txt'):
+             papers = conv_txt(extype)
+    
+        coldf = sorted(papers.select_dtypes(include=['object']).columns.tolist())
+            
+        c1, c2 = st.columns([3,4])
+        method = c1.selectbox(
+                'Choose method',
+                ('Choose...', 'pyLDA', 'Biterm', 'BERTopic'), on_change=reset_all)
+        num_cho = c1.number_input('Choose number of topics', min_value=2, max_value=30, value=5)
+        ColCho = c2.selectbox(
+                'Choose column',
+                (coldf), on_change=reset_all)
+        words_to_remove = c2.text_input("Remove specific words. Separate words by semicolons (;)")
+        rem_copyright = c1.toggle('Remove copyright statement', value=True, on_change=reset_all)
+        rem_punc = c2.toggle('Remove punctuation', value=True, on_change=reset_all)
+         
+        #===clean csv===
+        @st.cache_data(ttl=3600, show_spinner=False)
+        def clean_csv(extype):
+            paper = papers.dropna(subset=[ColCho])
+                     
+            #===mapping===
+            paper['Abstract_pre'] = paper[ColCho].map(lambda x: x.lower())
+            if rem_punc:
+                 paper['Abstract_pre'] = paper['Abstract_pre'].map(lambda x: re.sub('[,:;\.!-?•=]', ' ', x))
+                 paper['Abstract_pre'] = paper['Abstract_pre'].str.replace('\u201c|\u201d', '', regex=True) 
+            if rem_copyright:  
+                 paper['Abstract_pre'] = paper['Abstract_pre'].map(lambda x: re.sub('©.*', '', x))
+            
+            #===stopword removal===
+            stop = stopwords.words('english')
+            paper['Abstract_stop'] = paper['Abstract_pre'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)]))
+              
+            #===lemmatize===
+            lemmatizer = WordNetLemmatizer()
+            def lemmatize_words(text):
+                words = text.split()
+                words = [lemmatizer.lemmatize(word) for word in words]
+                return ' '.join(words)
+            paper['Abstract_lem'] = paper['Abstract_stop'].apply(lemmatize_words)
         
-        #===stopword removal===
-        stop = stopwords.words('english')
-        paper['Abstract_stop'] = paper['Abstract_pre'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)]))
-          
-        #===lemmatize===
-        lemmatizer = WordNetLemmatizer()
-        def lemmatize_words(text):
-            words = text.split()
-            words = [lemmatizer.lemmatize(word) for word in words]
-            return ' '.join(words)
-        paper['Abstract_lem'] = paper['Abstract_stop'].apply(lemmatize_words)
+            words_rmv = [word.strip() for word in words_to_remove.split(";")]
+            remove_dict = {word: None for word in words_rmv}
+            def remove_words(text):
+                 words = text.split()
+                 cleaned_words = [word for word in words if word not in remove_dict]
+                 return ' '.join(cleaned_words) 
+            paper['Abstract_lem'] = paper['Abstract_lem'].map(remove_words)
+             
+            topic_abs = paper.Abstract_lem.values.tolist()
+            return topic_abs, paper
+    
+        d1, d2 = st.columns([7,3]) 
+        d2.info("Don't do anything during the computing", icon="⚠️")
+        topic_abs, paper=clean_csv(extype) 
+    
+        #===advance settings===
+        with d1.expander("🧮 Show advance settings"): 
+             t1, t2 = st.columns([5,5])
+             if method == 'pyLDA':
+                  py_random_state = t1.number_input('Random state', min_value=0, max_value=None, step=1)
+                  py_chunksize = t2.number_input('Chunk size', value=100 , min_value=10, max_value=None, step=1)
+             elif method == 'Biterm':
+                  btm_seed = t1.number_input('Random state seed', value=100 , min_value=1, max_value=None, step=1)
+                  btm_iterations = t2.number_input('Iterations number', value=20 , min_value=2, max_value=None, step=1)
+             elif method == 'BERTopic':
+                  bert_top_n_words = t1.number_input('top_n_words', value=5 , min_value=5, max_value=25, step=1)
+                  bert_random_state = t1.number_input('random_state', value=42 , min_value=1, max_value=None, step=1)
+                  bert_n_components = t2.number_input('n_components', value=5 , min_value=1, max_value=None, step=1)
+                  bert_n_neighbors = t2.number_input('n_neighbors', value=15 , min_value=1, max_value=None, step=1)
+                  bert_embedding_model = st.radio(
+                       "embedding_model", 
+                       ["all-MiniLM-L6-v2", "paraphrase-multilingual-MiniLM-L12-v2", "en_core_web_md"], index=0, horizontal=True)
+             else:
+                  st.write('Please choose your preferred method')
+        if st.button("Submit", on_click=reset_all):
+             num_topic = num_cho  
+    
+        if method == 'BERTopic':
+            st.info('BERTopic is an expensive process when dealing with a large volume of text with our existing resources. Please kindly wait until the visualization appears.', icon="ℹ️")
+               
+        #===topic===
+        if method == 'Choose...':
+            st.write('')
+    
+        elif method == 'pyLDA':       
+            tab1, tab2, tab3 = st.tabs(["📈 Generate visualization", "📃 Reference", "📓 Recommended Reading"])
     
-        words_rmv = [word.strip() for word in words_to_remove.split(";")]
-        remove_dict = {word: None for word in words_rmv}
-        def remove_words(text):
-             words = text.split()
-             cleaned_words = [word for word in words if word not in remove_dict]
-             return ' '.join(cleaned_words) 
-        paper['Abstract_lem'] = paper['Abstract_lem'].map(remove_words)
+            with tab1:
+            #===visualization===
+                @st.cache_data(ttl=3600, show_spinner=False)
+                def pylda(extype):
+                    topic_abs_LDA = [t.split(' ') for t in topic_abs]
+                    id2word = Dictionary(topic_abs_LDA)
+                    corpus = [id2word.doc2bow(text) for text in topic_abs_LDA]
+                    #===LDA===
+                    lda_model = LdaModel(corpus=corpus,
+                                id2word=id2word,
+                                num_topics=num_topic, 
+                                random_state=py_random_state,
+                                chunksize=py_chunksize,
+                                alpha='auto',
+                                per_word_topics=True)
          
-        topic_abs = paper.Abstract_lem.values.tolist()
-        return topic_abs, paper
-
-    d1, d2 = st.columns([7,3]) 
-    d2.info("Don't do anything during the computing", icon="⚠️")
-    topic_abs, paper=clean_csv(extype) 
-
-    #===advance settings===
-    with d1.expander("🧮 Show advance settings"): 
-         t1, t2 = st.columns([5,5])
-         if method == 'pyLDA':
-              py_random_state = t1.number_input('Random state', min_value=0, max_value=None, step=1)
-              py_chunksize = t2.number_input('Chunk size', value=100 , min_value=10, max_value=None, step=1)
-         elif method == 'Biterm':
-              btm_seed = t1.number_input('Random state seed', value=100 , min_value=1, max_value=None, step=1)
-              btm_iterations = t2.number_input('Iterations number', value=20 , min_value=2, max_value=None, step=1)
-         elif method == 'BERTopic':
-              bert_top_n_words = t1.number_input('top_n_words', value=5 , min_value=5, max_value=25, step=1)
-              bert_random_state = t1.number_input('random_state', value=42 , min_value=1, max_value=None, step=1)
-              bert_n_components = t2.number_input('n_components', value=5 , min_value=1, max_value=None, step=1)
-              bert_n_neighbors = t2.number_input('n_neighbors', value=15 , min_value=1, max_value=None, step=1)
-              bert_embedding_model = st.radio(
-                   "embedding_model", 
-                   ["all-MiniLM-L6-v2", "paraphrase-multilingual-MiniLM-L12-v2", "en_core_web_md"], index=0, horizontal=True)
-         else:
-              st.write('Please choose your preferred method')
-    if st.button("Submit", on_click=reset_all):
-         num_topic = num_cho  
-
-    if method == 'BERTopic':
-        st.info('BERTopic is an expensive process when dealing with a large volume of text with our existing resources. Please kindly wait until the visualization appears.', icon="ℹ️")
-           
-    #===topic===
-    if method == 'Choose...':
-        st.write('')
-
-    elif method == 'pyLDA':       
-         tab1, tab2, tab3 = st.tabs(["📈 Generate visualization", "📃 Reference", "📓 Recommended Reading"])
-
-         with tab1:
-         #===visualization===
-              @st.cache_data(ttl=3600, show_spinner=False)
-              def pylda(extype):
-                 topic_abs_LDA = [t.split(' ') for t in topic_abs]
-                 id2word = Dictionary(topic_abs_LDA)
-                 corpus = [id2word.doc2bow(text) for text in topic_abs_LDA]
-                 #===LDA===
-                 lda_model = LdaModel(corpus=corpus,
-                             id2word=id2word,
-                             num_topics=num_topic, 
-                             random_state=py_random_state,
-                             chunksize=py_chunksize,
-                             alpha='auto',
-                             per_word_topics=True)
-     
-                 pprint(lda_model.print_topics())
-                 doc_lda = lda_model[corpus]
-     
-                 #===visualization===
-                 coherence_model_lda = CoherenceModel(model=lda_model, texts=topic_abs_LDA, dictionary=id2word, coherence='c_v')
-                 coherence_lda = coherence_model_lda.get_coherence()
-                 vis = pyLDAvis.gensim_models.prepare(lda_model, corpus, id2word)
-                 py_lda_vis_html = pyLDAvis.prepared_data_to_html(vis)
-                 return py_lda_vis_html, coherence_lda, vis
-                   
-              with st.spinner('Performing computations. Please wait ...'):
-                   try:
+                    pprint(lda_model.print_topics())
+                    doc_lda = lda_model[corpus]
+         
+                    #===visualization===
+                    coherence_model_lda = CoherenceModel(model=lda_model, texts=topic_abs_LDA, dictionary=id2word, coherence='c_v')
+                    coherence_lda = coherence_model_lda.get_coherence()
+                    vis = pyLDAvis.gensim_models.prepare(lda_model, corpus, id2word)
+                    py_lda_vis_html = pyLDAvis.prepared_data_to_html(vis)
+                    return py_lda_vis_html, coherence_lda, vis
+                       
+                with st.spinner('Performing computations. Please wait ...'):
+                    try:
                         py_lda_vis_html, coherence_lda, vis = pylda(extype)
                         st.write('Coherence score: ', coherence_lda)
                         components.html(py_lda_vis_html, width=1500, height=800)
                         st.markdown('Copyright (c) 2015, Ben Mabey. https://github.com/bmabey/pyLDAvis')
-                       
+                           
                         @st.cache_data(ttl=3600, show_spinner=False)
                         def img_lda(vis):
-                             pyLDAvis.save_html(vis, 'output.html')
-                             hti = Html2Image()
-                             hti.browser.flags = ['--default-background-color=ffffff', '--hide-scrollbars']
-                             css = "body {background: white;}"
-                             hti.screenshot(
-                                  other_file='output.html', css_str=css, size=(1500, 800),
-                                  save_as='ldavis_img.png'
-                             )
-                             
+                            pyLDAvis.save_html(vis, 'output.html')
+                            hti = Html2Image()
+                            hti.browser.flags = ['--default-background-color=ffffff', '--hide-scrollbars']
+                            css = "body {background: white;}"
+                            hti.screenshot(
+                                other_file='output.html', css_str=css, size=(1500, 800),
+                                save_as='ldavis_img.png'
+                            )
+                                 
                         img_lda(vis)   
                         with open("ldavis_img.png", "rb") as file:
-                              btn = st.download_button(
-                                  label="Download image",
-                                  data=file,
-                                  file_name="ldavis_img.png",
-                                  mime="image/png"
-                                  )
-                       
-                   except NameError:
+                            btn = st.download_button(
+                                label="Download image",
+                                data=file,
+                                file_name="ldavis_img.png",
+                                mime="image/png"
+                                )
+                           
+                    except NameError:
                         st.warning('🖱️ Please click Submit')
-
-         with tab2:
-             st.markdown('**Sievert, C., & Shirley, K. (2014). LDAvis: A method for visualizing and interpreting topics. Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces.** https://doi.org/10.3115/v1/w14-3110')
-
-         with tab3:
-             st.markdown('**Chen, X., & Wang, H. (2019, January). Automated chat transcript analysis using topic modeling for library reference services. Proceedings of the Association for Information Science and Technology, 56(1), 368–371.** https://doi.org/10.1002/pra2.31')
-             st.markdown('**Joo, S., Ingram, E., & Cahill, M. (2021, December 15). Exploring Topics and Genres in Storytime Books: A Text Mining Approach. Evidence Based Library and Information Practice, 16(4), 41–62.** https://doi.org/10.18438/eblip29963')
-             st.markdown('**Lamba, M., & Madhusudhan, M. (2021, July 31). Topic Modeling. Text Mining for Information Professionals, 105–137.** https://doi.org/10.1007/978-3-030-85085-2_4')
-             st.markdown('**Lamba, M., & Madhusudhan, M. (2019, June 7). Mapping of topics in DESIDOC Journal of Library and Information Technology, India: a study. Scientometrics, 120(2), 477–505.** https://doi.org/10.1007/s11192-019-03137-5')
-     
-     #===Biterm===
-    elif method == 'Biterm':            
-             
-        #===optimize Biterm===
-        @st.cache_data(ttl=3600, show_spinner=False)
-        def biterm_topic(extype):
-            X, vocabulary, vocab_dict = btm.get_words_freqs(topic_abs)
-            tf = np.array(X.sum(axis=0)).ravel()
-            docs_vec = btm.get_vectorized_docs(topic_abs, vocabulary)
-            docs_lens = list(map(len, docs_vec))
-            biterms = btm.get_biterms(docs_vec)
-            model = btm.BTM(
-              X, vocabulary, seed=btm_seed, T=num_topic, M=20, alpha=50/8, beta=0.01)
-            model.fit(biterms, iterations=btm_iterations)
-            p_zd = model.transform(docs_vec)
-            coherence = model.coherence_
-            phi = tmp.get_phi(model)
-            topics_coords = tmp.prepare_coords(model)
-            totaltop = topics_coords.label.values.tolist()
-            perplexity = model.perplexity_
-            return topics_coords, phi, totaltop, perplexity
-
-        tab1, tab2, tab3 = st.tabs(["📈 Generate visualization", "📃 Reference", "📓 Recommended Reading"])
-        with tab1:
-             try:
-               with st.spinner('Performing computations. Please wait ...'): 
-                    topics_coords, phi, totaltop, perplexity = biterm_topic(extype)            
-                    col1, col2 = st.columns([4,6])
-                  
-                    @st.cache_data(ttl=3600)
-                    def biterm_map(extype):
-                         btmvis_coords = tmp.plot_scatter_topics(topics_coords, size_col='size', label_col='label', topic=numvis)
-                         return btmvis_coords
-                            
-                    @st.cache_data(ttl=3600)
-                    def biterm_bar(extype):
-                         terms_probs = tmp.calc_terms_probs_ratio(phi, topic=numvis, lambda_=1)
-                         btmvis_probs = tmp.plot_terms(terms_probs, font_size=12)
-                         return btmvis_probs
-                            
-                    with col1:
-                         st.write('Perplexity score: ', perplexity)
-                         st.write('')
-                         numvis = st.selectbox(
-                              'Choose topic',
-                              (totaltop), on_change=reset_biterm)
-                         btmvis_coords = biterm_map(extype)
-                         st.altair_chart(btmvis_coords)
-                    with col2:
-                         btmvis_probs = biterm_bar(extype)
-                         st.altair_chart(btmvis_probs, use_container_width=True)
-
-             except ValueError:
-                   st.error('🙇‍♂️ Please raise the number of topics and click submit')
-             except NameError:
-                   st.warning('🖱️ Please click Submit')
-
-        with tab2: 
-            st.markdown('**Yan, X., Guo, J., Lan, Y., & Cheng, X. (2013, May 13). A biterm topic model for short texts. Proceedings of the 22nd International Conference on World Wide Web.** https://doi.org/10.1145/2488388.2488514')
-        with tab3:
-            st.markdown('**Cai, M., Shah, N., Li, J., Chen, W. H., Cuomo, R. E., Obradovich, N., & Mackey, T. K. (2020, August 26). Identification and characterization of tweets related to the 2015 Indiana HIV outbreak: A retrospective infoveillance study. PLOS ONE, 15(8), e0235150.** https://doi.org/10.1371/journal.pone.0235150')
-            st.markdown('**Chen, Y., Dong, T., Ban, Q., & Li, Y. (2021). What Concerns Consumers about Hypertension? A Comparison between the Online Health Community and the Q&A Forum. International Journal of Computational Intelligence Systems, 14(1), 734.** https://doi.org/10.2991/ijcis.d.210203.002')
-            st.markdown('**George, Crissandra J., "AMBIGUOUS APPALACHIANNESS: A LINGUISTIC AND PERCEPTUAL INVESTIGATION INTO ARC-LABELED PENNSYLVANIA COUNTIES" (2022). Theses and Dissertations-- Linguistics. 48.** https://doi.org/10.13023/etd.2022.217')
-            st.markdown('**Li, J., Chen, W. H., Xu, Q., Shah, N., Kohler, J. C., & Mackey, T. K. (2020). Detection of self-reported experiences with corruption on twitter using unsupervised machine learning. Social Sciences & Humanities Open, 2(1), 100060.** https://doi.org/10.1016/j.ssaho.2020.100060')
-          
-     #===BERTopic===
-    elif method == 'BERTopic':
-        @st.cache_data(ttl=3600, show_spinner=False)
-        def bertopic_vis(extype):
-          umap_model = UMAP(n_neighbors=bert_n_neighbors, n_components=bert_n_components, 
-                  min_dist=0.0, metric='cosine', random_state=bert_random_state)   
-          cluster_model = KMeans(n_clusters=num_topic)
-          if bert_embedding_model == 'all-MiniLM-L6-v2':
-               emb_mod = 'all-MiniLM-L6-v2'
-               lang = 'en'
-          elif bert_embedding_model == 'en_core_web_md':
-               emb_mod = en_core_web_md.load(exclude=['tagger', 'parser', 'ner', 'attribute_ruler', 'lemmatizer'])
-               lang = 'en'
-          elif bert_embedding_model == 'paraphrase-multilingual-MiniLM-L12-v2':
-               emb_mod = 'paraphrase-multilingual-MiniLM-L12-v2'
-               lang = 'multilingual'
-          topic_model = BERTopic(embedding_model=emb_mod, hdbscan_model=cluster_model, language=lang, umap_model=umap_model, top_n_words=bert_top_n_words)
-          topics, probs = topic_model.fit_transform(topic_abs)
-          return topic_model, topics, probs
-        
-        @st.cache_data(ttl=3600, show_spinner=False)
-        def Vis_Topics(extype):
-          fig1 = topic_model.visualize_topics()
-          return fig1
+    
+            with tab2:
+                st.markdown('**Sievert, C., & Shirley, K. (2014). LDAvis: A method for visualizing and interpreting topics. Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces.** https://doi.org/10.3115/v1/w14-3110')
+    
+            with tab3:
+                st.markdown('**Chen, X., & Wang, H. (2019, January). Automated chat transcript analysis using topic modeling for library reference services. Proceedings of the Association for Information Science and Technology, 56(1), 368–371.** https://doi.org/10.1002/pra2.31')
+                st.markdown('**Joo, S., Ingram, E., & Cahill, M. (2021, December 15). Exploring Topics and Genres in Storytime Books: A Text Mining Approach. Evidence Based Library and Information Practice, 16(4), 41–62.** https://doi.org/10.18438/eblip29963')
+                st.markdown('**Lamba, M., & Madhusudhan, M. (2021, July 31). Topic Modeling. Text Mining for Information Professionals, 105–137.** https://doi.org/10.1007/978-3-030-85085-2_4')
+                st.markdown('**Lamba, M., & Madhusudhan, M. (2019, June 7). Mapping of topics in DESIDOC Journal of Library and Information Technology, India: a study. Scientometrics, 120(2), 477–505.** https://doi.org/10.1007/s11192-019-03137-5')
+         
+         #===Biterm===
+        elif method == 'Biterm':            
+                 
+            #===optimize Biterm===
+            @st.cache_data(ttl=3600, show_spinner=False)
+            def biterm_topic(extype):
+                X, vocabulary, vocab_dict = btm.get_words_freqs(topic_abs)
+                tf = np.array(X.sum(axis=0)).ravel()
+                docs_vec = btm.get_vectorized_docs(topic_abs, vocabulary)
+                docs_lens = list(map(len, docs_vec))
+                biterms = btm.get_biterms(docs_vec)
+                model = btm.BTM(X, vocabulary, seed=btm_seed, T=num_topic, M=20, alpha=50/8, beta=0.01)
+                model.fit(biterms, iterations=btm_iterations)
+                p_zd = model.transform(docs_vec)
+                coherence = model.coherence_
+                phi = tmp.get_phi(model)
+                topics_coords = tmp.prepare_coords(model)
+                totaltop = topics_coords.label.values.tolist()
+                perplexity = model.perplexity_
+                return topics_coords, phi, totaltop, perplexity
+    
+            tab1, tab2, tab3 = st.tabs(["📈 Generate visualization", "📃 Reference", "📓 Recommended Reading"])
+            with tab1:
+                try:
+                    with st.spinner('Performing computations. Please wait ...'): 
+                        topics_coords, phi, totaltop, perplexity = biterm_topic(extype)            
+                        col1, col2 = st.columns([4,6])
+                      
+                        @st.cache_data(ttl=3600)
+                        def biterm_map(extype):
+                            btmvis_coords = tmp.plot_scatter_topics(topics_coords, size_col='size', label_col='label', topic=numvis)
+                            return btmvis_coords
+                                
+                        @st.cache_data(ttl=3600)
+                        def biterm_bar(extype):
+                            terms_probs = tmp.calc_terms_probs_ratio(phi, topic=numvis, lambda_=1)
+                            btmvis_probs = tmp.plot_terms(terms_probs, font_size=12)
+                            return btmvis_probs
+                                
+                        with col1:
+                            st.write('Perplexity score: ', perplexity)
+                            st.write('')
+                            numvis = st.selectbox(
+                                'Choose topic',
+                                (totaltop), on_change=reset_biterm)
+                            btmvis_coords = biterm_map(extype)
+                            st.altair_chart(btmvis_coords)
+                        with col2:
+                            btmvis_probs = biterm_bar(extype)
+                            st.altair_chart(btmvis_probs, use_container_width=True)
+    
+                except ValueError:
+                    st.error('🙇‍♂️ Please raise the number of topics and click submit')
+                except NameError:
+                    st.warning('🖱️ Please click Submit')
+    
+            with tab2: 
+                st.markdown('**Yan, X., Guo, J., Lan, Y., & Cheng, X. (2013, May 13). A biterm topic model for short texts. Proceedings of the 22nd International Conference on World Wide Web.** https://doi.org/10.1145/2488388.2488514')
+            with tab3:
+                st.markdown('**Cai, M., Shah, N., Li, J., Chen, W. H., Cuomo, R. E., Obradovich, N., & Mackey, T. K. (2020, August 26). Identification and characterization of tweets related to the 2015 Indiana HIV outbreak: A retrospective infoveillance study. PLOS ONE, 15(8), e0235150.** https://doi.org/10.1371/journal.pone.0235150')
+                st.markdown('**Chen, Y., Dong, T., Ban, Q., & Li, Y. (2021). What Concerns Consumers about Hypertension? A Comparison between the Online Health Community and the Q&A Forum. International Journal of Computational Intelligence Systems, 14(1), 734.** https://doi.org/10.2991/ijcis.d.210203.002')
+                st.markdown('**George, Crissandra J., "AMBIGUOUS APPALACHIANNESS: A LINGUISTIC AND PERCEPTUAL INVESTIGATION INTO ARC-LABELED PENNSYLVANIA COUNTIES" (2022). Theses and Dissertations-- Linguistics. 48.** https://doi.org/10.13023/etd.2022.217')
+                st.markdown('**Li, J., Chen, W. H., Xu, Q., Shah, N., Kohler, J. C., & Mackey, T. K. (2020). Detection of self-reported experiences with corruption on twitter using unsupervised machine learning. Social Sciences & Humanities Open, 2(1), 100060.** https://doi.org/10.1016/j.ssaho.2020.100060')
+              
+         #===BERTopic===
+        elif method == 'BERTopic':
+            @st.cache_data(ttl=3600, show_spinner=False)
+            def bertopic_vis(extype):
+                umap_model = UMAP(n_neighbors=bert_n_neighbors, n_components=bert_n_components, 
+                    min_dist=0.0, metric='cosine', random_state=bert_random_state)   
+                cluster_model = KMeans(n_clusters=num_topic)
+                if bert_embedding_model == 'all-MiniLM-L6-v2':
+                    emb_mod = 'all-MiniLM-L6-v2'
+                    lang = 'en'
+                elif bert_embedding_model == 'en_core_web_md':
+                    emb_mod = en_core_web_md.load(exclude=['tagger', 'parser', 'ner', 'attribute_ruler', 'lemmatizer'])
+                    lang = 'en'
+                elif bert_embedding_model == 'paraphrase-multilingual-MiniLM-L12-v2':
+                    emb_mod = 'paraphrase-multilingual-MiniLM-L12-v2'
+                    lang = 'multilingual'
+                topic_model = BERTopic(embedding_model=emb_mod, hdbscan_model=cluster_model, language=lang, umap_model=umap_model, top_n_words=bert_top_n_words)
+                topics, probs = topic_model.fit_transform(topic_abs)
+                return topic_model, topics, probs
+            
+            @st.cache_data(ttl=3600, show_spinner=False)
+            def Vis_Topics(extype):
+                fig1 = topic_model.visualize_topics()
+                return fig1
+            
+            @st.cache_data(ttl=3600, show_spinner=False)
+            def Vis_Documents(extype):
+                fig2 = topic_model.visualize_documents(topic_abs)
+                return fig2
+    
+            @st.cache_data(ttl=3600, show_spinner=False)
+            def Vis_Hierarchy(extype):
+                fig3 = topic_model.visualize_hierarchy(top_n_topics=num_topic)
+                return fig3
         
-        @st.cache_data(ttl=3600, show_spinner=False)
-        def Vis_Documents(extype):
-          fig2 = topic_model.visualize_documents(topic_abs)
-          return fig2
-
-        @st.cache_data(ttl=3600, show_spinner=False)
-        def Vis_Hierarchy(extype):
-          fig3 = topic_model.visualize_hierarchy(top_n_topics=num_topic)
-          return fig3
+            @st.cache_data(ttl=3600, show_spinner=False)
+            def Vis_Heatmap(extype):
+                global topic_model
+                fig4 = topic_model.visualize_heatmap(n_clusters=num_topic-1, width=1000, height=1000)
+                return fig4
     
-        @st.cache_data(ttl=3600, show_spinner=False)
-        def Vis_Heatmap(extype):
-          global topic_model
-          fig4 = topic_model.visualize_heatmap(n_clusters=num_topic-1, width=1000, height=1000)
-          return fig4
-
-        @st.cache_data(ttl=3600, show_spinner=False)
-        def Vis_Barchart(extype):
-          fig5 = topic_model.visualize_barchart(top_n_topics=num_topic)
-          return fig5
-       
-        tab1, tab2, tab3 = st.tabs(["📈 Generate visualization", "📃 Reference", "📓 Recommended Reading"])
-        with tab1:
-          try:
-               with st.spinner('Performing computations. Please wait ...'):
-               
-                    topic_model, topics, probs = bertopic_vis(extype)
-                    time.sleep(.5)
-                    st.toast('Visualize Topics', icon='🏃')
-                    fig1 = Vis_Topics(extype)
-                   
-                    time.sleep(.5)
-                    st.toast('Visualize Document', icon='🏃')
-                    fig2 = Vis_Documents(extype)
-                   
-                    time.sleep(.5)
-                    st.toast('Visualize Document Hierarchy', icon='🏃')
-                    fig3 = Vis_Hierarchy(extype)
-                   
-                    time.sleep(.5)
-                    st.toast('Visualize Topic Similarity', icon='🏃')
-                    fig4 = Vis_Heatmap(extype)
-                   
-                    time.sleep(.5)
-                    st.toast('Visualize Terms', icon='🏃')
-                    fig5 = Vis_Barchart(extype)
+            @st.cache_data(ttl=3600, show_spinner=False)
+            def Vis_Barchart(extype):
+                fig5 = topic_model.visualize_barchart(top_n_topics=num_topic)
+                return fig5
+           
+            tab1, tab2, tab3 = st.tabs(["📈 Generate visualization", "📃 Reference", "📓 Recommended Reading"])
+            with tab1:
+                try:
+                    with st.spinner('Performing computations. Please wait ...'):
                    
-                    with st.expander("Visualize Topics"):
-                        st.write(fig1)
-                    with st.expander("Visualize Terms"):
-                        st.write(fig5)
-                    with st.expander("Visualize Documents"):
-                        st.write(fig2)
-                    with st.expander("Visualize Document Hierarchy"):  
-                        st.write(fig3)
-                    with st.expander("Visualize Topic Similarity"):
-                        st.write(fig4)
-                                        
-          except ValueError:
-               st.error('🙇‍♂️ Please raise the number of topics and click submit')
-          
-          except NameError:
-               st.warning('🖱️ Please click Submit')
+                        topic_model, topics, probs = bertopic_vis(extype)
+                        time.sleep(.5)
+                        st.toast('Visualize Topics', icon='🏃')
+                        fig1 = Vis_Topics(extype)
+                       
+                        time.sleep(.5)
+                        st.toast('Visualize Document', icon='🏃')
+                        fig2 = Vis_Documents(extype)
+                       
+                        time.sleep(.5)
+                        st.toast('Visualize Document Hierarchy', icon='🏃')
+                        fig3 = Vis_Hierarchy(extype)
+                       
+                        time.sleep(.5)
+                        st.toast('Visualize Topic Similarity', icon='🏃')
+                        fig4 = Vis_Heatmap(extype)
+                       
+                        time.sleep(.5)
+                        st.toast('Visualize Terms', icon='🏃')
+                        fig5 = Vis_Barchart(extype)
+                       
+                        with st.expander("Visualize Topics"):
+                            st.write(fig1)
+                        with st.expander("Visualize Terms"):
+                            st.write(fig5)
+                        with st.expander("Visualize Documents"):
+                            st.write(fig2)
+                        with st.expander("Visualize Document Hierarchy"):  
+                            st.write(fig3)
+                        with st.expander("Visualize Topic Similarity"):
+                            st.write(fig4)
+                                            
+                except ValueError:
+                    st.error('🙇‍���️ Please raise the number of topics and click submit')
+              
+                except NameError:
+                    st.warning('🖱️ Please click Submit')
+    
+            with tab2:
+                st.markdown('**Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794.** https://doi.org/10.48550/arXiv.2203.05794')
+              
+            with tab3:
+                st.markdown('**Jeet Rawat, A., Ghildiyal, S., & Dixit, A. K. (2022, December 1). Topic modelling of legal documents using NLP and bidirectional encoder representations from transformers. Indonesian Journal of Electrical Engineering and Computer Science, 28(3), 1749.** https://doi.org/10.11591/ijeecs.v28.i3.pp1749-1755')
+                st.markdown('**Yao, L. F., Ferawati, K., Liew, K., Wakamiya, S., & Aramaki, E. (2023, April 20). Disruptions in the Cystic Fibrosis Community’s Experiences and Concerns During the COVID-19 Pandemic: Topic Modeling and Time Series Analysis of Reddit Comments. Journal of Medical Internet Research, 25, e45249.** https://doi.org/10.2196/45249')
 
-        with tab2:
-          st.markdown('**Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794.** https://doi.org/10.48550/arXiv.2203.05794')
-          
-        with tab3:
-          st.markdown('**Jeet Rawat, A., Ghildiyal, S., & Dixit, A. K. (2022, December 1). Topic modelling of legal documents using NLP and bidirectional encoder representations from transformers. Indonesian Journal of Electrical Engineering and Computer Science, 28(3), 1749.** https://doi.org/10.11591/ijeecs.v28.i3.pp1749-1755')
-          st.markdown('**Yao, L. F., Ferawati, K., Liew, K., Wakamiya, S., & Aramaki, E. (2023, April 20). Disruptions in the Cystic Fibrosis Community’s Experiences and Concerns During the COVID-19 Pandemic: Topic Modeling and Time Series Analysis of Reddit Comments. Journal of Medical Internet Research, 25, e45249.** https://doi.org/10.2196/45249')
+    except:
+        st.error("Please ensure that your file is correct. Please contact us if you find that this is an error.", icon="🚨")
+        st.stop()