Update pages/3 Bidirected Network.py

pages/3 Bidirected Network.py

@@ -51,7 +51,7 @@ st.subheader('Put your file here...', anchor=False)
 
 #===clear cache===
 def reset_all():
-     st.cache_data.clear()
+    st.cache_data.clear()
 
 #===check type===
 @st.cache_data(ttl=3600)
@@ -79,193 +79,198 @@ 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)
+    try:
+        extype = get_ext(uploaded_file)
+        if extype.endswith('.csv'):
+             papers = upload(extype) 
+        elif extype.endswith('.txt'):
+             papers = conv_txt(extype)
+        
+        @st.cache_data(ttl=3600)
+        def get_data_arul(extype):
+            list_of_column_key = list(papers.columns)
+            list_of_column_key = [k for k in list_of_column_key if 'Keyword' in k]
+            return papers, list_of_column_key
+         
+        papers, list_of_column_key = get_data_arul(extype)
     
-    @st.cache_data(ttl=3600)
-    def get_data_arul(extype):
-        list_of_column_key = list(papers.columns)
-        list_of_column_key = [k for k in list_of_column_key if 'Keyword' in k]
-        return papers, list_of_column_key
-     
-    papers, list_of_column_key = get_data_arul(extype)
-
-    col1, col2 = st.columns(2)
-    with col1:
-        method = st.selectbox(
-             'Choose method',
-           ('Lemmatization', 'Stemming'), on_change=reset_all)
-    with col2:
-        keyword = st.selectbox(
-            'Choose column',
-           (list_of_column_key), on_change=reset_all)
-
-
-    #===body=== 
-    @st.cache_data(ttl=3600)
-    def clean_arul(extype):
-        global keyword, papers
-        try:
-            arul = papers.dropna(subset=[keyword])
-        except KeyError:
-            st.error('Error: Please check your Author/Index Keywords column.')
-            sys.exit(1)
-        arul[keyword] = arul[keyword].map(lambda x: re.sub('-—–', ' ', x))
-        arul[keyword] = arul[keyword].map(lambda x: re.sub('; ', ' ; ', x))
-        arul[keyword] = arul[keyword].map(lambda x: x.lower())
-        arul[keyword] = arul[keyword].dropna()
-        return arul
-
-    arul = clean_arul(extype)   
-
-    #===stem/lem===
-    @st.cache_data(ttl=3600)
-    def lemma_arul(extype):
-        lemmatizer = WordNetLemmatizer()
-        def lemmatize_words(text):
-             words = text.split()
-             words = [lemmatizer.lemmatize(word) for word in words]
-             return ' '.join(words)
-        arul[keyword] = arul[keyword].apply(lemmatize_words)
-        return arul
+        col1, col2 = st.columns(2)
+        with col1:
+            method = st.selectbox(
+                 'Choose method',
+               ('Lemmatization', 'Stemming'), on_change=reset_all)
+        with col2:
+            keyword = st.selectbox(
+                'Choose column',
+               (list_of_column_key), on_change=reset_all)
     
-    @st.cache_data(ttl=3600)
-    def stem_arul(extype):
-        stemmer = SnowballStemmer("english")
-        def stem_words(text):
-            words = text.split()
-            words = [stemmer.stem(word) for word in words]
-            return ' '.join(words)
-        arul[keyword] = arul[keyword].apply(stem_words)
-        return arul
-
-    if method is 'Lemmatization':
-        arul = lemma_arul(extype)
-    else:
-        arul = stem_arul(extype)
     
-    @st.cache_data(ttl=3600)
-    def arm(extype):
-        arule = arul[keyword].str.split(' ; ')
-        arule_list = arule.values.tolist()  
-        te_ary = te.fit(arule_list).transform(arule_list)
-        df = pd.DataFrame(te_ary, columns=te.columns_)
-        return df
-    df = arm(extype)
-
-    col1, col2, col3 = st.columns(3)
-    with col1:
-        supp = st.slider(
-            'Select value of Support',
-            0.001, 1.000, (0.010), on_change=reset_all)
-    with col2:
-        conf = st.slider(
-            'Select value of Confidence',
-            0.001, 1.000, (0.050), on_change=reset_all)
-    with col3:
-        maxlen = st.slider(
-            'Maximum length of the itemsets generated',
-            2, 8, (2), on_change=reset_all)
-
-    tab1, tab2, tab3 = st.tabs(["📈 Result & Generate visualization", "📃 Reference", "📓 Recommended Reading"])
+        #===body=== 
+        @st.cache_data(ttl=3600)
+        def clean_arul(extype):
+            global keyword, papers
+            try:
+                arul = papers.dropna(subset=[keyword])
+            except KeyError:
+                st.error('Error: Please check your Author/Index Keywords column.')
+                sys.exit(1)
+            arul[keyword] = arul[keyword].map(lambda x: re.sub('-—–', ' ', x))
+            arul[keyword] = arul[keyword].map(lambda x: re.sub('; ', ' ; ', x))
+            arul[keyword] = arul[keyword].map(lambda x: x.lower())
+            arul[keyword] = arul[keyword].dropna()
+            return arul
     
-    with tab1:
-        #===Association rules===
+        arul = clean_arul(extype)   
+    
+        #===stem/lem===
         @st.cache_data(ttl=3600)
-        def freqitem(extype):
-            freq_item = fpgrowth(df, min_support=supp, use_colnames=True, max_len=maxlen)
-            return freq_item
-
-        freq_item = freqitem(extype)
-        col1, col2 = st.columns(2)
+        def lemma_arul(extype):
+            lemmatizer = WordNetLemmatizer()
+            def lemmatize_words(text):
+                 words = text.split()
+                 words = [lemmatizer.lemmatize(word) for word in words]
+                 return ' '.join(words)
+            arul[keyword] = arul[keyword].apply(lemmatize_words)
+            return arul
+        
+        @st.cache_data(ttl=3600)
+        def stem_arul(extype):
+            stemmer = SnowballStemmer("english")
+            def stem_words(text):
+                words = text.split()
+                words = [stemmer.stem(word) for word in words]
+                return ' '.join(words)
+            arul[keyword] = arul[keyword].apply(stem_words)
+            return arul
+    
+        if method is 'Lemmatization':
+            arul = lemma_arul(extype)
+        else:
+            arul = stem_arul(extype)
+        
+        @st.cache_data(ttl=3600)
+        def arm(extype):
+            arule = arul[keyword].str.split(' ; ')
+            arule_list = arule.values.tolist()  
+            te_ary = te.fit(arule_list).transform(arule_list)
+            df = pd.DataFrame(te_ary, columns=te.columns_)
+            return df
+        df = arm(extype)
+    
+        col1, col2, col3 = st.columns(3)
         with col1:
-             st.write('🚨 The more data you have, the longer you will have to wait.')
+            supp = st.slider(
+                'Select value of Support',
+                0.001, 1.000, (0.010), on_change=reset_all)
         with col2:
-             showall = st.checkbox('Show all nodes', value=True, on_change=reset_all)
-
-        @st.cache_data(ttl=3600)
-        def arm_table(extype):
-            restab = association_rules(freq_item, metric='confidence', min_threshold=conf) 
-            restab = restab[['antecedents', 'consequents', 'antecedent support', 'consequent support', 'support', 'confidence', 'lift', 'conviction']]
-            restab['antecedents'] = restab['antecedents'].apply(lambda x: ', '.join(list(x))).astype('unicode')
-            restab['consequents'] = restab['consequents'].apply(lambda x: ', '.join(list(x))).astype('unicode')
-            if showall:
-                 restab['Show'] = True
+            conf = st.slider(
+                'Select value of Confidence',
+                0.001, 1.000, (0.050), on_change=reset_all)
+        with col3:
+            maxlen = st.slider(
+                'Maximum length of the itemsets generated',
+                2, 8, (2), on_change=reset_all)
+    
+        tab1, tab2, tab3 = st.tabs(["📈 Result & Generate visualization", "📃 Reference", "📓 Recommended Reading"])
+        
+        with tab1:
+            #===Association rules===
+            @st.cache_data(ttl=3600)
+            def freqitem(extype):
+                freq_item = fpgrowth(df, min_support=supp, use_colnames=True, max_len=maxlen)
+                return freq_item
+    
+            freq_item = freqitem(extype)
+            col1, col2 = st.columns(2)
+            with col1:
+                 st.write('🚨 The more data you have, the longer you will have to wait.')
+            with col2:
+                 showall = st.checkbox('Show all nodes', value=True, on_change=reset_all)
+    
+            @st.cache_data(ttl=3600)
+            def arm_table(extype):
+                restab = association_rules(freq_item, metric='confidence', min_threshold=conf) 
+                restab = restab[['antecedents', 'consequents', 'antecedent support', 'consequent support', 'support', 'confidence', 'lift', 'conviction']]
+                restab['antecedents'] = restab['antecedents'].apply(lambda x: ', '.join(list(x))).astype('unicode')
+                restab['consequents'] = restab['consequents'].apply(lambda x: ', '.join(list(x))).astype('unicode')
+                if showall:
+                     restab['Show'] = True
+                else:
+                     restab['Show'] = False
+                return restab 
+             
+            if freq_item.empty:
+                st.error('Please lower your value.', icon="🚨")
             else:
-                 restab['Show'] = False
-            return restab 
-         
-        if freq_item.empty:
-            st.error('Please lower your value.', icon="🚨")
-        else:
-            restab = arm_table(extype)
-            restab = st.data_editor(restab, use_container_width=True)
-            res = restab[restab['Show'] == True] 
-                   
-             #===visualize===
-                
-            if st.button('📈 Generate network visualization', on_click=reset_all):
-                with st.spinner('Visualizing, please wait ....'): 
-                     @st.cache_data(ttl=3600)
-                     def map_node(extype):
-                        res['to'] = res['antecedents'] + ' → ' + res['consequents'] + '\n Support = ' +  res['support'].astype(str) + '\n Confidence = ' +  res['confidence'].astype(str) + '\n Conviction = ' +  res['conviction'].astype(str)
-                        res_ant = res[['antecedents','antecedent support']].rename(columns={'antecedents': 'node', 'antecedent support': 'size'}) 
-                        res_con = res[['consequents','consequent support']].rename(columns={'consequents': 'node', 'consequent support': 'size'}) 
-                        res_node = pd.concat([res_ant, res_con]).drop_duplicates(keep='first')
-                        return res_node, res
-                     
-                     res_node, res = map_node(extype)
-
-                     @st.cache_data(ttl=3600)
-                     def arul_network(extype):
-                        nodes = []
-                        edges = []
-
-                        for w,x in zip(res_node['size'], res_node['node']):
-                            nodes.append( Node(id=x, 
-                                            label=x,
-                                            size=50*w+10,
-                                            shape="dot",
-                                            labelHighlightBold=True,
-                                            group=x,
-                                            opacity=10,
-                                            mass=1)
-                                    )   
-
-                        for y,z,a,b in zip(res['antecedents'],res['consequents'],res['confidence'],res['to']):
-                            edges.append( Edge(source=y, 
-                                            target=z,
-                                            title=b,
-                                            width=a*2,
-                                            physics=True,
-                                            smooth=True
-                                            ) 
-                                    )  
-                        return nodes, edges
-
-                     nodes, edges = arul_network(extype)
-                     config = Config(width=1200,
-                                     height=800,
-                                     directed=True, 
-                                     physics=True, 
-                                     hierarchical=False,
-                                     maxVelocity=5
-                                     )
-
-                     return_value = agraph(nodes=nodes, 
-                                           edges=edges, 
-                                           config=config)
-                     time.sleep(1)
-                     st.toast('Process completed', icon='📈')
+                restab = arm_table(extype)
+                restab = st.data_editor(restab, use_container_width=True)
+                res = restab[restab['Show'] == True] 
+                       
+                 #===visualize===
                     
-    with tab2:
-         st.markdown('**Santosa, F. A. (2023). Adding Perspective to the Bibliometric Mapping Using Bidirected Graph. Open Information Science, 7(1), 20220152.** https://doi.org/10.1515/opis-2022-0152')
-         
-    with tab3:
-        st.markdown('**Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In ACM SIGMOD Record (Vol. 22, Issue 2, pp. 207–216). Association for Computing Machinery (ACM).** https://doi.org/10.1145/170036.170072')
-        st.markdown('**Brin, S., Motwani, R., Ullman, J. D., & Tsur, S. (1997). Dynamic itemset counting and implication rules for market basket data. ACM SIGMOD Record, 26(2), 255–264.** https://doi.org/10.1145/253262.253325')
-        st.markdown('**Edmonds, J., & Johnson, E. L. (2003). Matching: A Well-Solved Class of Integer Linear Programs. Combinatorial Optimization — Eureka, You Shrink!, 27–30.** https://doi.org/10.1007/3-540-36478-1_3') 
-        st.markdown('**Li, M. (2016, August 23). An exploration to visualise the emerging trends of technology foresight based on an improved technique of co-word analysis and relevant literature data of WOS. Technology Analysis & Strategic Management, 29(6), 655–671.** https://doi.org/10.1080/09537325.2016.1220518')
+                if st.button('📈 Generate network visualization', on_click=reset_all):
+                    with st.spinner('Visualizing, please wait ....'): 
+                         @st.cache_data(ttl=3600)
+                         def map_node(extype):
+                            res['to'] = res['antecedents'] + ' → ' + res['consequents'] + '\n Support = ' +  res['support'].astype(str) + '\n Confidence = ' +  res['confidence'].astype(str) + '\n Conviction = ' +  res['conviction'].astype(str)
+                            res_ant = res[['antecedents','antecedent support']].rename(columns={'antecedents': 'node', 'antecedent support': 'size'}) 
+                            res_con = res[['consequents','consequent support']].rename(columns={'consequents': 'node', 'consequent support': 'size'}) 
+                            res_node = pd.concat([res_ant, res_con]).drop_duplicates(keep='first')
+                            return res_node, res
+                         
+                         res_node, res = map_node(extype)
+    
+                         @st.cache_data(ttl=3600)
+                         def arul_network(extype):
+                            nodes = []
+                            edges = []
+    
+                            for w,x in zip(res_node['size'], res_node['node']):
+                                nodes.append( Node(id=x, 
+                                                label=x,
+                                                size=50*w+10,
+                                                shape="dot",
+                                                labelHighlightBold=True,
+                                                group=x,
+                                                opacity=10,
+                                                mass=1)
+                                        )   
+    
+                            for y,z,a,b in zip(res['antecedents'],res['consequents'],res['confidence'],res['to']):
+                                edges.append( Edge(source=y, 
+                                                target=z,
+                                                title=b,
+                                                width=a*2,
+                                                physics=True,
+                                                smooth=True
+                                                ) 
+                                        )  
+                            return nodes, edges
+    
+                         nodes, edges = arul_network(extype)
+                         config = Config(width=1200,
+                                         height=800,
+                                         directed=True, 
+                                         physics=True, 
+                                         hierarchical=False,
+                                         maxVelocity=5
+                                         )
+    
+                         return_value = agraph(nodes=nodes, 
+                                               edges=edges, 
+                                               config=config)
+                         time.sleep(1)
+                         st.toast('Process completed', icon='📈')
+                        
+        with tab2:
+             st.markdown('**Santosa, F. A. (2023). Adding Perspective to the Bibliometric Mapping Using Bidirected Graph. Open Information Science, 7(1), 20220152.** https://doi.org/10.1515/opis-2022-0152')
+             
+        with tab3:
+            st.markdown('**Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In ACM SIGMOD Record (Vol. 22, Issue 2, pp. 207–216). Association for Computing Machinery (ACM).** https://doi.org/10.1145/170036.170072')
+            st.markdown('**Brin, S., Motwani, R., Ullman, J. D., & Tsur, S. (1997). Dynamic itemset counting and implication rules for market basket data. ACM SIGMOD Record, 26(2), 255–264.** https://doi.org/10.1145/253262.253325')
+            st.markdown('**Edmonds, J., & Johnson, E. L. (2003). Matching: A Well-Solved Class of Integer Linear Programs. Combinatorial Optimization — Eureka, You Shrink!, 27–30.** https://doi.org/10.1007/3-540-36478-1_3') 
+            st.markdown('**Li, M. (2016, August 23). An exploration to visualise the emerging trends of technology foresight based on an improved technique of co-word analysis and relevant literature data of WOS. Technology Analysis & Strategic Management, 29(6), 655–671.** https://doi.org/10.1080/09537325.2016.1220518')
+
+    except:
+        st.error("Please ensure that your file is correct. Please contact us if you find that this is an error.", icon="🚨")
+        st.stop()