Update Home.py

Home.py

@@ -4,42 +4,66 @@ from PIL import Image
 
 #===config===
 st.set_page_config(
-     page_title="Coconut",
-     page_icon="🥥",
-     layout="wide"
+    page_title="Coconut",
+    page_icon="🥥",
+    layout="wide",
+    initial_sidebar_state="collapsed"    
 )
-st.title('🥥 Coconut Libtool')
+
 hide_streamlit_style = """
             <style>
-            #MainMenu {visibility: hidden;}
+            #MainMenu 
+            {visibility: hidden;}
             footer {visibility: hidden;}
+            [data-testid="collapsedControl"] {display: none}
             </style>
             """
 st.markdown(hide_streamlit_style, unsafe_allow_html=True) 
 
-st.sidebar.success('Select page above')
+st.title('🥥 Coconut Libtool', anchor=False)
 
 #===page===
-mt1, mt2, mt3 = st.tabs(["About", "How to", "Behind this app"])
-
-with mt1:
-   st.header("Hello and welcome to the Coconut Libtool!")
-   st.write("The coconut tree is known as one of the most useful trees. Each part of this important tree has an integral function from the leaves producing oxygen through photosynthesis to the shells, oil, wood, flowers, and husks being used in a variety of ways, such as building houses, cooking, and more.")
-   st.write("Our philosophy aspires to emulate this highly cohesive and functionally unified environment where each part serves a specific function to the greater whole. 🌴 Just like the coconut tree, the Coconut Libtool is the all-in-one data mining and textual analysis tool for librarians or anyone interested in these applications. Our tool does not require any prior knowledge of coding or programming, making it approachable and great for users who want to test out these data analysis and visualization techniques.")  
-   st.write("We cannot thank everyone enough for who has assisted in the creation of this tool. Due to each individual’s efforts, science will advance, allowing for multiple analysis and visualization techniques to coexist within this one tool. 🧑🏻‍🤝‍🧑🏾")
-   st.divider()
-   st.text('We support Scopus, Web of Science, Lens, as well as personalized CSV files. Further information can be found in the "How to" section.')
-   st.divider()
-   st.write('To cite the Coconut Libtool, please use the following reference:') 
-   st.info("Santosa, Faizhal Arif, Lamba, Manika, & George, Crissandra J. (2023). Coconut Libtool. Zenodo. https://doi.org/10.5281/zenodo.8323458", icon="✍️")
+mt1, mt2 = st.tabs(["Menu", "How to"])
+
+with mt1:   
+    col1, col2, col3 = st.columns(3)
+    with col1.container(border=True):
+        st.markdown("![Stemming](https://raw.githubusercontent.com/faizhalas/library-tools/main/images/lemma.png)")
+        if st.button("Go to Keywords Stem"):
+            st.switch_page("pages/6 Keywords Stem.py")
+        
+    with col2.container(border=True):
+        st.markdown("![Topic modeling](https://raw.githubusercontent.com/faizhalas/library-tools/main/images/topicmodeling.png)")
+        if st.button("Go to Topic Modeling"):
+            st.switch_page("pages/2 Topic Modeling.py")
+
+    with col3.container(border=True):
+        st.markdown("![Bidirected network](https://raw.githubusercontent.com/faizhalas/library-tools/main/images/bidirected.png)")
+        if st.button("Go to Bidirected Network"):
+            st.switch_page("pages/3 Bidirected Network.py")
+
+    with col1.container(border=True):
+        st.markdown("![Sunburst](https://raw.githubusercontent.com/faizhalas/library-tools/main/images/sunburst.png)")
+        if st.button("Go to Sunburst Visualization"):
+            st.switch_page("pages/4 Sunburst.py")
+
+    with col2.container(border=True):
+        st.markdown("![Burst](https://raw.githubusercontent.com/faizhalas/library-tools/main/images/burst.png)")
+        if st.button("Go to Burst Detection"):
+            st.switch_page("pages/5 Burst Detection.py")
+
+    with col3.container(border=True):
+        st.markdown("![Scattertext](https://raw.githubusercontent.com/faizhalas/library-tools/main/images/scattertext.png)")
+        if st.button("Go to Scattertext"):
+            st.switch_page("pages/1 Scattertext.py")          
          
 with mt2:
-   st.header("Before you start")
-   option = st.selectbox(
-    'Please choose....',
-    ('Keyword Stem', 'Topic Modeling', 'Bidirected Network', 'Sunburst'))
+    st.header("Before you start", anchor=False)
+    option = st.selectbox(
+        'Please choose....',
+        ('Keyword Stem', 'Topic Modeling', 'Bidirected Network', 'Sunburst', 'Burst Detection', 'Scattertext'))
    
-   if option == 'Keyword Stem':
+    if option == 'Keyword Stem':
         tab1, tab2, tab3, tab4 = st.tabs(["Prologue", "Steps", "Requirements", "Download Result"])
         with tab1:
             st.write("This approach is effective for locating basic words and aids in catching the true meaning of the word, which can lead to improved semantic analysis and comprehension of the text. Some people find it difficult to check keywords before performing bibliometrics (using software such as VOSviewer and Bibliometrix). This strategy makes it easy to combine and search for fundamental words from keywords, especially if you have a large number of keywords. To do stemming or lemmatization on other text, change the column name to 'Keyword' in your file.")
@@ -75,16 +99,16 @@ with mt2:
             """)
 
         with tab4:  
-             st.subheader(':blue[Result]')
+             st.subheader(':blue[Result]', anchor=False)
              st.button('Press to download result 👈')
              st.text("Go to Result and click Download button.")  
 
              st.divider()
-             st.subheader(':blue[List of Keywords]')
+             st.subheader(':blue[List of Keywords]', anchor=False)
              st.button('Press to download keywords 👈')
              st.text("Go to List of Keywords and click Download button.")  
             
-   elif option == 'Topic Modeling':
+    elif option == 'Topic Modeling':
         tab1, tab2, tab3, tab4 = st.tabs(["Prologue", "Steps", "Requirements", "Download Visualization"])
         with tab1:
             st.write("Topic modeling has numerous advantages for librarians in different aspects of their work. A crucial benefit is an ability to quickly organize and categorize a huge volume of textual content found in websites, institutional archives, databases, emails, and reference desk questions. Librarians can use topic modeling approaches to automatically identify the primary themes or topics within these documents, making navigating and retrieving relevant information easier. Librarians can identify and understand the prevailing topics of discussion by analyzing text data with topic modeling tools, allowing them to assess user feedback, tailor their services to meet specific needs and make informed decisions about collection development and resource allocation. Making ontologies, automatic subject classification, recommendation services, bibliometrics, altmetrics, and better resource searching and retrieval are a few examples of topic modeling. To do topic modeling on other text like chats and surveys, change the column name to 'Abstract' in your file.")
@@ -117,27 +141,27 @@ with mt2:
             """)
 
         with tab4:  
-             st.subheader(':blue[pyLDA]')
+             st.subheader(':blue[pyLDA]', anchor=False)
              st.button('Download image')
              st.text("Click Download Image button.")
              
              st.divider()
-             st.subheader(':blue[Biterm]')
+             st.subheader(':blue[Biterm]', anchor=False)
              st.text("Click the three dots at the top right then select the desired format.")
              st.markdown("![Downloading visualization](https://raw.githubusercontent.com/faizhalas/library-tools/main/images/download_biterm.jpg)")
              
              st.divider()
-             st.subheader(':blue[BERTopic]')
+             st.subheader(':blue[BERTopic]', anchor=False)
              st.text("Click the camera icon on the top right menu")
              st.markdown("![Downloading visualization](https://raw.githubusercontent.com/faizhalas/library-tools/main/images/download_bertopic.jpg)")
                              
-   elif option == 'Bidirected Network':
+    elif option == 'Bidirected Network':
         tab1, tab2, tab3, tab4 = st.tabs(["Prologue", "Steps", "Requirements", "Download Graph"])
         with tab1:
             st.write("The use of network text analysis by librarians can be quite beneficial. Finding hidden correlations and connections in textual material is a significant advantage. Using network text analysis tools, librarians can improve knowledge discovery, obtain deeper insights, and support scholars meaningfully, ultimately enhancing the library's services and resources. This menu provides a two-way relationship instead of the general network of relationships to enhance the co-word analysis. Since it is based on ARM, you may obtain transactional data information using this menu. Please name the column in your file 'Keyword' instead.")
             st.divider()
             st.write('💡 The idea came from this:') 
-            st.write('Santosa, F. (2023). Adding Perspective to the Bibliometric Mapping Using Bidirected Graph. Open Information Science, 7(1), 20220152. https://doi.org/10.1515/opis-2022-0152')
+            st.write('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 tab2:
             st.text("1. Put your file.")
@@ -170,12 +194,12 @@ with mt2:
             """)    
 
         with tab4:  
-             st.subheader(':blue[Bidirected Network]')
+             st.subheader(':blue[Bidirected Network]', anchor=False)
              st.text("Zoom in, zoom out, or shift the nodes as desired, then right-click and select Save image as ...")
              st.markdown("![Downloading graph](https://raw.githubusercontent.com/faizhalas/library-tools/main/images/download_bidirected.jpg)")     
      
             
-   elif option == 'Sunburst':
+    elif option == 'Sunburst':
         tab1, tab2, tab3, tab4 = st.tabs(["Prologue", "Steps", "Requirements", "Download Visualization"])
         with tab1:
             st.write("Sunburst's ability to present a thorough and intuitive picture of complex hierarchical data is an essential benefit. Librarians can easily browse and grasp the relationships between different levels of the hierarchy by employing sunburst visualizations. Sunburst visualizations can also be interactive, letting librarians and users drill down into certain categories or subcategories for further information. This interactive and visually appealing depiction improves the librarian's understanding of the collection and provides users with an engaging and user-friendly experience, resulting in improved information retrieval and decision-making.")
@@ -206,22 +230,80 @@ with mt2:
             """)          
 
         with tab4:  
-             st.subheader(':blue[Sunburst]')
+             st.subheader(':blue[Sunburst]', anchor=False)
              st.text("Click the camera icon on the top right menu")
              st.markdown("![Downloading visualization](https://raw.githubusercontent.com/faizhalas/library-tools/main/images/download_bertopic.jpg)")
-     
-     
-with mt3:
-   st.header('Behind this app')
-   st.subheader('Faizhal Arif Santosa')
-   st.text('Librarian. National Research and Innovation Agency.')
-   st.text('')
-   st.subheader('Dr. Manika Lamba')
-   st.text('Postdoctoral Research Associate. University of Illinois Urbana-Champaign.') 
-   st.text('')
-   st.subheader('Crissandra George')
-   st.text('Digital Collections Manager Librarian. Case Western Reserve University.')
-   st.text('')
-   st.text('')
-   st.divider()
-   st.text('If you want to take a part, please let us know!')
+
+    elif option == 'Burst Detection':
+        tab1, tab2, tab3, tab4 = st.tabs(["Prologue", "Steps", "Requirements", "Download Visualization"])
+        with tab1:
+            st.write("Burst detection identifies periods when a specific event occurs with unusually high frequency, referred to as 'bursty'. This method can be applied to identify bursts in a continuous stream of events or in discrete groups of events (such as poster title submissions to an annual conference).") 
+            st.divider()
+            st.write('💡 The idea came from this:') 
+            st.write('Kleinberg, J. (2002). Bursty and hierarchical structure in streams. Knowledge Discovery and Data Mining. https://doi.org/10.1145/775047.775061')
+            
+        with tab2:
+            st.text("1. Put your file. Choose your preferred column to analyze.")
+            st.text("2. Choose your preferred method to count the words and decide how many top words you want to include or remove.")
+            st.text("3. To adjust the visualization, you can change the number of columns.")
+            st.text("4. Finally, you can visualize your data.")
+            st.error("This app includes lemmatization and stopwords. Currently, we only offer English words.", icon="💬")
+            
+        with tab3:
+            st.text("""
+            +----------------+------------------------+----------------------------------+
+            |     Source     |       File Type        |              Column              |
+            +----------------+------------------------+----------------------------------+
+            | Scopus         | Comma-separated values | Choose your preferred column     |
+            |                | (.csv)                 | that you have to analyze and     |
+            +----------------+------------------------| and need a column called "Year"  |
+            | Web of Science | Tab delimited file     |                                  |
+            |                | (.txt)                 |                                  |
+            +----------------+------------------------|                                  |
+            | Lens.org       | Comma-separated values |                                  |
+            |                | (.csv)                 |                                  |
+            +----------------+------------------------|                                  |
+            | Other          | .csv                   |                                  |
+            +----------------+------------------------+----------------------------------+
+            """)
+            
+        with tab4:
+            st.subheader(':blue[Burst Detection]', anchor=False)
+            st.text("Click the camera icon on the top right menu")
+            st.markdown("![Downloading visualization](https://raw.githubusercontent.com/faizhalas/library-tools/main/images/download_bertopic.jpg)")
+
+    elif option == 'Scattertext':
+        tab1, tab2, tab3, tab4 = st.tabs(["Prologue", "Steps", "Requirements", "Download Visualization"])
+        with tab1:
+            st.write("Scattertext is an open-source tool designed to visualize linguistic variations between document categories in a language-independent way. It presents a scatterplot, with each axis representing the rank-frequency of a term's occurrence within a category of documents.") 
+            st.divider()
+            st.write('💡 The idea came from this:') 
+            st.write('Kessler, J. S. (2017). Scattertext: a Browser-Based Tool for Visualizing how Corpora Differ. https://doi.org/10.48550/arXiv.1703.00565')
+                
+        with tab2:
+            st.text("1. Put your file. Choose your preferred column to analyze.")
+            st.text("2. Choose your preferred method to compare and decide words you want to remove.")
+            st.text("3. Finally, you can visualize your data.")
+            st.error("This app includes lemmatization and stopwords. Currently, we only offer English words.", icon="💬")
+            
+        with tab3:
+            st.text("""
+            +----------------+------------------------+----------------------------------+
+            |     Source     |       File Type        |              Column              |
+            +----------------+------------------------+----------------------------------+
+            | Scopus         | Comma-separated values | Choose your preferred column     |
+            |                | (.csv)                 | that you have                    |
+            +----------------+------------------------|                                  |
+            | Web of Science | Tab delimited file     |                                  |
+            |                | (.txt)                 |                                  |
+            +----------------+------------------------|                                  |
+            | Lens.org       | Comma-separated values |                                  |
+            |                | (.csv)                 |                                  |
+            +----------------+------------------------|                                  |
+            | Other          | .csv                   |                                  |
+            +----------------+------------------------+----------------------------------+
+            """)
+            
+        with tab4:
+            st.subheader(':blue[Scattertext]', anchor=False)
+            st.write("Click the :blue[Download SVG] on the right side.")