Update Home.py

Home.py

@@ -15,9 +15,10 @@ st.sidebar.success('Select page above')
 mt1, mt2, mt3 = st.tabs(["About", "How to", "Behind this app"])
 
 with mt1:
-   st.header("🌌 Hello universe!")
-   st.write('The coconut tree is known as one of the most useful trees. 🌴 The leaves function to produce oxygen through photosynthesis and are used for handicrafts, even for roof houses. The shells, the oil, the wood, the flowers, or even the husks can be something useful. From this philosophy, the Coconut Library Tool aims to be useful for librarians or anyone who needs cool features but is hindered by programming languages.')
-   st.write("We thank the cool people who have created so many facilities that we can place them in a place. We can't name them all, but we believe science will advance due to your efforts. 🧑🏻‍🤝‍🧑🏾")
+   st.header("Hello and welcome to the Coconut Library Tool!")
+   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 Library Tool 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.text('')
    st.text('')
    st.text('')
@@ -64,7 +65,7 @@ with mt2:
             """)
             
    elif option == 'Topic Modeling':
-        tab1, tab2, tab3 = st.tabs(["Prologue", "Steps", "Requirements"])
+        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.")
             st.divider()
@@ -73,7 +74,7 @@ with mt2:
 
         with tab2:
             st.text("1. Put your file. We use abstract column for this process.")
-            st.text("2. Choose your preferred method. LDA is the most widely used, whereas Biterm is appropriate for short text, and BERTopic works well for large text data.")
+            st.text("2. Choose your preferred method. LDA is the most widely used, whereas Biterm is appropriate for short text, and BERTopic works well for large text data as well as supports more than 50+ languages.")
             st.text("3. Finally, you can visualize your data.")
             st.error("This app includes lemmatization and stopwords for the abstract text. Currently, we only offer English words. For other languages you can use stemming.", icon="💬")
             st.error("If you want to see the topic on another data (chats, questionnaire, or other text), change the column name of your table to 'Abstract'.", icon="🚨")
@@ -92,9 +93,24 @@ with mt2:
             | Other          | .csv                   | Change your column to 'Abstract' |
             +----------------+------------------------+----------------------------------+
             """)
+
+        with tab4:  
+             st.subheader(':blue[pyLDA]')
+             st.button('Download image')
+             st.text("Click Download Image button.")
+             
+             st.divider()
+             st.subheader(':blue[Biterm]')
+             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.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':
-        tab1, tab2, tab3 = st.tabs(["Prologue", "Steps", "Requirements"])
+        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()
@@ -128,9 +144,15 @@ with mt2:
             |                |                        | and separate the words with ';' |
             +----------------+------------------------+---------------------------------+
             """)    
+
+        with tab4:  
+             st.subheader(':blue[Bidirected Network]')
+             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':
-        tab1, tab2, tab3 = st.tabs(["Prologue", "Steps", "Requirements"])
+        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.")
             
@@ -153,6 +175,12 @@ with mt2:
             |                | (.txt)                 |                |
             +----------------+------------------------+----------------+
             """)          
+
+        with tab4:  
+             st.subheader(':blue[Sunburst]')
+             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')