#import module
import streamlit as st
from PIL import Image

#===config===
st.set_page_config(
     page_title="Coconut",
     page_icon="🥥",
     layout="wide"
)
st.title('🥥 Coconut Library Tool')
st.sidebar.success('Select page above')

#===page===
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.text('')
   st.text('')
   st.text('')
   st.text('')
   st.divider()
   st.error("This app works on Scopus's CSV file, Web of Science's Tab delimited file, and custom CSV file.", icon="🚨")
         
with mt2:
   st.header("Before you start")
   option = st.selectbox(
    'Please choose....',
    ('Keyword Stem', 'Topic Modeling', 'Bidirected Network', 'Sunburst'))
   
   if option == 'Keyword Stem':
        tab1, tab2, tab3 = st.tabs(["Prologue", "Steps", "Requirements"])
        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.")
            st.divider()
            st.write('💡 The idea came from this:')
            st.write('Santosa, F. A. (2022). 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.text("1. Put your file.")
            st.text("2. Choose your preferable method. Picture below may help you to choose wisely.")
            st.markdown("![Source: https://studymachinelearning.com/stemming-and-lemmatization/](https://studymachinelearning.com/wp-content/uploads/2019/09/stemmin_lemm_ex-1.png)")
            st.text('Source: https://studymachinelearning.com/stemming-and-lemmatization/')
            st.text("3. Now you need to select what kind of keywords you need.")
            st.text("4. Finally, you can download and use the file on VOSviewer, Bibliometrix, or else!")
            st.error("Please check what has changed. It's possible some keywords failed to find their roots.", icon="🚨")
            
        with tab3:
            st.text("""
            +----------------+------------------------+---------------------------------+
            |     Source     |       File Type        |             Column              |
            +----------------+------------------------+---------------------------------+
            | Scopus         | Comma-separated values | Author Keywords                 |
            |                | (.csv)                 | Index Keywords                  |
            +----------------+------------------------+---------------------------------+
            | Web of Science | Tab delimited file     | Author Keywords                 |
            |                | (.txt)                 | Keywords Plus                   |
            +----------------+------------------------+---------------------------------+
            | Other          | .csv                   | Change your column to 'Keyword' |
            +----------------+------------------------+---------------------------------+
            """)
            
   elif option == 'Topic Modeling':
        tab1, tab2, tab3 = st.tabs(["Prologue", "Steps", "Requirements"])
        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()
            st.write('💡 The idea came from this:')
            st.write('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')

        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("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="🚨")
            
        with tab3:
            st.text("""
            +----------------+------------------------+----------------------------------+
            |     Source     |       File Type        |              Column              |
            +----------------+------------------------+----------------------------------+
            | Scopus         | Comma-separated values | Abstract                         |
            |                | (.csv)                 |                                  |
            +----------------+------------------------+----------------------------------+
            | Web of Science | Tab delimited file     | Abstract                         |
            |                | (.txt)                 |                                  |
            +----------------+------------------------+----------------------------------+
            | Other          | .csv                   | Change your column to 'Abstract' |
            +----------------+------------------------+----------------------------------+
            """)
                             
   elif option == 'Bidirected Network':
        tab1, tab2, tab3 = st.tabs(["Prologue", "Steps", "Requirements"])
        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('Munan Li (2016): 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, DOI: 10.1080/09537325.2016.1220518')
            st.write('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')

        with tab2:
            st.text("1. Put your file.")
            st.text("2. Choose your preferable method. Picture below may help you to choose wisely.")
            st.markdown("![Source: https://studymachinelearning.com/stemming-and-lemmatization/](https://studymachinelearning.com/wp-content/uploads/2019/09/stemmin_lemm_ex-1.png)")
            st.text('Source: https://studymachinelearning.com/stemming-and-lemmatization/')
            st.text("3. Choose the value of Support and Confidence. If you're not sure how to use it please read the article above or just try it!")
            st.text("4. You can see the table and a simple visualization before making a network visualization.")
            st.text('5. Click "Generate network visualization" to see the network')
            st.error("The more data on your table, the more you'll see on network.", icon="🚨")
            st.error("If the table contains many rows, the network will take more time to process. Please use it efficiently.", icon="⌛")
            
        with tab3:
            st.text("""
            +----------------+------------------------+---------------------------------+
            |     Source     |       File Type        |             Column              |
            +----------------+------------------------+---------------------------------+
            | Scopus         | Comma-separated values | Author Keywords                 |
            |                | (.csv)                 | Index Keywords                  |
            +----------------+------------------------+---------------------------------+
            | Web of Science | Tab delimited file     | Author Keywords                 |
            |                | (.txt)                 | Keywords Plus                   |
            +----------------+------------------------+---------------------------------+
            | Other          | .csv                   | Change your column to 'Keyword' |
            |                |                        | and separate the words with ';' |
            +----------------+------------------------+---------------------------------+
            """)    
            
   elif option == 'Sunburst':
        tab1, tab2, tab3 = st.tabs(["Prologue", "Steps", "Requirements"])
        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.")
            
        with tab2:
            st.text("1. Put your Scopus CSV file.")
            st.text("2. You can set the range of years to see how it changed.")
            st.text("3. The sunburst has 3 levels. The inner circle is the type of data, meanwhile, the middle is the source title and the outer is the year the article was published.")
            st.text("4. The size of the slice depends on total documents. The average of inner and middle levels is calculated by formula below:")
            st.code('avg = sum(a * weights) / sum(weights)', language='python')
            
        with tab3:
            st.text("""
            +----------------+------------------------+----------------+
            |     Source     |       File Type        |     Column     |
            +----------------+------------------------+----------------+
            | Scopus         | Comma-separated values | (Source title, |
            |                | (.csv)                 | Document Type, |
            +----------------+------------------------| Cited by,      |
            | Web of Science | Tab delimited file     | Year)          |
            |                | (.txt)                 |                |
            +----------------+------------------------+----------------+
            """)          
     
with mt3:
   st.header('Behind this app')
   st.subheader('Faizhal Arif Santosa')
   st.text('Librarian. Polytechnic Institute of Nuclear Technology, National Research and Innovation Agency.')
   st.text('')
   st.subheader('Crissandra George')
   st.text('Digital Collections Manager Librarian. Case Western Reserve University.')
   st.text('')
   st.divider()
   st.header('Advisor')
   st.subheader('Dr. Manika Lamba')
   st.text('Elected Standing Committee Member & Chair of Professional Development Sub-Committee at IFLA STL Section | Editor-in-Chief for IJLIS (IGI Global).')
   st.text('')
   st.text('')
   st.divider()
   st.text('If you want to take a part, please let us know!')