import streamlit as st
from streamlit_option_menu import option_menu
from word2vec import *
import pandas as pd
from autocomplete import *
from plots import *
from lsj_dict import *
import json
from streamlit_tags import st_tags, st_tags_sidebar


st.set_page_config(page_title="ἄγαλμα | AGALMA", layout="centered", page_icon="images/AGALMA_logo.png")

# Cache data
@st.cache_data
def load_lsj_dict():
    return json.load(open('lsj_dict.json', 'r'))

@st.cache_data
def load_all_models_words():
    return sorted(load_compressed_word_list('corpora/compass_filtered.pkl.gz'), key=custom_sort)

@st.cache_data
def load_models_for_word_dict():
    return word_in_models_dict('corpora/compass_filtered.pkl.gz')


@st.cache_data
def load_all_lemmas():
    return load_compressed_word_list('all_lemmas.pkl.gz')

@st.cache_data
def load_lemma_count_dict():
    return count_lemmas('lemma_list_raw')

# Load compressed word list
all_models_words = load_all_models_words()

# Prepare lsj dictionary
lemma_dict = load_lsj_dict()

# Load dictionary with words as keys and eligible models as values
models_for_word_dict = load_models_for_word_dict()

lemma_counts = load_lemma_count_dict()



# Set styles for menu
styles_horizontal = {
    "container": {"display": "flex", "justify-content": "center"},
    "nav": {"display": "flex", "gap": "2px", "margin": "5px"},
    "nav-item": {"flex": "1", "font-family": "Helvetica"},
    "nav-link": {
        "background-color": "#f0f0f0",
        "border": "1px solid #ccc",
        "border-radius": "5px",
        "padding": "10px",
        "width": "150px",  
        "height": "60px",  
        "display": "flex",
        "align-items": "center",
        "justify-content": "center",
        "transition": "background-color 0.3s, color 0.3s",
        "color": "black",
        "text-decoration": "none"
    },
    "nav-link:hover": {
        "background-color": "rgb(238, 238, 238)",
        "color": "#000"
    },
    "nav-link-selected": {
        "background-color": "#B8E52B",
        "color": "white",
        "font-weight": "bold"
    },
    "icon": {"display": "None"}
}


styles_vertical = {
    "nav-link-selected": {
        "background-color": "#B8E52B",
        "color": "white",
        "font-weight": "bold"
    }
}

# Set vertical sidebar width to 350px
st.markdown(
    """
    <style>
        section[data-testid="stSidebar"] {
            width: 350px !important; # Set the width to your desired value
        }
    </style>
    """,
    unsafe_allow_html=True,
)


with st.sidebar:
    st.image('images/AGALMA_logo_v2.png')
    # st.markdown('# ἄγαλμα | AGALMA')
    selected = option_menu('ἄγαλμα | AGALMA', ["App", "About", "FAQ", "Subcorpora", "License"],
                           menu_icon="menu", default_index=0, orientation="vertical", styles=styles_vertical, icons=['house', 'file-person', 'question-square', 'book', 'file-earmark'])
    
if selected == "App":
    # Horizontal menu
    active_tab = option_menu(None, ["Nearest neighbours", "Cosine similarity", "3D graph", 'Dictionary'], 
        menu_icon="cast", default_index=0, orientation="horizontal", styles=styles_horizontal)


    # Adding CSS style to remove list-style-type
    st.markdown("""
    <style>
    /* Define a class to remove list-style-type */
    .no-list-style {
        list-style-type: none;
    }
    </style>
    """, unsafe_allow_html=True)



    # Nearest neighbours tab
    if active_tab == "Nearest neighbours":
        
        # All models in a list
        eligible_models = ["Archaic", "Classical", "Hellenistic", "Early Roman", "Late Roman"]
        all_models_words = load_all_models_words()
        
        with st.container():
            st.markdown("## Nearest Neighbours")
            st.markdown(
                    'Here you can extract the nearest neighbours to a chosen lemma. \
                    Please select one or more time slices and the preferred number of nearest neighbours. \
                    **Only type in Greek, with correct spirits and accents**.'
                )
            target_word = st.multiselect("Enter a word", options=all_models_words, max_selections=1)
            if len(target_word) > 0:
                target_word = target_word[0]
                
                eligible_models = models_for_word_dict[target_word]
            
            models = st.multiselect(
                "Select models to search for neighbours",
                eligible_models
                )
            n = st.slider("Number of neighbours", 1, 50, 15)
            
            nearest_neighbours_button = st.button("Find nearest neighbours")

        if nearest_neighbours_button:
            if validate_nearest_neighbours(target_word, n, models) == False:
                st.error('Please fill in all fields')
            else:
                # Rewrite models to list of all loaded models
                models = load_selected_models(models)
                
                nearest_neighbours = get_nearest_neighbours(target_word, n, models)
                
                all_dfs = []
                                
                # Create dataframes
                for model in nearest_neighbours.keys():
                    st.write(f"### {model}")
                    df = pd.DataFrame(
                        nearest_neighbours[model],
                        columns = ['Word', 'Cosine Similarity']
                    )
                    
                    # Add word occurences to dataframe
                    df['Occurences'] = df['Word'].apply(lambda x: lemma_counts[model][x])


                    
                    all_dfs.append((model, df))
                    st.table(df)        
                
                
                # Store content in a temporary file
                tmp_file = store_df_in_temp_file(all_dfs)
                
                # Open the temporary file and read its content
                with open(tmp_file, "rb") as file:
                    file_byte = file.read()
                    
                    # Create download button
                    st.download_button(
                        "Download results",
                        data=file_byte,
                        file_name = f'nearest_neighbours_{target_word}.xlsx',
                        mime='application/octet-stream'
                        )
                    

    # Cosine similarity tab
    elif active_tab == "Cosine similarity":
        all_models_words = load_all_models_words()
        
        with st.container():
            eligible_models_1 = []
            eligible_models_2 = []
            st.markdown("## Cosine similarity")
            st.markdown(
                    'Here you can extract the cosine similarity between two lemmas. \
                    Please select a time slice for each lemma. \
                    You can also calculate the cosine similarity between two vectors of the same lemma in different time slices. \
                    **Only type in Greek, with correct spirits and accents**. '
                )
            col1, col2 = st.columns(2)
            col3, col4 = st.columns(2)
            with col1:
                word_1 = st.multiselect("Enter a word", placeholder="πατήρ", max_selections=1, options=all_models_words)
                if len(word_1) > 0:
                    word_1 = word_1[0]
                    eligible_models_1 = models_for_word_dict[word_1]
                    
            with col2:
                time_slice_1 = st.selectbox("Time slice word 1", options = eligible_models_1)


            with st.container():
                with col3:
                    word_2 = st.multiselect("Enter a word", placeholder="μήτηρ", max_selections=1, options=all_models_words)
                    if len(word_2) > 0:
                        word_2 = word_2[0]
                        eligible_models_2 = models_for_word_dict[word_2]
                    
                with col4:
                    time_slice_2 = st.selectbox("Time slice word 2", eligible_models_2)
        
            # Create button for calculating cosine similarity
            cosine_similarity_button = st.button("Calculate cosine similarity")
        
        # If the button is clicked, execute calculation
        if cosine_similarity_button:
            cosine_simularity_score = get_cosine_similarity(word_1, time_slice_1, word_2, time_slice_2)
            st.markdown('''<span style="font-size: 24px"> The Cosine Similarity between %s (%s) and %s (%s) is: **%s**</span>''' % (word_1, time_slice_1, word_2, time_slice_2, cosine_simularity_score), unsafe_allow_html=True)	

    # 3D graph tab
    elif active_tab == "3D graph":
        st.markdown("## 3D graph")
        st.markdown('''
                    Here you can generate a 3D representation of the semantic space surrounding a target lemma. Please choose the lemma and the time slice.\
                    **Only type in Greek, with correct spirits and accents**.  \
                    
                    
                    **NB**: the 3D representations are reductions of the multi-dimensional representations created by the models. \
                    This is necessary for visualization, but while reducing the dimnesions some informations gets lost. \
                    The 3D representations are thus not 100% accurate. For more information, please consult the FAQ.
                    ''')
        
        col1, col2 = st.columns(2)
        
        # Load compressed word list
        all_models_words = load_all_models_words()
        
        with st.container():
            eligible_models = []
            with col1:
                word = st.multiselect("Enter a word", all_models_words, max_selections=1)
                if len(word) > 0:
                    word = word[0]
                    eligible_models = models_for_word_dict[word]
                
            with col2:
                time_slice = st.selectbox("Time slice", eligible_models)

            n = st.slider("Number of words", 1, 50, 15)

            graph_button = st.button("Create 3D graph")
            
            if graph_button:
                time_slice_model = convert_time_name_to_model(time_slice)
                nearest_neighbours_vectors = get_nearest_neighbours_vectors(word, time_slice_model, n)
                
                fig, df = make_3d_plot_tSNE(nearest_neighbours_vectors, word, time_slice_model)            
                
                st.plotly_chart(fig)
                
                
                
            
                
    # Dictionary tab
    elif active_tab == "Dictionary":
        
        with st.container():
            st.markdown('## Dictionary')
            st.markdown('Search a word in the Liddell-Scott-Jones dictionary. **Only type in Greek, with correct spirits and accents**. ')


            all_lemmas = load_all_lemmas()
            
            # query_word = st.multiselect("Search a word in the LSJ dictionary", all_lemmas, max_selections=1)
            
            query_tag = st_tags(label='',
                                text = '',
                                value = [],
                                suggestions = all_lemmas,
                                maxtags = 1,
                                key = '1'
                                )
            
            # If a word has been selected by user
            if query_tag:
                
                # Display word information
                if query_tag[0] in lemma_dict:
                    st.write(f"### {query_tag[0]}")
                    data = lemma_dict[query_tag[0]]
                elif query_tag[0].capitalize() in lemma_dict: # Some words are capitalized in the dictionary
                    st.write(f"### {query_tag[0].capitalize()}")
                    data = lemma_dict[query_tag[0].capitalize()]
                else:
                    st.error("Word not found in dictionary")
                    exit(-1)
                
                # Put text in readable format
                text = format_text(data)
                
                
                st.markdown(format_text(data), unsafe_allow_html = True)
                
                
                
                st.markdown("""
                            <style>
                            .tab {
                                display: inline-block;
                                margin-left: 4em;
                            }
                            .tr {
                                font-weight: bold;
                            }
                            .list-class {
                                list-style-type: none;
                                margin-top: 1em;
                            }
                            .primary-indicator {
                                font-weight: bold;
                                font-size: x-large;
                            }
                            .secondary-indicator {
                                font-weight: bold;
                                font-size: large;
                            }
                            .tertiary-indicator {
                                font-weight: bold;
                                font-size: medium;
                            }
                            .quaternary-indicator {
                                font-weight: bold;
                                font-size: medium;
                            }
                            .primary-class {
                                padding-left: 2em;
                            }
                            .secondary-class {
                                padding-left: 4em;
                            }
                            .tertiary-class {
                                padding-left: 6em;
                            }
                            .quaternary-class {
                                padding-left: 8em;
                            }
                            </style>
                            """, unsafe_allow_html=True)
                        

            
            
if selected == "About":
    st.markdown("""
        ## About
        Welcome to AGALMA | ἄγαλμα, the Ancient Greek Accessible Language Models for linguistic Analysis!        
        
        This interface was developed in the framework of Silvia Stopponi’s PhD project, \
        supervised by Saskia Peels-Matthey and Malvina Nissim at the University of Groningen (The Netherlands). \
        The aim of this tool is to make language models trained on Ancient Greek available to all interested people, respectless of their coding skills. \
            
        The following people were involved in the creation of this interface:
        
        **Mark den Ouden** developed the interface.
        
        **Silvia Stopponi** trained the models, defined the structure of the interface, and wrote the textual content.
        
        **Saskia Peels-Matthey** supervised the project and revised the structure of the interface and the textual content.
        
        **Malvina Nissim** supervised the project.
        
        **Anchoring Innovation** financially supported the creation of this interface. \
        Anchoring Innovation is the Gravitation Grant research agenda of the Dutch National Research School in Classical Studies, OIKOS. \
        It is financially supported by the Dutch ministry of Education, Culture and Science (NWO project number 024.003.012).
        
        <div style="text-align: center; font-weight: bold;">How to cite</div>
        
        If you use this interface for your research, please cite it as:

        Stopponi, Silvia, Mark den Ouden, Saskia Peels-Matthey & Malvina Nissim. 2024. \
        <span style="font-style: italic;">AGALMA: Ancient Greek Accessible Language Models for linguistic Analysis.</span>
        
        """, unsafe_allow_html=True)
 

if selected == "FAQ":
    st.markdown("""
        ## FAQ
        """)
    
    

    with st.expander(r"$\textsf{\Large What is this interface based on?}$"):
        st.write(
                "This interface is based on language models. Language models are probability distributions of \
                words or word sequences, which store statistical information about word co-occurrences. \
                This happens during the training phase, in which models process a corpus of texts in the \
                target language(s). Once trained, linguistic information can be extracted from the models, or \
                the models can be used to perform specific linguistic tasks. In this interface, we focus on the \
                extraction of semantic information. To that end, we created five models, corresponding to five \
                time slices. The models on which this interface is based are so-called Word Embedding \
                models (the specific architecture is called Word2Vec)."
                )
        
    with st.expander(r"$\textsf{\Large What are Word Embeddings?}$"):
        st.write(
            "Word Embeddings are representations of words obtained via language modelling. More in \
            detail, they are strings of numbers (called *vectors*) produced by a language model to \
            represent each word in the training corpus in a multi-dimensional space. Words that are more \
            similar in meaning will be closer to one another in this vector space (or semantic space) than \
            words that are less similar in meaning. The term *word embeddings* is often used as a \
            synonym of *predict models*, a type of language models introduced by Mikolov *et al.* (2013) \
            with the Word2Vec architecture. This interface is built upon Word2Vec models."
        )
        
    with st.expander(r"$\textsf{\Large Which corpus was used to train the models?}$"):
        st.markdown('''
            The five models on which this interface is based were trained on five diachronic slices of the \
            Diorisis Ancient Greek Corpus, which is ‘a digital collection of ancient Greek texts (from \
            Homer to the early fifth century AD) compiled for linguistic analyses’ (Vatri &amp; McGillivray \
            2018: 55). The Diorisis corpus contains a subset of the texts that can be found in the \
            Thesaurus Linguae Graecae. More information about the works and authors included in each \
            subcorpus is provided in the 'Subcorpora' tab in the menu on the left.'''
        , unsafe_allow_html=True)
        
        
        
    with st.expander(r"$\textsf{\Large How was the corpus divided into time slices?}$"):
        st.write(
            "The texts in the corpus were divided according to chronology. We tried to strike a balance \
            between respecting the traditional divisions of Ancient Greek literature into periods and \
            having slices of a more or less comparable size. The division is the following: \
            \
            Archaic: beginning-500 BCE; Classical: 499-324 BCE; Hellenistic: 323-0 BCE, Early Roman: \
            1-250 CE; Late Roman: 251-500 CE."
        )
    
    
    with st.expander(r"$\textsf{\small Which are the theoretical assumptions behind distributional semantic models, such as Word Embeddings?}$"):
        st.write(
            "Computational semantics is based on the Distributional Hypothesis. According to this \
            hypothesis, words used in similar lexical contexts (contexts of words surrounding them) will \
            have a similar meaning. This hypothesis was famously summarized by J.R. Firth as ‘you \
            shall know a word by the company it keeps’ (1957: xx). Phrased differently, this \
            means that two words that occur in similar lexical contexts are probably semantically \
            related. The words that occur in the most similar lexical contexts are referred to as \
            nearest neighbours. This does not necessarily mean, though, that these words even \
            occur together. A detailed introduction to distributional semantics can be found in the book \
            *Distributional Semantics* (Lenci &amp; Sahlgren 2023: 3-25)."
        )
        
    with st.expander(r"$\textsf{\Large What are the nearest neighbours?}$"):
        st.write(
            "Word vectors can be used as coordinates to represent words in a geometric space, called \
            *semantic space*. Words with similar vectors, occurring in similar contexts, are closer in the \
            space. The nearest neighbours to a word are the closest words to it in the semantic space. \
            Words close in the space are not necessarily synonyms, they are rather in a relationship of \
            semantic relatedness, i.e. they belong to the same semantic area. An example of neighbours \
            in the space could be: *star – moon – sun – cloud – plane – fly – blue*."
        )
        
    with st.expander(r"$\textsf{\Large Are the nearest neighbours the same as concordances?}$"):
        st.write(
            "No. The nearest neighbours to a target word do not necessarily occur together with it in the \
            same context, but each of them will be found in similar lexical contexts. For example, my \
            colleague Pete and I may often go to the same type of conferences and meet the same \
            group of people there, but it is quite possible that Pete and I never go to the same \
            conference at the same time. Pete and I are similar, but not necessarily spending time \
            together. The extraction of the nearest neighbours with word embeddings is thus different \
            from finding concordances. The nearest neighbours cannot be extracted manually with close- \
            reading methods."
        )
        
    with st.expander(r"$\textsf{\Large Which framework and parameters were used to train the models?}$"):
        st.write(
            "The Word2vec models were trained by using the CADE framework (Bianchi *et al.* 2020), a \
            technique which does not require space alignment, i.e. word embeddings trained on different \
            corpus slices are directly comparable. CADE was used with the following parameters: \
            size=30, siter=5, diter=5, workers=4, sg=0, ns=20. The chosen architecture was the \
            Continuous-Bag-of-Words. The context that is taken into account for each word are the 5 \
            words before, and the 5 words after the target word."
        )
        
    with st.expander(r"$\textsf{\Large What is the cosine similarity value?}$"):
        st.write(
            "The cosine similarity is a measure of the distance between two words in the semantic space. \
            More precisely, the cosine similarity is the cosine of angle between the two vectors in the \
            multi-dimensional space. The value ranges from -1 to 1. The higher the value of the cosine \
            similarity (the closer it is to 1), the closer two words are in the semantic space. For example, \
            according to our model, the cosine similarity value of πατήρ and μήτηρ in the Classical period \
            is 0.93, relatively high as we might expected for these obviously related words, while the \
            cosine similarity value of a random pair like πατήρ and τράπεζα in the same time slice is \
            0.12, considerably lower."
        )
   
    with st.expander(r"$\textsf{\Large What are the 3D representations?}$"):
        st.write(
           "The 3D representation is a way to graphically visualize the semantic space, the method used \
            on this website is called t-SNE. Semantic spaces are multi-dimensional, with as many \
            dimensions as the digits in the vectors. The embeddings used for this interface only have 30 \
            dimensions. A 3D representation reduces the dimensions to 3, to allow for graphic \
            representation. Even if 3D representations are effective means of making a semantic space \
            visible, **they are not 100% accurate**, since the visualization shows a reduction of the 30 \
            dimensions. We thus advise not to base any conclusions on the graphic representation only, \
            but to rely on nearest neighbours extraction and on cosine similarity."
       )
        
    with st.expander(r"$\textsf{\Large Is the information stored by Word Embeddings reliable?}$"):
        st.write(
            "The information stored in word embeddings is solely based on the training corpus. This \
            means that our models have no additional knowledge of the Ancient Greek language and \
            culture. All information extracted from a model thus reflect word co-occurrences, and word \
            meaning, in its specific training corpus. \
            \
            Please take into account that the results for words occurring very rarely may be inaccurate. \
            Language modelling works on a statistical basis, so that a word with only few occurrences \
            may not provide enough evidence to obtain reliable results. But it has been observed that an \
            extremely high word frequency can also affect the results. It often happens that the nearest \
            neighbours to words occurring very often are other high-frequency words, such as stop \
            words (e.g., prepositions, articles, particles). "
        )
        
    with st.expander(r"$\textsf{\Large What if I obtain 'strange' results?}$"):
        st.write(
            "For the abovementioned reasons mentioned, word embeddings are not always reliable \
            methods of semantic investigation. Interpretation of the results is always needed to decide \
            whether the results at hand are real patterns present in the corpus, and could thus reveal \
            interesting phenomena, or just noise present in the data."
        )
   
    with st.expander(r"$\textsf{\Large How can word embeddings help us study semantic change?}$"):
        st.write(
            "Cosine similarity can be computed between vectors of the same word in different time slices. \
            The higher the cosine similarity, the more similar the usage of a word is in the two considered \
            time slices. If the cosine similarity between a word’s vectors in two consecutive time slices is \
            particularly low, there is a chance that semantic change happened at that point in time. The \
            analysis of the nearest neighbours to the target word in the two slices can help clarifying if \
            change actually happened, and which is its direction."
        )
    
    st.markdown("""
    ## References
    
    Bianchi, F., Di Carlo, V., Nicoli, P., &amp; Palmonari, M. (2020). Compass-aligned distributional
    embeddings for studying semantic differences across corpora. *arXiv preprint
    arXiv:2004.06519*.
    
    Lenci, A., &amp; Sahlgren, M. (2023). *Distributional semantics*. Cambridge University Press.
    
    Mikolov, T., Chen, K., Corrado, G., &amp; Dean, J. (2013). Efficient estimation of word
    representations in vector space. *arXiv preprint arXiv:1301.3781*.

    Vatri, A., &amp; McGillivray, B. (2018). The Diorisis ancient Greek corpus: Linguistics and
    literature. *Research Data Journal for the Humanities and Social Sciences*, 3(1), 55-65.
    """)
      
  
if selected == "Subcorpora":
    st.markdown("""
                ## Subcorpora

                | Time Slice               | Tokens     | Authors/Texts                                                                                                                                                                                                                                                                                                 |
                |--------------------------|------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
                | Archaic (Homer-500 BCE)  | 229,999    | Homer, Hesiod, *Shield of Heracles*, 34 Homeric hymns.                                                                                                                                                                                                                                                           |
                | Classical (499-324 BCE)  | 2,628,193  | Andocides, Aeneas Tacticus, Antiphon, Aeschines, Aeschylus, Aristophanes, Aristotle, Demosthenes, Demades, Euripides, Herodotus, Hippocrates, Hyperides, Isaeus, Isocrates, Lycurgus, Lysias, Pindar, Plato, Sophocles, Thucydides, Xenophon.                          |
                | Hellenistic (323-31 BCE) | 1,471,917  | Apollonius Rhodius, Aratus, Asclepiodotus, Callimachus, Bion of Phlossa, Demetrius, *Against Dionysodorus* (Demosthenes), Dinarchus, Diodorus, Euclides, Hyperides, Moschus, Lycophron, Septuaginta, Polybius, Theocritus, Theophrastus.                                   |
                | Early Roman (30 BCE-250 CE) | 4,900,879 | Achilles Tatius, Aelian, Appian, Agathemerus, Aelius Aristides, Aretaeus, Arrian, Athenaeus, Barnabas, Cassius Dio, Clement of Alexandria, Claudius Ptolemy, Chariton, Dio Chrysostom, Diogenes Laertius, Dionysius of Halicarnassus, Epictetus, Flavius Josephus, Harpocration, Galen, Lucian, Longinus, Longus, New Testament, Marcus Aurelius, Oppian, Oppian of Apamaea, Onasander, Philostratus the Athenian, Philostratus the Younger, Parthenius of Nicaea, Pausanias, Philostratus of Lemnos, Plutarch, Pseudo Apollodorus, Pseudo-Aristides, Pseudo-Plutarch, *Second Alcibiades*, Strabo, Triphiodorus, Xenophon of Ephesus. |
                | Late Roman (251-500 CE)  | 753,907    | Callistratus, Basilius, Eusebius of Caesarea, Julian the Emperor, Nonnus, Plotinus, Quintus Smyrnaeus.                                                                                                                                                                                                        |

                """, unsafe_allow_html=True)
    

if selected == "License":
    st.markdown("""
        ## License
        The cosine similarity, nearest neighbours, and 3D representation data are licensed under a CC BY License.

        The LSJ dictionary has a CC BY-SA license and comes from the Unicode version of the dictionary produced by \
        [Giuseppe G. A. Celano](%s). The original (Betacode) version is provided under a CC BY-SA license by the [Perseus Digital Library](https://www.perseus.tufts.edu/). \
        Data available at https://github.com/PerseusDL/lexica/.
        """ % 'https://github.com/gcelano/LSJ_GreekUnicode?tab=readme-ov-file')
            
            

streamlit_style = """
            <style> 
            html, body {
                font-family: 'Helvetica';
            }
            </style>
            """
            
st.markdown(streamlit_style, unsafe_allow_html=True)