app.py

from typing import List, Sequence, Tuple, Optional, Dict, Union, Callable   
import spacy
from spacy import displacy
from spacy.language import Language
import streamlit as st
from spacy_streamlit import visualize_parser
import base64
from PIL import Image
import deplacy
import graphviz





st.set_page_config(layout="wide")

st.title("Ancient Greek Analyzer")

st.markdown("Here you'll find four spaCy models for processing ancient Greek. They have been trained with the Universal Dependencies datasets *Perseus* and *Proiel*. We provide two types of models for each dataset. The '_lg' models were built with tok2vec pretrained embeddings and fasttext vectors, while the '_tfr' models have a transfomers layer. You can choose among models to compare their performance. More information about the models can be found in the [Huggingface Models Hub] (https://huggingface.co/Jacobo).")

st.sidebar.image("logo.png", use_column_width=False, width=150, caption="\n provided by Diogenet")

st.sidebar.title("Choose model:")
spacy_model = st.sidebar.selectbox("", ["grc_ud_perseus_lg", "grc_ud_proiel_lg"])

st.header("Text to analyze:")
text = st.text_area("", "Πλάτων ὁ Περικτιόνης τὸ γένος ἀνέφερεν εἰς Σόλωνα.")


nlp = spacy.load(spacy_model)
doc = nlp(text)

def get_html(html: str):
    """Convert HTML so it can be rendered."""
    WRAPPER = """<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; margin-bottom: 2.5rem">{}</div>"""
    # Newlines seem to mess with the rendering
    html = html.replace("\n", " ")
    return WRAPPER.format(html)

def get_svg(svg: str, style: str = "", wrap: bool = True):
    """Convert an SVG to a base64-encoded image."""
    b64 = base64.b64encode(svg.encode("utf-8")).decode("utf-8")
    html = f'<img src="data:image/svg+xml;base64,{b64}" style="{style}"/>'
    return get_html(html) if wrap else html

def visualize_parser(
    doc: spacy.tokens.Doc,
    *,
    title: Optional[str] = "Dependency parse & part of speech",
    key: Optional[str] = None,
) -> None:
    """Visualizer for dependency parses."""
    if title:
        st.header(title)
    cols = st.columns(4)
    split_sents = cols[0].checkbox(
        "Split sentences", value=True, key=f"{key}_parser_split_sents"
    )
    options = {
        "collapse_punct": cols[1].checkbox(
            "Collapse punct", value=True, key=f"{key}_parser_collapse_punct"    
        ),
        "compact": cols[3].checkbox("Compact mode", value=True, key=f"{key}_parser_compact"),
    }
    docs = [span.as_doc() for span in doc.sents] if split_sents else [doc]
    for sent in docs:
        html = displacy.render(sent, options=options, style="dep")
        # Double newlines seem to mess with the rendering
        html = html.replace("\n\n", "\n")
        if split_sents and len(docs) > 1:
            st.markdown(f"> {sent.text}")
        st.write(get_svg(html), unsafe_allow_html=True)


visualize_parser(doc)

#graph_r = deplacy.render(doc)

#st.graphviz_chart(graph_r)


graph_dot = deplacy.dot(doc)

#graphviz.Source(deplacy.dot(doc))

st.graphviz_chart(graph_dot)





#st.sidebar.title("Model 2")
#spacy_model2 = st.sidebar.selectbox("Model 2", ["grc_ud_perseus_lg", "grc_ud_proiel_lg"])

#st.header("Text to analyze:")
#text = st.text_area("", "Πλάτων ὁ Περικτιόνης τὸ γένος ἀνέφερεν εἰς Σόλωνα.")


#nlp = spacy.load(spacy_model2)
#doc2 = nlp(text)

#visualize_parser(doc2)

#visualizers = ["pos", "dep"]
#spacy_streamlit.visualize(models, default_text,visualizers)