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| 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) |