Update app.py

app.py

@@ -1,13 +1,14 @@
-from typing import List, Sequence, Tuple, Optional, Dict, Union, Callable   
+from typing import Optional 
 import spacy
 from spacy import displacy
 from spacy.language import Language
 import streamlit as st
 from spacy_streamlit import visualize_parser
+from spacy_streamlit import visualize_tokens
 import base64
 from PIL import Image
 import deplacy
-import graphviz
+#import graphviz
 
 
 
@@ -15,16 +16,16 @@ import graphviz
 
 st.set_page_config(layout="wide")
 
-st.title("Ancient Greek Analyzer")
+st.image("logo.png", use_column_width=False, width=150)
 
-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.title("Diogenet's Ancient Greek Syntax Analyzer")
 
-st.sidebar.image("logo.png", use_column_width=False, width=150, caption="\n provided by Diogenet")
+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.title("Choose model:")
-spacy_model = st.sidebar.selectbox("", ["grc_ud_perseus_lg", "grc_ud_proiel_lg","grc_ud_perseus_trf"])
+st.header("Select a model:")
+spacy_model = st.selectbox("", ["grc_ud_perseus_lg", "grc_ud_proiel_lg","grc_ud_perseus_trf"])
 
-st.header("Text to analyze:")
+st.header("Enter text:")
 text = st.text_area("", "Πλάτων ὁ Περικτιόνης τὸ γένος ἀνέφερεν εἰς Σόλωνα.")
 
 
@@ -47,7 +48,7 @@ def get_svg(svg: str, style: str = "", wrap: bool = True):
 def visualize_parser(
     doc: spacy.tokens.Doc,
     *,
-    title: Optional[str] = "Dependency parse & part of speech",
+    title: Optional[str] = "Dependency parse & part of speech:",
     key: Optional[str] = None,
 ) -> None:
     """Visualizer for dependency parses."""
@@ -61,7 +62,7 @@ def visualize_parser(
         "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"),
+        "compact": cols[3].checkbox("Compact mode", value=False, key=f"{key}_parser_compact"),
     }
     docs = [span.as_doc() for span in doc.sents] if split_sents else [doc]
     for sent in docs:
@@ -75,32 +76,11 @@ def visualize_parser(
 
 visualize_parser(doc)
 
-#graph_r = deplacy.render(doc)
-
-#st.graphviz_chart(graph_r)
 
+st.header("Tree View:")
 
 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)
+visualize_tokens(doc, attrs=["text", "lemma_", "pos_", "dep_"], title="Table View:")