Update demo texts and fix morph word selector persistence

- Senter: swap to Sallust BC 5 (avoids pl. abbreviation bug)
- NER: use paper sentence (Iason et Medea)
- Dependency: use Ritchie's Fabulae Faciles (simple Latin)
- Morphology: use Seneca Ep. 1.3, fix session_state so word
selector persists across reruns

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

pages/3_senter_demo.py

@@ -19,7 +19,7 @@ st.title("Latin Sentence Segmenter")
 # Input text area
 text = st.text_area(
     "Enter a paragraph of Latin text to segment into sentences:",
-    value="Decrevit quondam senatus, ut L. Opimius consul videret, ne quid res publica detrimenti caperet; nox nulla intercessit; interfectus est propter quasdam seditionum suspiciones C. Gracchus, clarissimo patre, avo, maioribus, occisus est cum liberis M. Fulvius consularis. Simili senatus consulto C. Mario et L. Valerio consulibus est permissa res publica; num unum diem postea L. Saturninum tribunum pl. et C. Servilium praetorem mors ac rei publicae poena remorata est? At [vero] nos vicesimum iam diem patimur hebescere aciem horum auctoritatis. Habemus enim huiusce modi senatus consultum, verum inclusum in tabulis tamquam in vagina reconditum, quo ex senatus consulto confestim te interfectum esse, Catilina, convenit. Vivis, et vivis non ad deponendam, sed ad confirmandam audaciam. Cupio, patres conscripti, me esse clementem, cupio in tantis rei publicae periculis me non dissolutum videri, sed iam me ipse inertiae nequitiaeque condemno.",
+    value="Lucius Catilina, nobili genere natus, fuit magna vi et animi et corporis, sed ingenio malo pravoque. Huic ab adulescentia bella intestina, caedes, rapinae, discordia civilis grata fuere ibique iuventutem suam exercuit. Corpus patiens inediae, algoris, vigiliae supra quam cuiquam credibile est. Animus audax, subdolus, varius, cuius rei lubet simulator ac dissimulator, alieni adpetens, sui profusus, ardens in cupiditatibus; satis eloquentiae, sapientiae parum. Vastus animus inmoderata, incredibilia, nimis alta semper cupiebat.",
     height=200,
 )
 

pages/4_ner_demo.py

@@ -5,7 +5,7 @@ from spacy_streamlit import visualize_ner
 st.set_page_config(page_title="NER Demo", layout="wide")
 st.sidebar.header("NER Demo")
 
-default_text = """Gallia est omnis divisa in partes tres, quarum unam incolunt Belgae, aliam Aquitani, tertiam qui ipsorum lingua Celtae, nostra Galli appellantur. Hi omnes lingua, institutis, legibus inter se differunt. Gallos ab Aquitanis Garumna flumen, a Belgis Matrona et Sequana dividit."""
+default_text = """Iason et Medea e Thessalia expulsi ad urbem Corinthum venerunt, cuius urbis Creon quidam regnum tum obtinebat."""
 
 st.title("Latin Named Entity Recognition")
 

pages/5_dependency_demo.py

@@ -5,7 +5,7 @@ from spacy_streamlit import visualize_parser
 st.set_page_config(page_title="Dependency Demo", layout="wide")
 st.sidebar.header("Dependency Demo")
 
-default_text = """Quo usque tandem abutere, Catilina, patientia nostra? Quam diu etiam furor iste tuus nos eludet? Quem ad finem sese effrenata iactabit audacia?"""
+default_text = """Haec narrantur a poetis de Perseo. Perseus filius erat Iovis, maximi deorum; avus eius Acrisius appellabatur."""
 
 st.title("Latin Dependency Tree Visualizer")
 

pages/7_morphology_demo.py

@@ -5,7 +5,7 @@ import pandas as pd
 st.set_page_config(page_title="Morphology Demo", layout="wide")
 st.sidebar.header("Morphology Demo")
 
-default_text = """Arma virumque cano, Troiae qui primus ab oris Italiam, fato profugus, Laviniaque venit litora."""
+default_text = """Quaedam tempora eripiuntur nobis, quaedam subducuntur, quaedam effluunt."""
 
 # Human-readable labels for morphological feature values.
 # Keyed by (Feature, Value) to disambiguate collisions like
@@ -148,6 +148,7 @@ if st.button("Analyze Morphology"):
     doc = nlp(text)
 
     rows = []
+    token_data = []
     for token in doc:
         if token.is_punct or token.is_space:
             continue
@@ -159,6 +160,22 @@ if st.button("Analyze Morphology"):
                 "Features": format_morph_readable(token.morph),
             }
         )
+        token_data.append(
+            {
+                "text": token.text,
+                "lemma": token.lemma_,
+                "pos": token.pos_,
+                "tag": token.tag_,
+                "morph": token.morph.to_dict(),
+            }
+        )
+
+    st.session_state["morph_rows"] = rows
+    st.session_state["morph_tokens"] = token_data
+
+if "morph_rows" in st.session_state:
+    rows = st.session_state["morph_rows"]
+    token_data = st.session_state["morph_tokens"]
 
     df = pd.DataFrame(rows)
     st.dataframe(df, use_container_width=True, hide_index=True)
@@ -166,26 +183,24 @@ if st.button("Analyze Morphology"):
     st.markdown("---")
     st.markdown("**Select a word for detailed analysis:**")
 
-    # Build options with index to disambiguate duplicate tokens
-    content_tokens = [t for t in doc if not t.is_punct and not t.is_space]
-    word_options = [f"{t.text} ({i + 1})" for i, t in enumerate(content_tokens)]
+    word_options = [f"{t['text']} ({i + 1})" for i, t in enumerate(token_data)]
     selected_idx = st.selectbox(
         "Word:", range(len(word_options)), format_func=lambda i: word_options[i],
         key="morph_word",
     )
 
     if selected_idx is not None:
-        token = content_tokens[selected_idx]
+        t = token_data[selected_idx]
         col1, col2 = st.columns(2)
         with col1:
-            st.markdown(f"### {token.text}")
-            st.markdown(f"**Lemma:** {token.lemma_}")
+            st.markdown(f"### {t['text']}")
+            st.markdown(f"**Lemma:** {t['lemma']}")
             st.markdown(
-                f"**Part of Speech:** {POS_LABELS.get(token.pos_, token.pos_)}"
+                f"**Part of Speech:** {POS_LABELS.get(t['pos'], t['pos'])}"
             )
-            st.markdown(f"**Fine-grained Tag:** {token.tag_}")
+            st.markdown(f"**Fine-grained Tag:** {t['tag']}")
         with col2:
-            morph_dict = token.morph.to_dict()
+            morph_dict = t["morph"]
             if morph_dict:
                 st.markdown("**Morphological Features:**")
                 for feat, val in morph_dict.items():