Update modules/morphosyntax/morphosyntax_interface.py

modules/morphosyntax/morphosyntax_interface.py

@@ -168,31 +168,54 @@ def display_morphosyntax_results(result, lang_code, t):
             
             # Renombrar las columnas para mayor claridad
             pos_df = pos_df.rename(columns={
-                'pos': t['grammatical_category'],
-                'count': t['count'],
-                'percentage': t['percentage'],
-                'examples': t['examples']
+                'pos': morpho_t.get('grammatical_category', 'Grammatical category'),
+                'count': morpho_t.get('count', 'Count'),
+                'percentage': morpho_t.get('percentage', 'Percentage'),
+                'examples': morpho_t.get('examples', 'Examples')
             })
-            
+                        
             # Mostrar el dataframe
             st.dataframe(pos_df)
     
     with col2:
-        with st.expander(morpho_t.get('morphological_analysis', 'Morphological Analysis' ], expanded=True):
+        with st.expander(morpho_t.get('morphological_analysis', 'Morphological Analysis'), expanded=True):
             morph_df = pd.DataFrame(advanced_analysis['morphological_analysis'])
             
             # Definir el mapeo de columnas
             column_mapping = {
-                'text': t['word'],
-                'lemma': t['lemma'],
-                'pos': t['grammatical_category'],
-                'dep': t['dependency'],
-                'morph': t['morphology']
+                'text': morpho_t.get('word', 'Word'),
+                'lemma': morpho_t.get('lemma', 'Lemma'),
+                'pos': morpho_t.get('grammatical_category', 'Grammatical category'),
+                'dep': morpho_t.get('dependency', 'Dependency'),
+                'morph': morpho_t.get('morphology', 'Morphology')
             }
             
             # Renombrar las columnas existentes
             morph_df = morph_df.rename(columns={col: new_name for col, new_name in column_mapping.items() if col in morph_df.columns})
             
+            # Primero definimos las columnas con morpho_t
+            cat_col = morpho_t.get('grammatical_category', 'Grammatical category')
+            dep_col = morpho_t.get('dependency', 'Dependency')
+            morph_col = morpho_t.get('morphology', 'Morphology')
+    
+            # Luego las usamos en las transformaciones
+            morph_df[cat_col] = morph_df[cat_col].map(lambda x: POS_TRANSLATIONS[lang_code].get(x, x))
+            morph_df[dep_col] = morph_df[dep_col].map(lambda x: dep_translations[lang_code].get(x, x))
+            morph_df[morph_col] = morph_df[morph_col].apply(lambda x: translate_morph(x, lang_code))
+    
+            # Seleccionar y ordenar las columnas a mostrar
+            columns_to_display = [
+                morpho_t.get('word', 'Word'),
+                morpho_t.get('lemma', 'Lemma'),
+                cat_col,
+                dep_col,
+                morph_col
+            ]
+            columns_to_display = [col for col in columns_to_display if col in morph_df.columns]
+            
+            # Mostrar el DataFrame
+            st.dataframe(morph_df[columns_to_display])
+
             # Traducir las categorías gramaticales
             morph_df[t['grammatical_category']] = morph_df[t['grammatical_category']].map(lambda x: POS_TRANSLATIONS[lang_code].get(x, x))
             
@@ -232,6 +255,7 @@ def display_morphosyntax_results(result, lang_code, t):
                     'goeswith': 'va avec', 'reparandum': 'réparation', 'punct': 'ponctuation'
                 }
             }
+            
             morph_df[t['dependency']] = morph_df[t['dependency']].map(lambda x: dep_translations[lang_code].get(x, x))
             
             # Traducir la morfología
@@ -268,18 +292,24 @@ def display_morphosyntax_results(result, lang_code, t):
             morph_df[t['morphology']] = morph_df[t['morphology']].apply(lambda x: translate_morph(x, lang_code))
             
             # Seleccionar y ordenar las columnas a mostrar
-            columns_to_display = [t['word'], t['lemma'], t['grammatical_category'], t['dependency'], t['morphology']]
+            columns_to_display = [
+                morpho_t.get('word', 'Word'),
+                morpho_t.get('lemma', 'Lemma'),
+                morpho_t.get('grammatical_category', 'Grammatical category'),
+                morpho_t.get('dependency', 'Dependency'),
+                morpho_t.get('morphology', 'Morphology')
+            ]
             columns_to_display = [col for col in columns_to_display if col in morph_df.columns]
             
             # Mostrar el DataFrame
             st.dataframe(morph_df[columns_to_display])
             
     # Mostrar diagramas de arco (código existente)
-    with st.expander(t['arc_diagram'], expanded=True):
+    with st.expander(morpho_t.get('arc_diagram', 'Syntactic analysis: Arc diagram'), expanded=True):
         sentences = list(doc.sents)
         arc_diagrams = []
         for i, sent in enumerate(sentences):
-            st.subheader(f"{t['sentence']} {i+1}")
+            st.subheader(f"{morpho_t.get('sentence', 'Sentence')} {i+1}")
             html = displacy.render(sent, style="dep", options={"distance": 100})
             html = html.replace('height="375"', 'height="200"')
             html = re.sub(r'<svg[^>]*>', lambda m: m.group(0).replace('height="450"', 'height="300"'), html)