Update modules/text_analysis/morpho_analysis.py

modules/text_analysis/morpho_analysis.py

@@ -1,5 +1,7 @@
 import spacy
+from collections import Counter
 from spacy import displacy
+import re
 from streamlit.components.v1 import html
 import base64
 
@@ -88,74 +90,111 @@ POS_TRANSLATIONS = {
     }
 }
 
-def generate_arc_diagram(doc):
-    arc_diagrams = []
-    for sent in doc.sents:
-        words = [token.text for token in sent]
-        # Calculamos el ancho del SVG basado en la longitud de la oración
-        svg_width = max(600, len(words) * 120)
-        # Altura fija para cada oración
-        svg_height = 350 # Controla la altura del SVG
-
-        # Renderizamos el diagrama de dependencias
-        html = displacy.render(sent, style="dep", options={
-            "add_lemma":False, # Introduced in version 2.2.4, this argument prints the lemma’s in a separate row below the token texts.
-            "arrow_spacing": 12, #This argument is used for adjusting the spacing between arrows in px to avoid overlaps.
-            "arrow_width": 2, #This argument is used for adjusting the width of arrow head in px.
-            "arrow_stroke": 2, #This argument is used for adjusting the width of arrow path in px.
-            "collapse_punct": True, #It attaches punctuation to the tokens.
-            "collapse_phrases": False, # This argument merges the noun phrases into one token.
-            "compact":False, # If you will take this argument as true, you will get the “Compact mode” with square arrows that takes up less space.
-            "color": "#ffffff",
-            "bg": "#0d6efd",
-            "compact": False, #Put the value of this argument True, if you want to use fine-grained part-of-speech tags (Token.tag_), instead of coarse-grained tags (Token.pos_).
-            "distance": 100,  # Aumentamos la distancia entre palabras
-            "fine_grained": False, #Put the value of this argument True, if you want to use fine-grained part-of-speech tags (Token.tag_), instead of coarse-grained tags (Token.pos_).
-            "offset_x": 55, #	This argument is used for spacing on left side of the SVG in px.
-            "word_spacing": 25, #This argument is used for adjusting the vertical spacing between words and arcs in px.
-        })
-
-        # Ajustamos el tamaño del SVG y el viewBox
-        html = re.sub(r'width="(\d+)"', f'width="{svg_width}"', html)
-        html = re.sub(r'height="(\d+)"', f'height="{svg_height}"', html)
-        html = re.sub(r'<svg', f'<svg viewBox="0 0 {svg_width} {svg_height}"', html)
-
-        #html = re.sub(r'<svg[^>]*>', lambda m: m.group(0).replace('height="450"', 'height="300"'), html)
-        #html = re.sub(r'<g [^>]*transform="translate\((\d+),(\d+)\)"', lambda m: f'<g transform="translate({m.group(1)},50)"', html)
-
-        # Movemos todo el contenido hacia abajo
-        #html = html.replace('<g', f'<g transform="translate(50, {svg_height - 200})"')
-
-         # Movemos todo el contenido hacia arriba para eliminar el espacio vacío en la parte superior
-        html = re.sub(r'<g transform="translate\((\d+),(\d+)\)"',
-                      lambda m: f'<g transform="translate({m.group(1)},10)"', html)
-
-
-        # Ajustamos la posición de las etiquetas de las palabras
-        html = html.replace('dy="1em"', 'dy="-1em"')
+#############################################################################################
+def get_repeated_words_colors(doc):
+    word_counts = Counter(token.text.lower() for token in doc if token.pos_ != 'PUNCT')
+    repeated_words = {word: count for word, count in word_counts.items() if count > 1}
 
-        # Ajustamos la posición de las etiquetas POS
-        html = html.replace('dy="0.25em"', 'dy="-3em"')
-
-        # Aumentamos el tamaño de la fuente para las etiquetas POS
-        html = html.replace('.displacy-tag {', '.displacy-tag { font-size: 14px;')
-
-        # Rotamos las etiquetas de las palabras para mejorar la legibilidad
-        #html = html.replace('class="displacy-label"', 'class="displacy-label" transform="rotate(30)"')
+    word_colors = {}
+    for token in doc:
+        if token.text.lower() in repeated_words:
+            word_colors[token.text.lower()] = POS_COLORS.get(token.pos_, '#FFFFFF')
 
+    return word_colors
+    
+######################################################################################################
+def highlight_repeated_words(doc, word_colors):
+    highlighted_text = []
+    for token in doc:
+        if token.text.lower() in word_colors:
+            color = word_colors[token.text.lower()]
+            highlighted_text.append(f'<span style="background-color: {color};">{token.text}</span>')
+        else:
+            highlighted_text.append(token.text)
+    return ' '.join(highlighted_text)
+    
+#################################################################################################
+def generate_arc_diagram(doc, lang_code):
+    sentences = list(doc.sents)
+    arc_diagrams = []
+    for sent in sentences:
+        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)
+        html = re.sub(r'<g [^>]*transform="translate\((\d+),(\d+)\)"', lambda m: f'<g transform="translate({m.group(1)},50)"', html)
         arc_diagrams.append(html)
     return arc_diagrams
-##################################################################################################################################
-
 
+#################################################################################################
+def get_detailed_pos_analysis(doc):
+    """
+    Realiza un análisis detallado de las categorías gramaticales (POS) en el texto.
+    """
+    pos_counts = Counter(token.pos_ for token in doc)
+    total_tokens = len(doc)
+    pos_analysis = []
+    for pos, count in pos_counts.items():
+        percentage = (count / total_tokens) * 100
+        pos_analysis.append({
+            'pos': pos,
+            'count': count,
+            'percentage': round(percentage, 2),
+            'examples': [token.text for token in doc if token.pos_ == pos][:5]  # Primeros 5 ejemplos
+        })
+    return sorted(pos_analysis, key=lambda x: x['count'], reverse=True)
+
+#################################################################################################
+def get_morphological_analysis(doc):
+    """
+    Realiza un análisis morfológico detallado de las palabras en el texto.
+    """
+    morphology_analysis = []
+    for token in doc:
+        if token.pos_ in ['NOUN', 'VERB', 'ADJ', 'ADV']:  # Enfocarse en categorías principales
+            morphology_analysis.append({
+                'text': token.text,
+                'lemma': token.lemma_,
+                'pos': token.pos_,
+                'tag': token.tag_,
+                'dep': token.dep_,
+                'shape': token.shape_,
+                'is_alpha': token.is_alpha,
+                'is_stop': token.is_stop,
+                'morph': str(token.morph)
+            })
+    return morphology_analysis
+
+#################################################################################################
+def get_sentence_structure_analysis(doc):
+    """
+    Analiza la estructura de las oraciones en el texto.
+    """
+    sentence_analysis = []
+    for sent in doc.sents:
+        sentence_analysis.append({
+            'text': sent.text,
+            'root': sent.root.text,
+            'root_pos': sent.root.pos_,
+            'num_tokens': len(sent),
+            'num_words': len([token for token in sent if token.is_alpha]),
+            'subjects': [token.text for token in sent if "subj" in token.dep_],
+            'objects': [token.text for token in sent if "obj" in token.dep_],
+            'verbs': [token.text for token in sent if token.pos_ == "VERB"]
+        })
+    return sentence_analysis
+    
+#################################################################################################
 def perform_advanced_morphosyntactic_analysis(text, nlp):
+    """
+    Realiza un análisis morfosintáctico avanzado del texto.
+    """
     doc = nlp(text)
     return {
         'pos_analysis': get_detailed_pos_analysis(doc),
         'morphological_analysis': get_morphological_analysis(doc),
         'sentence_structure': get_sentence_structure_analysis(doc),
-        'arc_diagrams': generate_arc_diagram(doc),
-        'repeated_words': get_repeated_words_colors(doc)
+        'arc_diagram': generate_arc_diagram(doc, nlp.lang)
     }
-    
-__all__ = ['perform_advanced_morphosyntactic_analysis']
+
+# Al final del archivo morph_analysis.py
+__all__ = ['get_repeated_words_colors', 'highlight_repeated_words', 'generate_arc_diagram', 'perform_advanced_morphosyntactic_analysis', 'POS_COLORS', 'POS_TRANSLATIONS']