Update modules/ui.py

modules/ui.py

@@ -301,49 +301,73 @@ def display_semantic_analysis_interface(nlp_models, lang_code):
     translations = {
         'es': {
             'title': "AIdeaText - Análisis semántico",
-            'input_label': "Ingrese un texto para analizar (máx. 5,000 palabras):",
-            'input_placeholder': "El objetivo de esta aplicación es que mejore sus habilidades de redacción...",  
+            'file_uploader': "Cargar archivo de texto",
             'analyze_button': "Analizar texto",
-            'network_diagram': "Análisis semántico: Diagrama de red",
+            'entities_list': "Lista de entidades",
+            'network_diagram': "Diagrama de red semántica",
         },
         'en': {
             'title': "AIdeaText - Semantic Analysis",
-            'input_label': "Enter a text to analyze (max 5,000 words):",
-            'input_placeholder': "The goal of this app is for you to improve your writing skills...",
+            'file_uploader': "Upload text file",
             'analyze_button': "Analyze text",
-            'network_diagram': "Semantic analysis: Network diagram",
+            'entities_list': "Entities List",
+            'network_diagram': "Semantic Network Diagram",
         },
         'fr': {
             'title': "AIdeaText - Analyse sémantique",
-            'input_label': "Entrez un texte à analyser (max 5 000 mots) :",
-            'input_placeholder': "Le but de cette application est d'améliorer vos compétences en rédaction...",
+            'file_uploader': "Télécharger le fichier texte",
             'analyze_button': "Analyser le texte",
-            'network_diagram': "Analyse sémantique : Diagramme de réseau",
+            'entities_list': "Liste des entités",
+            'network_diagram': "Diagramme de réseau sémantique",
         }
     }
 
     t = translations[lang_code]
 
-    if 'semantic_input_text' not in st.session_state:
-        st.session_state.semantic_input_text = ""
+    st.header(t['title'])
 
-    sentence_input = st.text_area(
-        t['input_label'],
-        height=150,
-        placeholder=t['input_placeholder'],
-        value=st.session_state.semantic_input_text,
-        key=f"semantic_text_input_{lang_code}"
-    )
-    st.session_state.semantic_input_text = sentence_input
+    uploaded_file = st.file_uploader(t['file_uploader'], type=['txt'])
+
+    if uploaded_file is not None:
+        text_content = uploaded_file.getvalue().decode('utf-8')
 
-    if st.button(t['analyze_button'], key=f"semantic_analyze_button_{lang_code}"):
-        if sentence_input:
-            doc = nlp_models[lang_code](sentence_input)
+        if st.button(t['analyze_button']):
+            col1, col2 = st.columns([1, 3])
 
-            with st.expander(t['network_diagram'], expanded=True):
-                fig = visualize_syntax(sentence_input, nlp_models[lang_code], lang_code)
+            with col1:
+                st.subheader(t['entities_list'])
+                entities = extract_entities(text_content, nlp_models[lang_code])
+                for entity_type, entity_list in entities.items():
+                    st.write(f"**{entity_type}:**")
+                    for entity in entity_list:
+                        st.write(f"- {entity}")
+
+            with col2:
+                st.subheader(t['network_diagram'])
+                fig = visualize_semantic_network(text_content, nlp_models[lang_code], lang_code)
                 st.pyplot(fig)
 
+def extract_entities(text, nlp):
+    doc = nlp(text)
+    entities = {
+        "Personas": [],
+        "Conceptos": [],
+        "Lugares": [],
+        "Fechas": []
+    }
+
+    for ent in doc.ents:
+        if ent.label_ == "PER":
+            entities["Personas"].append(ent.text)
+        elif ent.label_ == "LOC" or ent.label_ == "GPE":
+            entities["Lugares"].append(ent.text)
+        elif ent.label_ == "DATE":
+            entities["Fechas"].append(ent.text)
+        else:
+            entities["Conceptos"].append(ent.text)
+
+    return entities
+
 ##################################################################################################
 def get_chatbot_response(input_text):
     # Esta función debe ser implementada o importada de otro módulo