Update modules/semantic/semantic_interface.py

modules/semantic/semantic_interface.py

@@ -0,0 +1,89 @@
+# Importaciones generales
+import streamlit as st
+import re
+import io
+from io import BytesIO
+import base64
+import matplotlib.pyplot as plt
+import plotly.graph_objects as go
+import pandas as pd
+import numpy as np
+import time
+from datetime import datetime
+from streamlit_player import st_player  # Necesitarás instalar esta librería: pip install streamlit-player
+from spacy import displacy
+import logging
+import random
+
+
+from ..utils.widget_utils import generate_unique_key
+
+from ..database.morphosintax_mongo_db import store_student_morphosyntax_result
+from ..database.chat_db import store_chat_history
+from ..database.morphosintaxis_export import export_user_interactions
+
+import logging
+logger = logging.getLogger(__name__)
+
+
+def display_semantic_analysis_interface(nlp_models, lang_code):
+
+    t = translations[lang_code]
+
+    st.header(t['title'])
+
+    # Opción para introducir texto
+    text_input = st.text_area(
+        t['text_input_label'],
+        height=150,
+        placeholder=t['text_input_placeholder'],
+    )
+
+    # Opción para cargar archivo
+    uploaded_file = st.file_uploader(t['file_uploader'], type=['txt'])
+
+    if st.button(t['analyze_button']):
+        if text_input or uploaded_file is not None:
+            if uploaded_file:
+                text_content = uploaded_file.getvalue().decode('utf-8')
+            else:
+                text_content = text_input
+
+            # Realizar el análisis
+            analysis_result = perform_semantic_analysis(text_content, nlp_models[lang_code], lang_code)
+
+            # Guardar el resultado en el estado de la sesión
+            st.session_state.semantic_result = analysis_result
+
+            # Mostrar resultados
+            display_semantic_results(st.session_state.semantic_result, lang_code, t)           
+
+            # Guardar el resultado del análisis
+            if store_semantic_result(st.session_state.username, text_content, analysis_result):
+                st.success(t['success_message'])
+            else:
+                st.error(t['error_message'])
+        else:
+            st.warning(t['warning_message'])
+
+    elif 'semantic_result' in st.session_state:
+
+        # Si hay un resultado guardado, mostrarlo
+        display_semantic_results(st.session_state.semantic_result, lang_code, t)
+    
+    else:
+        st.info(t['initial_message'])  # Asegúrate de que 'initial_message' esté en tus traducciones
+            
+def display_semantic_results(result, lang_code, t):
+    if result is None:
+        st.warning(t['no_results'])  # Asegúrate de que 'no_results' esté en tus traducciones
+        return
+
+    # Mostrar conceptos clave
+    with st.expander(t['key_concepts'], expanded=True):
+        concept_text = " | ".join([f"{concept} ({frequency:.2f})" for concept, frequency in result['key_concepts']])
+        st.write(concept_text)
+
+    # Mostrar el gráfico de relaciones conceptuales
+    with st.expander(t['conceptual_relations'], expanded=True):
+        st.pyplot(result['relations_graph'])