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| import streamlit as st | |
| import logging | |
| from io import BytesIO | |
| import base64 | |
| from .semantic_float_reset import semantic_float_init, float_graph, toggle_float_visibility, update_float_content | |
| from .semantic_process import process_semantic_analysis | |
| from ..chatbot.chatbot import initialize_chatbot, process_semantic_chat_input | |
| from ..database.database_oldFromV2 import ( | |
| initialize_mongodb_connection, | |
| initialize_database_connections, | |
| create_admin_user, | |
| create_student_user, | |
| get_user, | |
| get_student_data, | |
| store_file_contents, | |
| retrieve_file_contents, | |
| get_user_files, | |
| delete_file, | |
| store_application_request, | |
| store_user_feedback, | |
| store_morphosyntax_result, | |
| store_semantic_result, | |
| store_discourse_analysis_result, | |
| store_chat_history, | |
| export_analysis_and_chat, | |
| get_user_analysis_summary, | |
| get_user_recents_chats, | |
| get_user_analysis_details | |
| ) | |
| from ..utils.widget_utils import generate_unique_key | |
| from .flexible_analysis_handler import FlexibleAnalysisHandler | |
| semantic_float_init() | |
| logging.basicConfig(level=logging.DEBUG) | |
| logger = logging.getLogger(__name__) | |
| def get_translation(t, key, default): | |
| return t.get(key, default) | |
| def fig_to_base64(fig): | |
| buf = BytesIO() | |
| fig.savefig(buf, format='png') | |
| buf.seek(0) | |
| img_str = base64.b64encode(buf.getvalue()).decode() | |
| return f'<img src="data:image/png;base64,{img_str}" />' | |
| def display_semantic_interface(lang_code, nlp_models, t): | |
| st.set_page_config(layout="wide") | |
| if 'semantic_chatbot' not in st.session_state: | |
| st.session_state.semantic_chatbot = initialize_chatbot('semantic') | |
| if 'semantic_chat_history' not in st.session_state: | |
| st.session_state.semantic_chat_history = [] | |
| if 'show_graph' not in st.session_state: | |
| st.session_state.show_graph = False | |
| if 'graph_id' not in st.session_state: | |
| st.session_state.graph_id = None | |
| 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 = process_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']) | |