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import streamlit as st
from ..text_analysis.morpho_analysis import perform_advanced_morphosyntactic_analysis
from ..text_analysis.semantic_analysis import perform_semantic_analysis
from ..text_analysis.discourse_analysis import perform_discourse_analysis
class AIdeaTextChatbot:
def __init__(self):
self.conversation_history = []
def handle_morphosyntactic_input(self, user_input, lang_code, nlp_models, t):
if user_input.startswith('/analisis_morfosintactico'):
text_to_analyze = user_input.split('[', 1)[1].rsplit(']', 1)[0]
result = perform_advanced_morphosyntactic_analysis(text_to_analyze, nlp_models[lang_code])
if result is None or 'arc_diagrams' not in result:
return t.get('morphosyntactic_analysis_error', 'Error en el análisis morfosintáctico'), None, None
return t.get('morphosyntactic_analysis_completed', 'Análisis morfosintáctico completado'), result['arc_diagrams'], result
else:
# Aquí puedes manejar otras interacciones relacionadas con el análisis morfosintáctico
return self.generate_response(user_input, lang_code), None, None
def handle_semantic_input(self, user_input, lang_code, nlp_models, t):
# Implementar lógica para análisis semántico
pass
def handle_discourse_input(self, user_input, lang_code, nlp_models, t):
# Implementar lógica para análisis de discurso
pass
def handle_generate_response(self, prompt, lang_code):
# Aquí iría la lógica para generar respuestas generales del chatbot
# Puedes usar la API de Claude aquí si lo deseas
pass
def initialize_chatbot():
return AIdeaTextChatbot()
def process_chat_input(user_input, lang_code, nlp_models, analysis_type, t, file_contents=None):
chatbot = st.session_state.get('aideatext_chatbot')
if not chatbot:
chatbot = initialize_chatbot()
st.session_state.aideatext_chatbot = chatbot
if analysis_type == 'morphosyntactic':
return chatbot.handle_morphosyntactic_input(user_input, lang_code, nlp_models, t)
# ... manejar otros tipos de análisis ... |