Update modules/ui/ui.py
Browse files- modules/ui/ui.py +13 -13
modules/ui/ui.py
CHANGED
@@ -984,47 +984,47 @@ def display_discourse_analysis_interface(nlp_models, lang_code):
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def display_discourse_results(result, lang_code, t):
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if result is None:
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-
st.warning(t
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return
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col1, col2 = st.columns(2)
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with col1:
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with st.expander(t
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if 'graph1' in result:
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st.pyplot(result['graph1'])
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else:
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st.warning(t
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st.subheader(t
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if 'key_concepts1' in result:
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concept_text1 = " | ".join([f"{concept} ({frequency:.2f})" for concept, frequency in result['key_concepts1']])
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st.write(concept_text1)
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else:
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-
st.warning(t
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with col2:
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-
with st.expander(t
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if 'graph2' in result:
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st.pyplot(result['graph2'])
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else:
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st.warning(t
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st.subheader(t
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if 'key_concepts2' in result:
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concept_text2 = " | ".join([f"{concept} ({frequency:.2f})" for concept, frequency in result['key_concepts2']])
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st.write(concept_text2)
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else:
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-
st.warning(t
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# Aquí puedes añadir más visualizaciones o comparaciones entre los dos documentos
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st.subheader(t
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if 'key_concepts1' in result and 'key_concepts2' in result:
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df = pd.DataFrame({
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t
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t
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}).fillna(0)
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st.dataframe(df)
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else:
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-
st.warning(t
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##################################################################################################
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#def display_saved_discourse_analysis(analysis_data):
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def display_discourse_results(result, lang_code, t):
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if result is None:
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st.warning(t.get('no_results', "No hay resultados disponibles."))
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return
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col1, col2 = st.columns(2)
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with col1:
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+
with st.expander(t.get('file_uploader1', "Documento 1"), expanded=True):
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if 'graph1' in result:
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st.pyplot(result['graph1'])
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else:
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st.warning(t.get('graph_not_available', "El gráfico no está disponible."))
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+
st.subheader(t.get('key_concepts', "Conceptos Clave"))
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if 'key_concepts1' in result:
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concept_text1 = " | ".join([f"{concept} ({frequency:.2f})" for concept, frequency in result['key_concepts1']])
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st.write(concept_text1)
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else:
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st.warning(t.get('concepts_not_available', "Los conceptos clave no están disponibles."))
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with col2:
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with st.expander(t.get('file_uploader2', "Documento 2"), expanded=True):
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if 'graph2' in result:
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st.pyplot(result['graph2'])
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else:
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+
st.warning(t.get('graph_not_available', "El gráfico no está disponible."))
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st.subheader(t.get('key_concepts', "Conceptos Clave"))
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if 'key_concepts2' in result:
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concept_text2 = " | ".join([f"{concept} ({frequency:.2f})" for concept, frequency in result['key_concepts2']])
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st.write(concept_text2)
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else:
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+
st.warning(t.get('concepts_not_available', "Los conceptos clave no están disponibles."))
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# Aquí puedes añadir más visualizaciones o comparaciones entre los dos documentos
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st.subheader(t.get('comparison', "Comparación"))
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if 'key_concepts1' in result and 'key_concepts2' in result:
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df = pd.DataFrame({
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t.get('file_uploader1', "Documento 1"): dict(result['key_concepts1']),
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t.get('file_uploader2', "Documento 2"): dict(result['key_concepts2'])
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}).fillna(0)
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st.dataframe(df)
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else:
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st.warning(t.get('comparison_not_available', "La comparación no está disponible."))
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##################################################################################################
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#def display_saved_discourse_analysis(analysis_data):
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