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import streamlit as st
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import spacy
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import networkx as nx
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import matplotlib.pyplot as plt
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import pandas as pd
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import numpy as np
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from .semantic_analysis import (
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create_concept_graph,
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visualize_concept_graph,
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identify_key_concepts,
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POS_COLORS,
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POS_TRANSLATIONS,
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ENTITY_LABELS
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)
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def compare_semantic_analysis(text1, text2, nlp, lang):
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doc1 = nlp(text1)
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doc2 = nlp(text2)
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key_concepts1 = identify_key_concepts(doc1)
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key_concepts2 = identify_key_concepts(doc2)
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G1 = create_concept_graph(doc1, key_concepts1)
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G2 = create_concept_graph(doc2, key_concepts2)
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fig1 = visualize_concept_graph(G1, lang)
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fig2 = visualize_concept_graph(G2, lang)
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fig1.suptitle("")
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fig2.suptitle("")
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return fig1, fig2, key_concepts1, key_concepts2
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def create_concept_table(key_concepts):
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df = pd.DataFrame(key_concepts, columns=['Concepto', 'Frecuencia'])
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df['Frecuencia'] = df['Frecuencia'].round(2)
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return df
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def perform_discourse_analysis(text1, text2, nlp, lang):
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graph1, graph2, key_concepts1, key_concepts2 = compare_semantic_analysis(text1, text2, nlp, lang)
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table1 = create_concept_table(key_concepts1)
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table2 = create_concept_table(key_concepts2)
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return {
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'graph1': graph1,
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'graph2': graph2,
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'key_concepts1': key_concepts1,
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'key_concepts2': key_concepts2
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}
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def display_discourse_analysis_results(analysis_result, lang_code):
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t = get_translations(lang_code)
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col1, col2 = st.columns(2)
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with col1:
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with st.expander(t['doc1_title'], expanded=True):
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st.pyplot(analysis_result['graph1'])
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st.subheader(t['key_concepts'])
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st.table(analysis_result['table1'])
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with col2:
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with st.expander(t['doc2_title'], expanded=True):
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st.pyplot(analysis_result['graph2'])
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st.subheader(t['key_concepts'])
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st.table(analysis_result['table2']) |
