import streamlit as st
import core.pipelines as pipelines_functions
from inspect import getmembers, isfunction
from networkx.drawing.nx_agraph import to_agraph

def component_select_pipeline(container):
    pipeline_names, pipeline_funcs = list(zip(*getmembers(pipelines_functions, isfunction)))
    pipeline_names = [' '.join([n.capitalize() for n in name.split('_')]) for name in pipeline_names]
    with container:
        selected_pipeline = st.selectbox(
            'Select pipeline',
            pipeline_names,
            index=pipeline_names.index('Keyword Search') if 'Keyword Search' in pipeline_names else 0
        )
        st.session_state['search_pipeline'], \
            st.session_state['index_pipeline'] = \
                pipeline_funcs[pipeline_names.index(selected_pipeline)]()

def component_show_pipeline(container, pipeline):
    """Draw the pipeline"""
    with st.expander('Show pipeline'):
        graphviz = to_agraph(pipeline.graph)
        graphviz.layout("dot")
        st.graphviz_chart(graphviz.string())
    
def component_show_search_result(container, results):
    with container:
        for idx, document in enumerate(results):
            st.markdown(f"### Match {idx+1}")
            st.markdown(f"**Text**: {document['text']}")
            st.markdown(f"**Document**: {document['id']}")
            if document['score'] is not None:
                st.markdown(f"**Score**: {document['score']:.3f}")
            st.markdown("---")

def component_text_input(container):
    """Draw the Text Input widget"""
    with container:
        texts = []
        doc_id = 1
        with st.expander("Enter documents"):
            while True:
                text = st.text_input(f"Document {doc_id}", key=doc_id)
                if text != "":
                    texts.append({"text": text})
                    doc_id += 1
                    st.markdown("---")
                else:
                    break
        corpus = [
            {"text": doc["text"], "id": doc_id}
            for doc_id, doc in enumerate(texts)
        ]
        return corpus