import streamlit as st
from streamlit_option_menu import option_menu
from core.search_index import index, search
from interface.components import (
    component_file_input,
    component_show_pipeline,
    component_show_search_result,
    component_text_input,
    component_article_url,
)


def page_landing_page(container):
    with container:
        st.header("Neural Search V1.0")

        st.markdown(
            "This is a tool to allow indexing & search content using neural capabilities"
        )
        st.markdown(
            "In this first version you can:"
            "\n  - Index raw text as documents"
            "\n  - Use Dense Passage Retrieval pipeline"
            "\n  - Search the indexed documents"
        )
        st.markdown(
            "TODO list:"
            "\n  - Build other pipelines"
            "\n  - Include file/url indexing"
            "\n  - [Optional] Include text to audio to read responses"
        )


def page_search(container):
    with container:
        st.title("Query me!")

        ## SEARCH ##
        query = st.text_input("Query")

        component_show_pipeline(st.session_state["pipeline"]["search_pipeline"])

        if st.button("Search"):
            st.session_state["search_results"] = search(
                queries=[query],
                pipeline=st.session_state["pipeline"]["search_pipeline"],
            )
        if "search_results" in st.session_state:
            component_show_search_result(
                container=container, results=st.session_state["search_results"][0]
            )


def page_index(container):
    with container:
        st.title("Index time!")

        component_show_pipeline(st.session_state["pipeline"]["index_pipeline"])

        input_funcs = {
            "Raw Text": (component_text_input, "card-text"),
            "URL": (component_article_url, "card-link"),
            "File": (component_file_input, "card-file"),
        }
        selected_input = option_menu(
            "Input Text",
            list(input_funcs.keys()),
            icons=[f[1] for f in input_funcs.values()],
            menu_icon="list",
            default_index=0,
            orientation="horizontal",
        )

        corpus = input_funcs[selected_input][0](container)

        if len(corpus) > 0:
            index_results = None
            if st.button("Index"):
                index_results = index(
                    corpus,
                    st.session_state["pipeline"]["index_pipeline"],
                )
            if index_results:
                st.write(index_results)