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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 V2.0")
st.markdown(
"This is a tool to allow indexing & search content using neural capabilities"
)
st.markdown(
"In this second version you can:"
"\n - Index raw text, URLs and almost any file as documents"
"\n - Use Dense Passage Retrieval & Keyword Search pipeline"
"\n - Search the indexed documents"
)
st.markdown(
"TODO list:"
"\n - Build other pipelines"
"\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)
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