import streamlit as st
from streamlit import session_state as session
from src.config.configs import ProjectPaths
import numpy as np
from src.laion_clap.inference import AudioEncoder


@st.cache(persist=True, show_spinner=False, suppress_st_warning=True)
def load_data():
    vectors = np.load(ProjectPaths.DATA_DIR.joinpath("vectors", "audio_representations.npy"))
    return vectors


recommender = AudioEncoder()
audio_vectors = load_data()

dataframe = None

st.title("""
Curate me a Playlist.
 """)

st.text("")
st.text("")
st.text("")
st.text("")

session.text_input = st.text(label="Describe a playlist")

st.text("")
st.text("")

session.slider_count = st.slider(label="movie_count", min_value=5, max_value=50)

st.text("")
st.text("")

buffer1, col1, buffer2 = st.columns([1.45, 1, 1])

is_clicked = col1.button(label="Curate")

if is_clicked:
    text_embed = recommender.get_text_embedding(session.text_input)


st.text("")
st.text("")
st.text("")
st.text("")

if dataframe is not None:
    st.table(dataframe)