import streamlit as st
import pandas as pd
from extraction.keyphrase_extraction_pipeline import KeyphraseExtractionPipeline
from extraction.keyphrase_generation_pipeline import KeyphraseGenerationPipeline
import orjson


if "config" not in st.session_state:
    with open("config.json", "r") as f:
        content = f.read()
    st.session_state.config = orjson.loads(content)

st.set_page_config(
    page_icon="🔑",
    page_title="Keyphrase extraction/generation with Transformers",
    layout="wide",
    initial_sidebar_state="auto",
)


@st.cache(allow_output_mutation=True)
def load_pipeline(chosen_model):
    if "keyphrase-extraction" in chosen_model:
        return KeyphraseExtractionPipeline(chosen_model)
    elif "keyphrase-generation" in chosen_model:
        return KeyphraseGenerationPipeline(chosen_model)


def extract_keyphrases():
    st.session_state.keyphrases = pipe(st.session_state.input_text)


st.header("🔑 Keyphrase extraction/generation with Transformers")
col1, col2 = st.columns([1, 3])

col1.subheader("Select model")
chosen_model = col1.selectbox(
    "Choose your model:",
    st.session_state.config.get("models"),
)
st.session_state.chosen_model = chosen_model

pipe = load_pipeline(st.session_state.chosen_model)

col2.subheader("Input your text")
st.session_state.input_text = col2.text_area(
    "Input", st.session_state.config.get("example_text"), height=150
)
pressed = col2.button("Extract", on_click=extract_keyphrases)

if pressed:
    col2.subheader("🐧 Output")
    df = pd.DataFrame(data=st.session_state.keyphrases, columns=["Keyphrases"])
    col2.table(df)