import streamlit as st
from annotated_text import annotated_text
from news_pipeline import NewsPipeline

CATEGORY_EMOJIS = {
    "Automobile": "🚗",
    "Entertainment": "🍿",
    "Politics": "⚖️",
    "Science": "🧪",
    "Sports": "🏀",
    "Technology": "💻",
    "World": "🌍",
}
FAKE_EMOJIS = {"Fake": "👻", "Real": "👍"}
CLICKBAIT_EMOJIS = {"Clickbait": "🎣", "Normal": "✅"}


def app():
    news_pipe = NewsPipeline()
    st.title("📰 News Analyzer")
    headline = st.text_input("Article headline:")
    content = st.text_area("Article content:")
    button = st.button("Analyze")
    if button:
        if headline == "" and content == "":
            st.error("Please, introduce an article headline and content.")
        else:
            if headline == "" or content == "":
                st.warning(
                    "Please, provide both headline and content to achieve better results."
                )
            else:
                st.success("Article successfully analyzed!")

            with st.spinner("Analyzing article..."):
                prediction = news_pipe(headline, content)
            col1, _, col2 = st.columns([2, 1, 6])
            with col1:
                st.subheader("Analysis:")
                st.markdown(
                    f"{CATEGORY_EMOJIS[prediction['category']]} **Category**: {prediction['category']}"
                )
                st.markdown(
                    f"{FAKE_EMOJIS[prediction['fake']]} **Fake**: {'Yes' if prediction['fake'] == 'Fake' else 'No'}"
                )
                st.markdown(
                    f"{CLICKBAIT_EMOJIS[prediction['clickbait']]} **Clickbait**: {'Yes' if prediction['clickbait'] == 'Clickbait' else 'No'}"
                )
            with col2:
                st.subheader("Headline")
                annotated_text(*parse_text(headline, prediction["ner"]["headline"]))
                st.subheader("Content")
                annotated_text(*parse_text(content, prediction["ner"]["content"]))


def parse_text(text, prediction):
    start = 0
    parsed_text = []
    for p in prediction:
        parsed_text.append(text[start : p["start"]])
        parsed_text.append((p["word"], p["entity_group"]))
        start = p["end"]
    parsed_text.append(text[start:])
    return parsed_text


if __name__ == "__main__":
    app()