import streamlit as st
import tensorflow as tf
import tensorflow_hub as hub
import numpy as np




new_model = tf.keras.models.load_model("best_model.h5",custom_objects={"KerasLayer": hub.KerasLayer}, compile=False)





def welcome():
    return "Welcome to my app"


def main():
    st.title("Financial News Sentiment Analysis App")
    st.write(
        "This app will tell you if mention news is Fake or Real by using Natural Language Processing")
    html_temp = """
    <div style="background-color:tomato;padding:10px">
    <h2 style="color:white;text-align:center;">Financial News Sentiment Analysis </h2>
    </div>
    """
    st.markdown(html_temp, unsafe_allow_html=True)

    text = st.text_area("Enter your Financial News")


    if st.button("Predict"):
        pred_prob = new_model.predict([text])
        predict = tf.squeeze(tf.round(pred_prob)).numpy()
        st.subheader("AI thinks that ...")
        
        if predict == 0:
            col1, col2 = st.columns(2)
            col1.metric("Prediction", value="It's a Negative News.")
            col2.metric("Confidence Level", value=f"{np.round(np.max(pred_prob) * 100)}%")
        else:
            col1, col2 = st.columns(2)
            col1.metric("Prediction", value="It's a Positive News.")
            col2.metric("Confidence Level", value=f"{np.round(np.max(pred_prob) * 100)}%")

    if st.button("About"):

        st.text("Built with Streamlit")


if __name__ == '__main__':
    main()