import streamlit as st
from transformers import pipeline

access = "hf_"
token = "hhbFNpjKohezoexWMlyPUpvJQLWlaFhJaa"

def main():
    # Load the text classification model pipeline
    analysis = pipeline("text-classification", model='ZephyruSalsify/FinNews_SentimentAnalysis')
    classification = pipeline("text-classification", model="nickmuchi/finbert-tone-finetuned-finance-topic-classification", token=access+token)
    
    st.set_page_config(page_title="Financial News Analysis", page_icon="♕")
    
    # Streamlit application layout
    st.title("Financial News Analysis")
    st.write("Analyze corresponding Topic and Trend for Financial News!")
    st.image("./Fin.jpg", use_column_width = True)
    
    # Text input for user to enter the text
    text = st.text_area("Enter the Financial News", "")
    
    # Perform text classification when the user clicks the "Classify" button
    if st.button("Analyze"):
    
        label_1 = ""
        score_1 = 0.0
        label_2 = ""
        score_2 = 0.0
    
        # Perform text analysis on the input text
        results_1 = analysis(text)[0]
        results_2 = classification(text)[0]
    
        label_1 = results_1["label"]
        score_1 = results_1["score"]
        label_2 = results_2["label"]
        score_2 = results_2["score"]
                
    st.write("Financial Text:", text)
    st.write("Trend:", label_1)
    st.write("Trend_Score:", score_1)
    
    st.write("Finance Topic:", label_2)
    st.write("Topic_Score:", score_2)

if_name_== "_main_":
    main()