app.py

import streamlit as st
from transformers import pipeline

access = "hf_"
token = "hhbFNpjKohezoexWMlyPUpvJQLWlaFhJaa"

# 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)