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| 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() |