Update app.py

app.py

@@ -1,44 +1,51 @@
 import streamlit as st
 from transformers import pipeline
 
-access = "hf_"
-token = "hhbFNpjKohezoexWMlyPUpvJQLWlaFhJaa"
+def analyze_financial_news():
+    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)
+    # 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="♕")
+    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)
+    # 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", "")
+    # Text input for user to enter the text
+    text = st.text_area("Enter the Financial News", "")
 
-label_1 = ""
-score_1 = 0.0
-label_2 = ""
-score_2 = 0.0
+    label_1 = ""
+    score_1 = 0.0
+    label_2 = ""
+    score_2 = 0.0
 
-analyze_clicked = st.button("Analyze")
+    analyze_clicked = st.button("Analyze")
 
-if analyze_clicked:
-    # Perform text analysis on the input text
-    results_1 = analysis(text)[0]
-    results_2 = classification(text)[0]
+    if analyze_clicked:
+        # 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"]
+        label_1 = results_1["label"]
+        score_1 = results_1["score"]
+        label_2 = results_2["label"]
+        score_2 = results_2["score"]
 
-# Display the results
-st.write("Financial Text:", text)
-st.write("Trend:", label_1)
-st.write("Trend_Score:", score_1)
+    # Display the results
+    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)
+    st.write("Finance Topic:", label_2)
+    st.write("Topic_Score:", score_2)
+
+def main():
+    analyze_financial_news()
+
+if __name__ == "__main__":
+    main()