Update app.py

app.py

@@ -4,40 +4,44 @@ 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)
+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()