Update app.py

app.py

@@ -5,7 +5,7 @@ access = "hf_"
 token = "hhbFNpjKohezoexWMlyPUpvJQLWlaFhJaa"
 
 # Load the text classification model pipeline
-analysis = pipeline("text-classification", model='ZephyruSalsify/FinNews_SentimentAnalysis_Test')
+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="♕")
@@ -24,24 +24,6 @@ if st.button("Analyze"):
     # Perform text analysis on the input text
     results_1 = analysis(text)[0]
     results_2 = classification(text)[0]
-    
-    # Display the analysis result
-    #max_score_1 = float('-inf')
-    #max_label_1 = ''
-    
-    #for result_1 in results_1:
-    #    if result_1['score'] > max_score_1:
-    #        max_score_1 = result_1['score']
-    #        max_label_1 = result_1['label']
-
-    # Display the classification result
-    #max_score_2 = float('-inf')
-    #max_label_2 = ''
-    
-    #for result_2 in results_2:
-    #    if result_2['score'] > max_score_2:
-    #        max_score_2 = result_2['score']
-    #        max_label_2 = result_2['label']
             
 st.write("Financial Text:", text)
 st.write("Trend:", results_1["label"])