Upload app.py

app.py

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+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_Test')
+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"):
+
+    # 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"])
+st.write("Trend_Score:", results_1["score"])
+
+st.write("Finance Topic:", results_2["label"])
+st.write("Topic_Score:", results_2["score"])