Create app.py

app.py

@@ -0,0 +1,47 @@
+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()