Update app.py

app.py

@@ -1,33 +1,42 @@
-import os
 import streamlit as st
-from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
+from transformers import pipeline
 
 def analyze_financial_news():
+    access = "hf_"
+    token = "hhbFNpjKohezoexWMlyPUpvJQLWlaFhJaa"
+
+    # Load the text classification model pipeline
+    classification = pipeline("text-classification", model="nickmuchi/finbert-tone-finetuned-finance-topic-classification", token=access+token)
+    sentiment_analysis = pipeline("sentiment-analysis")
+
+    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 financial news
+    # Text input for user to enter the text
     text = st.text_area("Enter the Financial News", "")
 
-    # Load the summarization pipeline
-    summarization_pipe = pipeline("summarization", model="facebook/bart-large-cnn")
-
-    # Use pipeline as a high-level helper
-    summary = summarization_pipe(text)[0]['summary_text']
+    analyze_clicked = st.button("Analyze")
 
-    # Perform sentiment analysis on the summarized text
-    sentiment_pipe = pipeline("sentiment-analysis")
-    sentiment_results = sentiment_pipe(summary)[0]
+    if analyze_clicked:
+        # Perform text classification on the input text
+        results = classification(text)[0]
 
-    # Display the results
-    st.write("Financial Text:", text)
-    st.write("Summary:", summary)
-    st.write("Sentiment:", sentiment_results["label"])
-    st.write("Sentiment Score:", sentiment_results["score"])
+        # Check if the classification is "Energy | Oil"
+        if results["label"] == "Energy | Oil":
+            # If the news is related to Energy | Oil, perform sentiment analysis
+            sentiment_results = sentiment_analysis(text)[0]
+            
+            # Display the sentiment analysis result
+            st.write("Sentiment:", sentiment_results["label"])
+            st.write("Sentiment Score:", sentiment_results["score"])
+        else:
+            st.write("The provided news is not relevant to Energy | Oil.")
 
 def main():
-    st.set_page_config(page_title="Financial News Analysis", page_icon="♕")
     analyze_financial_news()
 
 if __name__ == "__main__":