Update app.py

app.py

@@ -1,16 +1,41 @@
 import streamlit as st
 from transformers import pipeline
 
-sentiment_pipeline = pipeline("sentiment-analysis")
+# sentiment_pipeline = pipeline("sentiment-analysis")
 
-st.title("Financial Sentiment Analysis Using HuggingFace")
-st.write("Enter a Sentence to Analyze the Sentiment:")
+# st.title("Financial Sentiment Analysis Using HuggingFace")
+# st.write("Enter a Sentence to Analyze the Sentiment:")
 
-user_input = st.text_input("")
-if user_input:
-    result = sentiment_pipeline(user_input)
-    sentiment = result[0]["label"]
-    confidence = result[0]["score"]
+# user_input = st.text_input("")
+# if user_input:
+#     result = sentiment_pipeline(user_input)
+#     sentiment = result[0]["label"]
+#     confidence = result[0]["score"]
 
-    st.write(f"Sentiment: {sentiment}")
-    st.write(f"Confidence: {confidence:.2f}")
+#     st.write(f"Sentiment: {sentiment}")
+#     st.write(f"Confidence: {confidence:.2f}")
+
+def analyze_sentiment(text):
+    sentiment_analyzer = pipeline("sentiment-analysis")
+    result = sentiment_analyzer(text)
+    return result[0]['label'], result[0]['score']
+
+def main():
+    st.title("Financial Sentiment Analysis")
+
+    # User input
+    text_input = st.text_area("Enter financial news or tweet:", "")
+
+    if st.button("Analyze Sentiment"):
+        if text_input:
+            # Analyze sentiment
+            label, score = analyze_sentiment(text_input)
+
+            # Display results
+            st.write(f"Sentiment: {label}")
+            st.write(f"Confidence Score: {score:.2%}")
+        else:
+            st.warning("Please enter some text for analysis.")
+
+if __name__ == "__main__":
+    main()