new look change

app.py

@@ -1,11 +1,17 @@
 import streamlit as st
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-# Load pre-trained model and tokenizer
-model_name = "mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis"  # Replace with your chosen model
+model_name = "mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
+# Set the page title
+st.title("Financial Sentiment Analysis App")
+
+# Add a text input for the user to input financial news
+text_input = st.text_area("Enter Financial News:", "Tesla stock is soaring after record-breaking earnings.")
+
+# Function to perform sentiment analysis
 def predict_sentiment(text):
     inputs = tokenizer(text, return_tensors="pt")
     outputs = model(**inputs)
@@ -14,18 +20,31 @@ def predict_sentiment(text):
     predicted_sentiment = sentiment_mapping.get(sentiment_class, 'Unknown')
     return predicted_sentiment
 
-def main():
-    st.title("Financial Sentiment Analysis")
+# Button to trigger sentiment analysis
+if st.button("Analyze Sentiment"):
+    # Check if the input text is not empty
+    if text_input:
+        # Show loading spinner while processing
+        with st.spinner("Analyzing sentiment..."):
+            sentiment = predict_sentiment(text_input)
+            # Change the view based on the predicted sentiment
+            st.success(f"Sentiment: {sentiment}")
+            if sentiment == 'Positive':
+                st.balloons()  # Celebratory animation for positive sentiment
+            # Add additional views for other sentiments as needed
+    else:
+        st.warning("Please enter some text for sentiment analysis.")
 
-    # Get user input
-    text = st.text_area("Enter financial content:")
+# Optional: Display the raw sentiment scores
+if st.checkbox("Show Raw Sentiment Scores"):
+    if text_input:
+        inputs = tokenizer(text_input, return_tensors="pt")
+        outputs = model(**inputs)
+        raw_scores = outputs.logits[0].tolist()
+        st.info(f"Raw Sentiment Scores: {raw_scores}")
 
-    if st.button("Classify Sentiment"):
-        if text:
-            predicted_sentiment = predict_sentiment(text)
-            st.success(f"Predicted sentiment: {predicted_sentiment}")
-        else:
-            st.warning("Please enter some text.")
+# Optional: Display additional information or analysis
+# Add more components as needed for your specific use case
 
-if __name__ == "__main__":
-    main()
+# Add a footer
+st.text("Built with Streamlit and Transformers")