Update app.py

app.py

@@ -2,48 +2,46 @@ import streamlit as st
 from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
 
 def main():
-    # Streamlit app interface design
+    # Streamlit app interface design with updated title and emoji usage
     st.title("📈 Financial Text Analysis")
-    st.write("Enter the financial text below to analyze its sentiment and generate a summary:")
 
-    # User input area for text
+    # User input area with updated prompt and emoji
     user_input = st.text_area("📝 Text Input", height=300)
 
-    # Load sentiment analysis pipeline only once using st.session_state
+    # Initialize sentiment analysis and summarization pipelines only once using st.session_state
     if 'sentiment_analyzer' not in st.session_state:
-        # Load your custom sentiment analysis model
+        # Load your custom sentiment analysis model with updated paths
         model = AutoModelForSequenceClassification.from_pretrained("WRX020510/CustomModel_twitter", num_labels=3)
         tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest")
         st.session_state.sentiment_analyzer = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
-
-    # Load summarization pipeline only once using st.session_state
+    
     if 'summarizer' not in st.session_state:
-        # Load the T5 model for summarization
         st.session_state.summarizer = pipeline("summarization", model="t5-small")
 
-    # Button to analyze sentiment
-    if st.button("Analyze Sentiment"):
-        if user_input:
-            with st.spinner('Analyzing sentiment...'):
-                # Performing sentiment analysis
-                sentiment_result = st.session_state.sentiment_analyzer(user_input)
-                sentiment = sentiment_result[0]['label']
-                confidence = sentiment_result[0]['score']
-                st.write(f"Sentiment: {sentiment} with Confidence: {confidence:.2f}")
-        else:
-            st.error("Please enter some text to analyze sentiment.")
+    # Sentiment Analysis Section
+    with st.container():
+        st.write("## Sentiment Analysis")
+        if st.button("Analyze Sentiment"):
+            if user_input:
+                with st.spinner('Analyzing sentiment...'):
+                    sentiment_result = st.session_state.sentiment_analyzer(user_input)
+                    sentiment = sentiment_result[0]['label']
+                    confidence = sentiment_result[0]['score']
+                    st.write(f"Sentiment: {sentiment} with Confidence: {confidence:.2f}")
+            else:
+                st.error("Please enter some text to analyze sentiment.")
 
-    # Button to generate summary
-    if st.button("Generate Summary"):
-        if user_input:
-            with st.spinner('Generating summary...'):
-                # Generating the summary from the input text
-                summary_result = st.session_state.summarizer(user_input, max_length=130, min_length=30, do_sample=False)
-                summary = summary_result[0]['summary_text']
-                st.write("📜 Summary:")
-                st.write(summary)
-        else:
-            st.error("Please enter some text to generate a summary.")
+    # Summary Section with updated header
+    with st.container():
+        st.write("📜 Summary")
+        if st.button("Generate Summary"):
+            if user_input:
+                with st.spinner('Generating summary...'):
+                    summary_result = st.session_state.summarizer(user_input, max_length=130, min_length=30, do_sample=False)
+                    summary = summary_result[0]['summary_text']
+                    st.write(summary)
+            else:
+                st.error("Please enter some text to generate a summary.")
 
 if __name__ == "__main__":
     main()