Update app.py

app.py

@@ -1,19 +1,38 @@
 import streamlit as st
-from transformers import pipeline
+from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
 
 def main():
     # Streamlit app interface design
-    st.title("📈 Financial Text Summarization with T5")
-    st.write("Please enter the financial text below to generate a concise summary:")
+    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 = st.text_area("Text Input 📝", height=300)
+    user_input = st.text_area("📝 Text Input", height=300)
 
-    # Initialize the summarization pipeline only once using st.session_state
+    # Load sentiment analysis pipeline only once using st.session_state
+    if 'sentiment_analyzer' not in st.session_state:
+        # Load your custom sentiment analysis model
+        model = AutoModelForSequenceClassification.from_pretrained("path_to/CustomModel_twitter", num_labels=3)
+        tokenizer = AutoTokenizer.from_pretrained("path_to/CustomModel_twitter")
+        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:
-        # Initialize the pipeline with the T5 model for summarization
+        # 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.")
+
     # Button to generate summary
     if st.button("Generate Summary"):
         if user_input:
@@ -21,7 +40,7 @@ def main():
                 # 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:")
                 st.write(summary)
         else:
             st.error("Please enter some text to generate a summary.")