Update app.py

app.py

@@ -1,11 +1,30 @@
-from transformers import PegasusTokenizer, PegasusForConditionalGeneration
+import streamlit as st
+from transformers import pipeline
 
-model_name = "human-centered-summarization/financial-summarization-pegasus"
-tokenizer = PegasusTokenizer.from_pretrained(model_name, use_fast=False)
-model = PegasusForConditionalGeneration.from_pretrained(model_name)
+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:")
 
-text_to_summarize = "Your text here..."
-input_ids = tokenizer(text_to_summarize, return_tensors="pt").input_ids
-summary_ids = model.generate(input_ids, max_length=60, num_beams=5, early_stopping=True)
-summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-print(summary)
+    # User input area for text
+    user_input = st.text_area("Text Input 📝", height=300)
+
+    # Initialize the summarization pipeline only once using st.session_state
+    if 'summarizer' not in st.session_state:
+        # Initialize the pipeline with the T5 model for summarization
+        st.session_state.summarizer = pipeline("summarization", model="t5-small")
+
+    # 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.")
+
+if __name__ == "__main__":
+    main()