Update app.py

app.py

@@ -1,7 +1,6 @@
 import streamlit as st
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 import torch
-from examples import dialogue_examples
 
 def generate_summary(model, tokenizer, dialogue):
     # Tokenize input dialogue
@@ -17,12 +16,9 @@ def generate_summary(model, tokenizer, dialogue):
 
 st.set_page_config(
     page_title="Dialogue Summarizer App",
-    page_icon="ale.png",  # You can set your own emoji or use an image URL
+    page_icon="ale.png",
 )
 
-#logo_path = "ale.png"
-#logo_html = f'<div style="text-align:center;"><img src="{logo_path}" width="200"></div>'
-#st.markdown(logo_html, unsafe_allow_html=True)
 
 # Display the app name below the logo
 st.title("Dialogue Summarizer App")
@@ -30,32 +26,31 @@ st.title("Dialogue Summarizer App")
 st.info("\n🖥️ Note: This application is running on CPU. Please be patient ⏳.")
 st.markdown("This app summarizes dialogues. Enter a short dialogue in the text area. For best results, keep the dialogues at least a few sentences. You can also use the examples provided at the bottom of the page.")
 
+
 # Create two columns layout using st.columns
 col1, col2 = st.columns(2)
 
 # User input on the left side with increased height
-user_input = col1.text_area("Enter a Dialogue:", height=300)
-
-# Summary textbox on the right side with initial value (read-only)
-initial_summary = "Generated Summary will appear here."
-generated_summary = col2.text_area("Summary:", value=initial_summary, height=300, key="summary")
+user_input = col1.text_area("Enter the dialog:", height=300)
 
 # Add "Summarize" and "Clear" buttons
 summarize_button = col1.button("Summarize")
+clear_button = col1.button("Clear")
 
-# If "Summarize" button is clicked and there is user input, generate and display summary in the summary textbox
+# If "Clear" button is clicked, clear the user input
+if clear_button:
+    user_input = ""
+
+# If "Summarize" button is clicked and there is user input, generate and display summary on the right side
 if summarize_button and user_input:
     # Load pre-trained Pegasus model and tokenizer
     model_name = "ale-dp/pegasus-finetuned-dialog-summarizer"
-    tokenizer = AutoTokenizer.from_pretrained("google/pegasus-cnn_dailymail")
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
     model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 
     # Generate summary
     summary = generate_summary(model, tokenizer, user_input)
 
-    # Update the summary textbox with the generated summary
-    generated_summary.text(summary)
-
-st.markdown("**Dialogue examples:**")
-for idx, example in enumerate(dialogue_examples, 1):
-    st.write(f"Example {idx}:\n{example}")
+    # Display the generated summary on the right side
+    col2.subheader("Generated Summary:")
+    col2.write(summary)