Update app.py

app.py

@@ -1,22 +1,48 @@
 import streamlit as st
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 import torch
-import sys
 
+def generate_summary(model, tokenizer, dialogue):
+    # Tokenize input dialogue
+    inputs = tokenizer(dialogue, return_tensors="pt", max_length=1024, truncation=True)
+
+    # Generate summary
+    with torch.no_grad():
+        summary_ids = model.generate(inputs["input_ids"], max_length=150, length_penalty=0.8, num_beams=4)
+
+    # Decode and return the summary
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
+    return summary
+
+# Set page title and favicon
+st.set_page_config(
+    page_title="Dialog Summarizer App",
+    page_icon=":memo:",  # You can set your own emoji or use an image URL
+)
+
+# Add a logo at the top middle of the app
+logo_path = "path/to/your/logo.png"  # Replace with the path to your logo image file
+logo_html = f'<div style="text-align:center;"><img src="ale.png" width="200"></div>'
+st.markdown(logo_html, unsafe_allow_html=True)
+
+# Display the app name below the logo
 st.title("Dialog Summarizer App")
 
-# User input
-user_input = st.text_area("Enter the dialog:")
+# Create two columns layout
+col1, col2 = st.beta_columns(2)
+
+# User input on the left side
+user_input = col1.text_area("Enter the dialog:")
 
 # Add "Summarize" and "Clear" buttons
-summarize_button = st.button("Summarize")
-clear_button = st.button("Clear")
+summarize_button = col1.button("Summarize")
+clear_button = col1.button("Clear")
 
 # If "Clear" button is clicked, clear the user input
 if clear_button:
     user_input = ""
 
-# "Summarize" button and user input, generate and display summary
+# 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"
@@ -26,18 +52,6 @@ if summarize_button and user_input:
     # Generate summary
     summary = generate_summary(model, tokenizer, user_input)
 
-    # Display the generated summary
-    st.subheader("Generated Summary:")
-    st.write(summary)
-
-def generate_summary(model, tokenizer, dialogue):
-    # Tokenize input dialogue
-    inputs = tokenizer(dialogue, return_tensors="pt", max_length=1024, truncation=True)
-
-    # Generate summary
-    with torch.no_grad():
-        summary_ids = model.generate(inputs["input_ids"], max_length=150, length_penalty=0.8, num_beams=4)
-
-    # Decode and return the summary
-    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
-    return summary
+    # Display the generated summary on the right side
+    col2.subheader("Generated Summary:")
+    col2.write(summary)