app.py

import streamlit as st
from transformers import BartForConditionalGeneration, BartTokenizer

# Load the model and tokenizer from the local directory
model_path = "disilbart-med-summary" # Replace with the actual path
tokenizer = BartTokenizer.from_pretrained(model_path)
model = BartForConditionalGeneration.from_pretrained(model_path)

# Function to generate summary based on input
def generate_summary(input_text):
 # Tokenize the input text
 input_ids = tokenizer.encode(input_text, return_tensors="pt")

 # Generate summary
 summary_ids = model.generate(input_ids, max_length=4000, num_beams=4, no_repeat_ngram_size=2)

 # Decode the summary
 summary_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
 return summary_text

# Streamlit app
def main():
 # Apply custom styling for the title
 st.markdown("<h3 style='text-align: center; color: #333;'>Medical Summary - Text Generation</h3>", unsafe_allow_html=True)

 # Textbox for user input
 user_input = st.text_area("Enter Text:", "")

 # Button to trigger text generation
 if st.button("Generate Summary"):
 if user_input:
 # Call the generate_summary function with user input
 result = generate_summary(user_input)

 # Display the generated summary in a text area with word wrap
 st.text_area("Generated Summary:", result, key="generated_summary")

# Run the Streamlit app
if __name__ == "__main__":
 main()