Create app.py

app.py

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+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()