| from transformers import T5Tokenizer, T5ForConditionalGeneration | |
| import streamlit as st | |
| import json | |
| # Load the fine-tuned model and tokenizer | |
| model_name = "." | |
| tokenizer = T5Tokenizer.from_pretrained(model_name) | |
| model = T5ForConditionalGeneration.from_pretrained(model_name) | |
| # Function to generate text based on input | |
| def generate_text(input_text): | |
| # Tokenize and generate text with sampling and different decoding parameters | |
| input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=512) | |
| generated_text = model.generate( | |
| input_ids, | |
| max_length=200, | |
| num_beams=5, | |
| temperature=0.9, # Adjust the temperature for more randomness | |
| no_repeat_ngram_size=2, | |
| top_k=50, | |
| top_p=0.95, | |
| early_stopping=True, | |
| do_sample=True, | |
| ) | |
| # Decode and return the generated text | |
| decoded_text = tokenizer.decode(generated_text[0], skip_special_tokens=True) | |
| return decoded_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("Compute"): | |
| if user_input: | |
| # Call the generate_text function with user input | |
| result = generate_text(user_input) | |
| # Display the generated text in a box with word wrap | |
| #st.markdown(f"**Generated Text:**\n\n```\n{result}\n```", unsafe_allow_html=True) | |
| #st.text(result) | |
| # Display the generated text in a div with word wrap and auto-increasing height | |
| #st.markdown(f"<div style='white-space: pre-wrap; overflow-y: auto;'>**Generated Text:**\n\n```\n{result}\n```</div>", unsafe_allow_html=True) | |
| # Display the generated text in a div with word wrap and auto-increasing width and height | |
| #st.markdown(f"<div style='white-space: pre-wrap; width: 100%; overflow: auto;'>**Generated Text:**\n\n```\n{result}\n```</div>", unsafe_allow_html=True) | |
| # Display the generated text in a text area with word wrap | |
| st.text_area("Generated Text:", result, key="generated_text") | |
| # Run the Streamlit app | |
| if __name__ == "__main__": | |
| main() | |