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| import streamlit as st | |
| from transformers import pipeline | |
| import os | |
| # Load the Hugging Face access token from secrets | |
| access_token = os.getenv("HF_TOKEN") # Token is securely retrieved from secrets | |
| # Model name | |
| MODEL_NAME = "Alaaeldin/pubmedBERT-demo" | |
| def load_pipeline(): | |
| # Load the model and tokenizer with authentication | |
| return pipeline("text-generation", model=MODEL_NAME, tokenizer=MODEL_NAME, use_auth_token=access_token) | |
| # Load the pipeline | |
| qa_pipeline = load_pipeline() | |
| # Streamlit app UI | |
| st.title("PubMed BERT Q&A App") | |
| st.write("Ask questions directly based on the model's training!") | |
| # User input for the question | |
| question = st.text_input("Enter your question:") | |
| # Button to get the answer | |
| if st.button("Get Answer"): | |
| if question.strip(): | |
| with st.spinner("Generating the answer..."): | |
| result = qa_pipeline(question, max_length=100, num_return_sequences=1) | |
| st.success(f"Answer: {result[0]['generated_text']}") | |
| else: | |
| st.warning("Please enter a question.") | |
| # Footer | |
| st.markdown("---") | |
| st.markdown("Powered by **PubMed BERT** fine-tuned by [Alaaeldin](https://huggingface.co/Alaaeldin).") | |