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"

@st.cache_resource
def load_pipeline():
    return pipeline("question-answering", 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 based on biomedical content!")

# User inputs
context = st.text_area("Enter the biomedical context (e.g., PubMed abstract):", height=200)
question = st.text_input("Enter your question:")

# Button to get the answer
if st.button("Get Answer"):
    if context.strip() and question.strip():
        with st.spinner("Finding the answer..."):
            result = qa_pipeline(question=question, context=context)
            st.success(f"Answer: {result['answer']}")
            st.write(f"Confidence: {result['score']:.2f}")
    else:
        st.warning("Please provide both context and a question.")

# Footer
st.markdown("---")
st.markdown("Powered by **PubMed BERT** fine-tuned by [Alaaeldin](https://huggingface.co/Alaaeldin).")