Create app.py

app.py

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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"
+
+@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).")