| import streamlit as st | |
| from transformers import pipeline | |
| pipe = pipeline("text2text-generation", model="google/flan-t5-base") | |
| def generate_answer(question): | |
| """Generates an answer to a question using the T5 language model.""" | |
| answer = pipe(question, max_length=100, num_return_sequences=1)[0] | |
| return answer | |
| st.markdown( | |
| f""" | |
| <style> | |
| body {{ | |
| background-image: url("BK.jpg"); | |
| background-size: cover; | |
| }} | |
| </style> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| st.title("Question Answering App") | |
| question = st.text_input("Ask me a question:") | |
| if question: | |
| answer = generate_answer(question) | |
| st.markdown(f"**Answer:** {answer}") | |
| else: | |
| st.markdown("Please ask me a question.") | |