Spaces:
Sleeping
Sleeping
| # Step 2: Import necessary libraries | |
| import streamlit as st | |
| from transformers import pipeline | |
| # Step 3: Load pre-trained model and tokenizer | |
| qa_pipeline = pipeline("question-answering", model="distilbert-base-cased-distilled-squad", tokenizer="distilbert-base-cased") | |
| # Step 4: Define Streamlit app | |
| def main(): | |
| # Set app title | |
| st.title("Question Answering with ALBERT") | |
| # Input context | |
| context = st.text_area("Enter the context:") | |
| # Input questions | |
| num_questions = st.number_input("Enter the number of questions:", min_value=1, max_value=10, step=1) | |
| questions = [st.text_input(f"Enter question {i + 1}:") for i in range(num_questions)] | |
| # Ask questions and display answers | |
| if st.button("Get Answers"): | |
| for i, question in enumerate(questions): | |
| if question: | |
| answer = qa_pipeline({"context": context, "question": question}) | |
| st.write(f"Answer {i + 1}: {answer['answer']}") | |
| # Step 5: Run Streamlit app | |
| if __name__ == "__main__": | |
| main() |