qa.py

from transformers import pipeline
import streamlit as st


# def que():
#     question = st.text_input("ASk me a question")

#     oracle = pipeline(task= "question-answering",model="deepset/roberta-base-squad2")
#     oracle(question="Where do I live?", context="My name is Wolfgang and I live in Berlin")


def question_answering(question, context):
    """Answers a question given a context."""

    # Load the question answering model.


    qa_model = pipeline("question-answering")


    # Prepare the inputs for the model.
    inputs = {
        "question": question,
        "context": context,
    }

    # Get the answer from the model.
    output = qa_model(**inputs)
    answer = output["answer_start"]

    # Return the answer.
    return context[answer : answer + output["answer_length"]]


    if __name__ == "__main__": 
        # Get the question and context.
        question = "What is the capital of France?"
        context = "The capital of France is Paris."

        # Get the answer.
        answer = question_answering(question, context)

        # Print the answer.
        print(answer)