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| import transformers | |
| import streamlit as st | |
| from annotated_text import annotated_text | |
| def get_pipe(): | |
| tokenizer = transformers.AutoTokenizer.from_pretrained("deepset/roberta-base-squad2") | |
| model = transformers.AutoModelForQuestionAnswering.from_pretrained("deepset/roberta-base-squad2") | |
| pipe = transformers.pipeline("question-answering", model=model, tokenizer=tokenizer) | |
| return pipe | |
| def parse_context(context, prediction): | |
| parsed_context = [] | |
| parsed_context.append(context[:prediction["start"]]) | |
| parsed_context.append((prediction["answer"], "ANSWER", "#afa")) | |
| parsed_context.append(context[prediction["end"]:]) | |
| return parsed_context | |
| st.set_page_config(page_title="Question Answering") | |
| st.title("Question Answering") | |
| st.write("Enter context and a question and press 'Predict' to extract the answer from the context.") | |
| default_context = "My name is Wolfgang and I live in Berlin." | |
| default_question = "What is my name?" | |
| context = st.text_area("Enter context here:", value=default_context) | |
| question = st.text_input("Enter question here:", value=default_question) | |
| submit = st.button('Predict') | |
| with st.spinner("Loading model..."): | |
| pipe = get_pipe() | |
| if (submit and len(context.strip()) > 0 and len(question.strip()) > 0): | |
| prediction = pipe(question, context) | |
| parsed_context = parse_context(context, prediction) | |
| st.header("Prediction:") | |
| annotated_text(*parsed_context) | |
| st.header('Raw values:') | |
| st.json(prediction) | |