initial commit

app.py

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+import transformers
+import streamlit as st
+from annotated_text import annotated_text
+
+
+@st.cache(allow_output_mutation=True, show_spinner=False)
+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)

requirements.txt

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+st-annotated-text
+transformers