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Co-authored-by: Anna Filina <annafilina@users.noreply.huggingface.co>

answers.py

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+import streamlit as st
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+
+model_name = "deepset/roberta-base-squad2"
+model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+def get_answer(context, question):
+    nlp = pipeline('question-answering', model=model, tokenizer=tokenizer)
+    QA_input = {'question': question, 'context': context}
+    res = nlp(QA_input)
+    answer = res['answer']
+    return answer
+
+def main():
+    st.title("Question Answering App")
+    st.markdown("Enter the context and question, then click on 'Get Answer' to retrieve the answer.")
+
+    
+    context = st.text_area("Context", "Enter the context here...")
+    question = st.text_input("Question", "Enter the question here...")
+
+    
+    if st.button("Get Answer"):
+        
+        if context.strip() == "" or question.strip() == "":
+            st.warning("Please enter the context and question.")
+        else:
+            
+            answer = get_answer(context, question)
+            st.success(f"Answer: {answer}")
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit==1.21.0
+transformers==4.11.2
+torch==2.0.1