Update pages/answers_2.py

pages/answers_2.py

@@ -0,0 +1,45 @@
+import streamlit as st
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+from streamlit_extras.let_it_rain import rain
+from data/context_examples.py import context, questions
+
+rain(
+    emoji="❔",
+    font_size=54,
+    falling_speed=5,
+    animation_length="infinite",
+)
+
+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 :robot_face:")
+    st.divider()
+    st.markdown("### **Enter the context and question, then click on ':blue[Get Answer]' to retrieve the answer:**")
+
+    selected_index = st.selectbox("Select an index:", range(len(contexts)))
+    context = st.text_area("**:blue[Context]**", contexts[selected_index])
+    question = st.text_input("**:blue[Question]**", questions[selected_index])
+
+    
+    if st.button(":blue[**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()