Delete pages/answers_2.py

pages/answers_2.py

@@ -1,45 +0,0 @@
-import streamlit as st
-from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
-from streamlit_extras.let_it_rain import rain
-from 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()