Update app.py

app.py

@@ -1,23 +1,25 @@
 import streamlit as st
-from transformers import pipeline, AutoModelForQuestionAnswering, AutoTokenizer
+from transformers import pipeline, AutoTokenizer, AutoModelForQuestionAnswering
 
 @st.cache(allow_output_mutation=True)
 def load_qa_model():
-    model_name = "mrm8488/mobilebert-uncased-finetuned-squadv2"
-    model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+    model_name = "google/mobilebert-uncased"
     tokenizer = AutoTokenizer.from_pretrained(model_name)
-    qa = pipeline("question-answering", model=model, tokenizer=tokenizer)
-    return qa
+    model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+    qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer)
+    return qa_pipeline
 
 qa = load_qa_model()
+
 st.title("Ask Questions about your Text")
 sentence = st.text_area('Please paste your article :', height=30)
 question = st.text_input("Questions from this article?")
 button = st.button("Get me Answers")
-max = st.sidebar.slider('Select max', 50, 500, step=10, value=150)
-min = st.sidebar.slider('Select min', 10, 450, step=10, value=50)
+max_seq_length = st.sidebar.slider('Select max sequence length', 50, 500, step=10, value=150)
 do_sample = st.sidebar.checkbox("Do sample", value=False)
+
 with st.spinner("Discovering Answers.."):
     if button and sentence:
         answers = qa(question=question, context=sentence)
-        st.write(answers['answer'])
+        st.write("Answer:", answers['answer'])
+        st.write("Score:", answers['score'])