Update app.py

app.py

@@ -7,21 +7,17 @@ def load_qa_model():
     model = AutoModelForQuestionAnswering.from_pretrained(model_name)
     tokenizer = AutoTokenizer.from_pretrained(model_name)
     qa = pipeline("question-answering", model=model, tokenizer=tokenizer)
-    text_generator = pipeline("text-generation")
-    return qa, text_generator
+    return qa
 
-qa, text_generator = load_qa_model()
+qa = load_qa_model()
 st.title("Ask Questions about your Text")
 sentence = st.text_area('Please paste your article :', height=30)
-num_questions = st.number_input("Number of questions to generate:", min_value=1, max_value=10, value=3, step=1)
-num_answers = st.number_input("Number of answers per question:", min_value=1, max_value=5, value=1, step=1)
-button = st.button("Generate Questions and Answers")
-
-with st.spinner("Generating Questions and Answers.."):
+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)
+do_sample = st.sidebar.checkbox("Do sample", value=False)
+with st.spinner("Discovering Answers.."):
     if button and sentence:
-        generated_questions = text_generator(sentence, max_length=100, num_return_sequences=num_questions)
-        for question_index, question_output in enumerate(generated_questions):
-            st.subheader(f"Question {question_index + 1}: {question_output['generated_text']}")
-            answers = qa(question=question_output['generated_text'], context=sentence, topk=num_answers)
-            for answer_index, answer in enumerate(answers):
-                st.write(f"Answer {answer_index + 1}: {answer['answer']}")
+        answers = qa(question=question, context=sentence)
+        st.write(answers['answer'])