Create app.py

app.py

@@ -0,0 +1,25 @@
+import streamlit as st
+from transformers import pipeline, T5Tokenizer, T5ForConditionalGeneration
+
+# Replace with your Hugging Face model repository path
+model_repo_path = 'nxmwxm/answer_generator'
+
+# Load the pipeline with your model and tokenizer
+qa_pipeline = pipeline(
+    'question-answering',
+    model=model_repo_path,
+    tokenizer=model_repo_path
+)
+
+def generate_answer(question, distractors):
+    input_text = f"Question: {question} Distractors: {' '.join(distractors)}"
+    result = qa_pipeline(question=input_text, context=input_text)
+    return result['answer']
+
+st.title('Question Answering Model')
+question = st.text_input('Enter your question:')
+distractors = st.text_input('Enter distractors (comma separated):').split(',')
+
+if st.button('Generate Answer'):
+    answer = generate_answer(question, distractors)
+    st.write('Answer:', answer)