Create app.py

app.py

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+%%writefile app.py
+import streamlit as st
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+# Load the model and tokenizer
+model = T5ForConditionalGeneration.from_pretrained('/content/fine_tuned_model')
+tokenizer = T5Tokenizer.from_pretrained('/content/fine_tuned_model')
+
+def generate_answer(question, distractors):
+    input_text = f"Question: {question} Distractors: {' '.join(distractors)}"
+    inputs = tokenizer(input_text, return_tensors='pt')
+    output = model.generate(inputs['input_ids'], attention_mask=inputs['attention_mask'])
+    decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)
+    return decoded_output
+
+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)