Update app.py

app.py

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+import streamlit as st
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+
+# Load pre-trained model and tokenizer
+@st.cache_resource
+def load_model():
+    model_name = "mrm8488/t5-base-finetuned-question-generation-ap"
+    model = T5ForConditionalGeneration.from_pretrained(model_name)
+    tokenizer = T5Tokenizer.from_pretrained(model_name)
+    return model, tokenizer
+
+# Function to generate question
+def generate_question(text, model, tokenizer):
+    # Preprocess the input text with the "generate" task
+    input_text = f"generate question: {text}"
+    input_ids = tokenizer.encode(input_text, return_tensors="pt")
+
+    # Generate the question
+    outputs = model.generate(input_ids)
+
+    # Decode the output
+    question = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return question
+
+# Streamlit interface
+def main():
+    st.title("Question Generation with T5")
+
+    # Load the model and tokenizer
+    model, tokenizer = load_model()
+
+    # Input text from the user
+    passage = st.text_area("Enter a passage to generate a question:", "")
+
+    # Button to trigger question generation
+    if st.button("Generate Question"):
+        if passage:
+            # Generate question
+            question = generate_question(passage, model, tokenizer)
+            st.write(f"Generated Question: {question}")
+        else:
+            st.write("Please enter a passage of text to generate a question.")
+
+if __name__ == "__main__":
+    main()
+