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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()
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