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import streamlit as st
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from transformers import T5ForConditionalGeneration, T5Tokenizer
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from transformers import AutoTokenizer, AutoModelForCausalLM
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def load_model(model_name):
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if model_name == "T5":
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model = T5ForConditionalGeneration.from_pretrained('google/flan-t5-base')
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tokenizer = T5Tokenizer.from_pretrained('google/flan-t5-base')
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return model, tokenizer
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if model_name == "Llama3":
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model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B")
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tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B")
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return model, tokenizer
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if model_name == "Llama3-Instruct":
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tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
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model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
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return model, tokenizer
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else:
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st.error(f"Model {model_name} not available.")
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return None, None
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def generate_question(model,tokenizer,context):
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input_text = 'Generate a question from this: ' + context
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input_ids = tokenizer(input_text, return_tensors='pt').input_ids
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outputs = model.generate(input_ids,max_length=512)
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output_text = tokenizer.decode(outputs[0][1:len(outputs[0])-1])
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return output_text
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def main():
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st.title("Question Generation From Given Text")
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context = st.text_area("Enter text","Laughter is the best medicine.")
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st.write("Select a model and provide the text to generate questions.")
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model_choice = st.selectbox("Select a model", ["T5", "Llama3", "Llama3-Instruct"])
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if st.button("Generate Questions"):
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model, tokenizer = load_model(model_choice)
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if model and tokenizer:
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questions = generate_question(model, tokenizer, context)
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st.write("Generated Question:")
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st.write(questions)
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else:
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st.error("Model loading failed.")
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if __name__ == '__main__':
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main() |
