from transformers import T5Tokenizer, T5ForConditionalGeneration

# Load the model and tokenizer
model_name = "t5-base"  # lightweight and works offline
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)

def generate_mcqs(text, num_questions=3):
    input_text = f"generate questions: {text}"
    input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)

    outputs = model.generate(
        input_ids=input_ids,
        max_length=256,
        num_return_sequences=1,
        temperature=0.7
    )

    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return decoded.strip()

# 🔽 TEST EXAMPLE 🔽
if __name__ == "__main__":
    sample = """
    The process of photosynthesis allows plants to convert sunlight, water, and carbon dioxide into food. This process takes place in the chloroplasts and releases oxygen as a byproduct.
    """
    print("📘 Quiz Output:\n")
    print(generate_mcqs(sample))