from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load pre-trained GPT-2 model and tokenizer
model_name = "gpt2"  # You can use other GPT-2 variants like "gpt2-medium" or "gpt2-large"
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)

# Set the model to evaluation mode
model.eval()

def generate_recipe(prompt, max_length=150):
    # Tokenize the input prompt
    input_ids = tokenizer.encode(prompt, return_tensors="pt")

    # Generate text based on the input prompt
    output = model.generate(input_ids, max_length=max_length, num_beams=5, no_repeat_ngram_size=2, top_k=50, top_p=0.95, temperature=0.7)

    # Decode the generated output
    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
    
    return generated_text

# Take user input
user_input = input("Enter a cooking prompt to generate a recipe: ")
generated_recipe = generate_recipe(user_input)
print("Generated Recipe:")
print(generated_recipe)