from transformers import GPT2LMHeadModel, GPT2Tokenizer

Load pre-trained model and tokenizer

model_name = "gpt2-medium" # You can choose any model size here tokenizer = GPT2Tokenizer.from_pretrained(model_name) model = GPT2LMHeadModel.from_pretrained(model_name)

def chat_with_gpt(prompt): # Tokenize input text input_ids = tokenizer.encode(prompt, return_tensors="pt")

# Generate response
with torch.no_grad():
    output_ids = model.generate(input_ids, max_length=100, num_return_sequences=1)

# Decode and return response
response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
return response

Example usage

user_input = input("You: ") response = chat_with_gpt(user_input) print("ChatGPT:", response)

Built With

• phyton