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from transformers import GPT2Tokenizer, GPT2LMHeadModel
# Load the original GPT-2 tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
# Load your fine-tuned model
model = GPT2LMHeadModel.from_pretrained("./fine_tuned_model")
# Save the tokenizer along with your fine-tuned model
model.save_pretrained("./fine_tuned_model")
tokenizer.save_pretrained("./fine_tuned_model")
while True:
try:
# Prompt user for input
prompt_text = input("You: ")
# Tokenize the prompt text
input_ids = tokenizer.encode(prompt_text, return_tensors="pt")
# Generate response
output = model.generate(input_ids, max_length=100, num_return_sequences=1, temperature=0.7, do_sample=True)
# Decode and print the generated response
generated_response = tokenizer.decode(output[0], skip_special_tokens=True)
print("Bot:", generated_response)
except Exception as e:
print("An error occurred:", str(e))
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