# from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
# import torch
# tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
# model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
# inputs = tokenizer("Hello, my dog is sad", return_tensors="pt")


# inputs = tokenizer("Hello, my dog is sad", return_tensors="pt")
# with torch.no_grad():
#     logits = model(**inputs).logits



# predicted_class_id = logits.argmax().item()

# model.config.id2label[predicted_class_id]


# outputs = model(**inputs)

# print(predicted_class_id)

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
tokenizer.pad_token = tokenizer.eos_token
# Load the model
model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")

# Example input prompt
input_text = "Ann wants to buy a new car"

# Tokenize input

inputs = tokenizer(input_text, return_tensors="pt",padding=True, truncation=True)

# Generate text
outputs = model.generate(inputs.input_ids, max_length=100, num_return_sequences=1, top_k=50, top_p=0.9, temperature=0.7,do_sample=True,eos_token_id=None, attention_mask=inputs.attention_mask)
print(model.config)
# Decode the generated text
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Generated Text:\n", generated_text)