# from dotenv import load_dotenv
# from langchain import HuggingFaceHub, LLMChain
# from langchain import PromptTemplates
# import gradio

# load_dotenv()
# os.getenv('HF_API')

# hub_llm = HuggingFaceHub(repo_id='facebook/blenderbot-400M-distill')

# prompt = prompt_templates(
#     input_variable = ["question"],
#     template = "Answer is: {question}"
# )

# hub_chain = LLMChain(prompt=prompt, llm=hub_llm, verbose=True)





# Sample code for AI language model interaction
from transformers import GPT2Tokenizer, GPT2LMHeadModel
import gradio


def simptok(data):
    # Load pre-trained model and tokenizer (using the transformers library)
    model_name = "gpt2"
    tokenizer = GPT2Tokenizer.from_pretrained(model_name)
    model = GPT2LMHeadModel.from_pretrained(model_name)
    
    # User input
    user_input = data
    
    # Tokenize input
    input_ids = tokenizer.encode(user_input, return_tensors="pt")
    
    # Generate response
    output = model.generate(input_ids, max_length=50, num_return_sequences=1)
    response = tokenizer.decode(output[0], skip_special_tokens=True)
    
    print(response)


def responsenew(data):
    return simptok(data)


gradio_interface = gradio.Interface(
  fn = responsenew,
  inputs = "text",
  outputs = "text"
)
gradio_interface.launch()