"""
import torch 
import tensorflow as tf 
import flax
import gradio as gr
from transformers import pipeline

sentiment_pipeline= pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")


# texts = ["Hugging face? weired, but memorable.", "I am despirate"]

# results = sentiment_pipeline(texts)

# for text, results in zip(texts, results):
    # print(f"Text: {text}")
    # print(f"Sentiment: {result['label']}, Score: {result['score']:.4f}\n")
  
    
def predict_sentiment(text):
    result = sentiment_pipeline(text)
    return result[0]['label'], result[0]['score']

iface = gr.Interface(fn=predict_sentiment, inputs="text", outputs = ["label","number"])

if __name__ == "__main__": 
    iface.launch()

"""


from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

torch_device = "cuda" if torch.cuda.is_available() else "cpu"

tokenizer = AutoTokenizer.from_pretrained("gpt2")

model = AutoModelForCausalLM.from_pretrained("gpt2", pad_token_id=tokenizer.eos_token_id).to(torch_device)

model_inputs = tokenizer('An explanation of Linear Regression: ', return_tensors='pt').to(torch_device)

output = model.generate(**model_inputs, max_new_tokens=50, do_sample=True, top_p=0.92, top_k=0, temperature=0.6)

print(tokenizer.decode(output[0],skip_special_tokens=True))