import gradio as gr
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel

# Initialize tokenizer and model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')

# Create a function to generate text
def generate_text(input_text):
    # Encode the input text
    input_ids = tokenizer.encode(input_text, return_tensors='pt')
    # Generate the output text
    output_ids = model.generate(input_ids, max_length=50, do_sample=True, top_k=50, top_p=0.95, num_return_sequences=1)
    # Decode the output text
    output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
    return output_text

# Create the interface
gr.Interface(fn=generate_text, inputs="text", outputs="text", title="Chat GPT").launch()