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

# Load pre-trained model and tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("gpt2")

# Define a function to generate text
def generate_text(input_text):
    input_ids = tokenizer.encode(input_text, return_tensors="pt")

    # Generate text
    with torch.no_grad():
        output = model.generate(input_ids, max_length=100, no_repeat_ngram_size=True, do_sample=True, temperature=0.9)
    
    # Decode the generated text
    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
    
    return generated_text

# Create a Gradio interface

# Create a Gradio interface
iface = gr.Interface(
    generate_text,
    gr.Textbox(lines=5, label="Input Text"),
    "textbox",
    examples=[
        ["Once upon a time, in a land far, far away..."]
    ],
    title="GPT-2 Text Generation",
    description="Enter some text and GPT-2 will generate more!",
    theme="compact"
)
iface.launch()