app.py

import gradio as gr
from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load the GPT-2 model and tokenizer
model_name = "gpt2-xl"
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)

def generate_text(prompt, max_tokens=100, temperature=0.7):
    input_ids = tokenizer.encode(prompt, return_tensors='pt')
    
    # Generate text
    output = model.generate(input_ids, max_length=max_tokens, temperature=temperature, num_return_sequences=1)
    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
    
    return generated_text

# Create a Gradio interface
demo = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.Textbox(label="Input Prompt"),
        gr.Slider(minimum=1, maximum=2048, value=100, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature"),
    ],
    outputs="text",
    title="GPT-2 Text Generation",
    description="Enter a prompt to generate text using the GPT-2 model."
)

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