app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the model
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelForCausalLM.from_pretrained("gpt2")

def generate_text(text_input, max_length, temperature, top_k):
    input_ids = tokenizer.encode(text_input, return_tensors='pt')
    output = model.generate(input_ids, max_length=max_length, temperature=temperature, top_k=top_k, do_sample=True)
    response = tokenizer.decode(output[0], skip_special_tokens=True)
    return response

# Create a Gradio interface
iface = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.Textbox(lines=2, placeholder="Enter a prompt..."),
        gr.Slider(minimum=1, maximum=500, step=1, value=100, label='Max Length'),
        gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.8, label='Temperature'),
        gr.Slider(minimum=100, maximum=1000, step=50, value=100, label='Top K')
    ],
    outputs='text',
    title="GPT-2 Text Generator"
)

# Run the interface
iface.launch()