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

# Load the model and tokenizer
model = GPT2LMHeadModel.from_pretrained("samwell/SamGPT")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")  # Assuming you used the GPT-2 tokenizer

def generate_text(prompt, max_length):
    input_ids = tokenizer.encode(prompt, return_tensors="pt")
    output = model.generate(input_ids, max_length=max_length, num_return_sequences=1, no_repeat_ngram_size=2)
    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
    return generated_text

iface = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.Textbox(label="Prompt"),
        gr.Slider(minimum=10, maximum=200, value=50, step=1, label="Max Length")
    ],
    outputs=gr.Textbox(label="Generated Text"),
    title="SamGPT Text Generation",
    description="Enter a prompt to generate text with SamGPT."
)

iface.launch()