app.py

import gradio as gr
import torch
import tiktoken
from train_shakespeare import GPT, GPTConfig, generate, get_autocast_device

# Initialize model and tokenizer
def init_model():
    model = GPT(GPTConfig())
    checkpoint = torch.load('model/best_model.pt', map_location='cpu')
    model.load_state_dict(checkpoint['model_state_dict'])
    model.eval()
    return model

enc = tiktoken.get_encoding("gpt2")
model = init_model()

def generate_text(prompt, max_length=500, temperature=0.8, top_k=40):
    # Tokenize input
    input_ids = torch.tensor(enc.encode(prompt)).unsqueeze(0)
    
    # Generate text
    with torch.no_grad():
        output_ids = generate(
            model=model,
            idx=input_ids,
            max_new_tokens=max_length,
            temperature=temperature,
            top_k=top_k,
            device='cpu'  # Force CPU for Spaces
        )
    
    # Decode and return generated text
    return enc.decode(output_ids[0].tolist())

# Create Gradio interface
demo = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.Textbox(label="Enter your prompt", placeholder="Start your text here..."),
        gr.Slider(minimum=10, maximum=1000, value=500, step=10, label="Maximum Length"),
        gr.Slider(minimum=0.1, maximum=2.0, value=0.8, step=0.1, label="Temperature"),
        gr.Slider(minimum=1, maximum=100, value=40, step=1, label="Top-k")
    ],
    outputs=gr.Textbox(label="Generated Text"),
    title="Shakespeare-style Text Generator",
    description="Generate Shakespeare-style text using a fine-tuned GPT-2 model",
    examples=[
        ["First Citizen:", 500, 0.8, 40],
        ["To be, or not to be,", 500, 0.8, 40],
        ["Friends, Romans, countrymen,", 500, 0.8, 40]
    ]
)

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