app.py

import os
import pickle

import gradio as gr
import torch
from model import GPT, GPTConfig

ckpt_path = 'model/ckpt.pt'
meta_path = 'model/meta.pkl'
seed = 1337
device = 'cpu'

torch.manual_seed(seed)

# Load the model and meta data
checkpoint = torch.load(ckpt_path, map_location=device)
gptconf = GPTConfig(**checkpoint['model_args'])
model = GPT(gptconf)
state_dict = checkpoint['model']
unwanted_prefix = '_orig_mod.'
for k, v in list(state_dict.items()):
    if k.startswith(unwanted_prefix):
        state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
model.load_state_dict(state_dict)

model.eval()
model.to(device)

with open(meta_path, 'rb') as f:
    meta = pickle.load(f)
stoi, itos = meta['stoi'], meta['itos']
encode = lambda s: [stoi[c] for c in s]
decode = lambda l: ''.join([itos[i] for i in l])

# Define the function for generating text
def generate_text(start, temperature, max_new_tokens):
    start_ids = encode(start)
    x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...])

    # Generate text
    with torch.no_grad():
        y = model.generate(x, max_new_tokens, temperature=temperature)
        generated_text = decode(y[0].tolist())

    return generated_text

# Create a Gradio interface with sliders
examples = [['sport', 0.7, 200], ['lord', 1.2, 300]]
iface = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.Textbox(label="Starting Prompt"),
        gr.Slider(minimum=0.1, maximum=4, step=0.1, label="Temperature"),
        gr.Slider(minimum=100, maximum=1000, step=50, label="Max New Tokens"),
    ],
    outputs=gr.Textbox(label="Generated Text"),
    examples = examples
)

iface.launch()