app.py

import gradio as gr
import random
import torch
import pathlib

from src.model import GPTModel
from src.inference import generate as generate_text
from src.utils import vocab_size

batch_size = 64
block_size = 256
max_iters = 5000
eval_interval = 500
learning_rate = 3e-4
device = "cuda:1" if torch.cuda.is_available() else "cpu"
eval_iters = 200
n_embeds = 384
n_heads = 6
n_layers = 6
dropout = 0.2


def load_model():
    model_ckpt = torch.load("checkpoints/model.pth", map_location=device)
    model = GPTModel(
        vocab_size, n_embeds, block_size, n_heads, n_layers, dropout, device
    )
    model.load_state_dict(model_ckpt.state_dict())
    return model


model = load_model()


def generate(prompt, max_new_tokens):
    prompt = prompt.strip()
    out = generate_text(prompt, model, block_size, max_new_tokens, device)
    return {gpt_output: out}


with gr.Blocks() as app:
    gr.Markdown("## ERA Session21 - GPT from scratch")
    gr.Markdown(
        """This is an implementation of GPT [Let's build GPT: from scratch, in code, spelled out.](https://www.youtube.com/watch?v=kCc8FmEb1nY&t=2s) by Andrej Karpathy.
        
        Please find the source code and training details [here](https://github.com/RaviNaik/ERA-SESSION21).
        
        Dataset used to train: [tinyshakespeare](https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt).
        """
    )
    with gr.Row():
        with gr.Column():
            prompt_box = gr.Textbox(label="Initial Prompt", interactive=True)
            max_new_tokens = gr.Slider(
                minimum=10,
                maximum=2500,
                value=100,
                step=10,
                label="Select Number of Tokens to be Generated",
                interactive=True,
            )
            submit_btn = gr.Button(value="Generate")

        with gr.Column():
            gpt_output = gr.TextArea(
                label="Text Generated by GPT",
                show_label=True,
                max_lines=100,
                interactive=False,
            )

        submit_btn.click(
            generate,
            inputs=[prompt_box, max_new_tokens],
            outputs=[gpt_output],
        )

app.launch()