app.py

import gradio as gr
import torch
import numpy as np
from model import Transformer
from transformers import PreTrainedTokenizerFast
from tokenizers import Tokenizer
from utils import (
    DEVICE,
    DROPOUT,
    NUM_EMBED,
    NUM_HEAD,
    NUM_LAYER,
    BLOCK_SIZE,
    encode,
    decode
)

tokenizer = PreTrainedTokenizerFast(tokenizer_object=Tokenizer.from_file("BPE.json"))
vocab_size = tokenizer.vocab_size

# train a new model
model = Transformer(
    vocab_size=vocab_size,
    num_embed=NUM_EMBED,
    block_size=BLOCK_SIZE,
    num_heads=NUM_HEAD,
    num_layers=NUM_LAYER,
    dropout=DROPOUT
)
# load model to GPU if available
m = model.to(DEVICE)
# print the number of parameters in the model

m.load_state_dict(torch.load("base_model.pth", map_location=torch.device(DEVICE)))
m.eval()

#print(
#    "Model with {:.2f}M parameters".format(sum(p.numel() for p in m.parameters()) / 1e6)
#)
def model_generate(text, number_of_new_token, temperature, top_p):
    print(text)
    context = encode(str(text), tokenizer).unsqueeze(0).to(DEVICE)

    gen = decode(enc_sec=m.generate(idx=context, max_new_tokens=number_of_new_token, temperature = temperature, top_p = top_p)[0], tokenizer=tokenizer)
    print(gen)
    return gen

iface = gr.Interface(fn=model_generate, inputs=["text", gr.Slider(10, 1000), gr.Slider(0, 1, value=0.7, step = 0.05), gr.Slider(0, 1, value=0.95, step = 0.05)], outputs="text")
iface.launch()