import gradio as gr import torch from torchtext.data.utils import get_tokenizer import numpy as np import subprocess from huggingface_hub import hf_hub_download from transformer import Transformer model_url = "https://huggingface.co/spacy/en_core_web_sm/resolve/main/en_core_web_sm-any-py3-none-any.whl" subprocess.run(["pip", "install", model_url]) MAX_LEN = 350 tokenizer = get_tokenizer('spacy', language='en_core_web_sm') vocab = torch.load(hf_hub_download(repo_id="nickgardner/chatbot", filename="vocab.pth")) vocab_token_dict = vocab.get_stoi() indices_to_tokens = vocab.get_itos() pad_token = vocab_token_dict[''] unknown_token = vocab_token_dict[''] sos_token = vocab_token_dict[''] eos_token = vocab_token_dict[''] text_pipeline = lambda x: vocab(tokenizer(x)) d_model = 512 heads = 8 N = 6 src_vocab = len(vocab) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = Transformer(len(vocab), len(vocab), d_model, N, heads).to(device) model.load_state_dict(torch.load(hf_hub_download(repo_id="nickgardner/chatbot", filename="alpaca_train_400_epoch.pt"), map_location=device)) model.eval() def respond(search_type, input): model.eval() src = torch.tensor(text_pipeline(input), dtype=torch.int64).unsqueeze(0).to(device) src_mask = ((src != pad_token) & (src != unknown_token)).unsqueeze(-2).to(device) e_outputs = model.encoder(src, src_mask) outputs = torch.zeros(MAX_LEN).type_as(src.data).to(device) outputs[0] = torch.tensor([vocab.get_stoi()['']]) for i in range(1, MAX_LEN): trg_mask = np.triu(np.ones([1, i, i]), k=1).astype('uint8') trg_mask = torch.autograd.Variable(torch.from_numpy(trg_mask) == 0).to(device) out = model.out(model.decoder(outputs[:i].unsqueeze(0), e_outputs, src_mask, trg_mask)) if search_type == "Greedy": out = torch.nn.functional.softmax(out, dim=-1) val, ix = out[:, -1].data.topk(1) outputs[i] = ix[0][0] if ix[0][0] == vocab_token_dict['']: break else: out = torch.nn.functional.softmax(out, dim=-1)[:, -1].squeeze().detach().numpy() ix = np.random.choice(np.arange(len(out)), 1, p=out) outputs[i] = ix[0] if ix[0] == vocab_token_dict['']: break return ' '.join([indices_to_tokens[ix] for ix in outputs[1:i]]) iface = gr.Interface(fn=respond, inputs=[gr.Radio(["Greedy", "Probabilistic"], value="Greedy", label="Search Type"), "text"], outputs="text") iface.launch()