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| import unicodedata | |
| import string | |
| import gradio as gr | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| all_letters = string.ascii_letters + " .,;'" | |
| n_letters = len(all_letters) | |
| all_categories = ['Arabic','Chinese','Czech','Dutch','English','French','German','Greek', | |
| 'Irish','Italian','Japanese','Korean','Polish','Portuguese','Russian','Scottish', | |
| 'Spanish','Vietnamese'] | |
| # Turn a Unicode string to plain ASCII, thanks to https://stackoverflow.com/a/518232/2809427 | |
| def unicodeToAscii(s): | |
| return ''.join( | |
| c for c in unicodedata.normalize('NFD', s) | |
| if unicodedata.category(c) != 'Mn' | |
| and c in all_letters | |
| ) | |
| # Find letter index from all_letters, e.g. "a" = 0 | |
| def letterToIndex(letter): | |
| return all_letters.find(letter) | |
| # Turn a line into a <line_length x 1 x n_letters>, | |
| # or an array of one-hot letter vectors | |
| def lineToTensor(line): | |
| tensor = torch.zeros(len(line), 1, n_letters) | |
| for li, letter in enumerate(line): | |
| tensor[li][0][letterToIndex(letter)] = 1 | |
| return tensor | |
| # Just return an output given a line | |
| def evaluate_model(line_tensor): | |
| model = torch.jit.load("torchscript_classify_names_lstm.pt") | |
| hidden = (torch.zeros(1, 1, 128),torch.zeros(1, 1, 128)) | |
| output = model(line_tensor,hidden) | |
| return output | |
| def classify_lastname(last_name): | |
| # Converting to Ascii and capitalizing first letter | |
| last_name = unicodeToAscii(last_name) | |
| last_name = last_name.title() | |
| # Converting name to tensor | |
| line_tensor = lineToTensor(last_name) | |
| output = evaluate_model(line_tensor) | |
| # Grabbing top3 probabilities and categories | |
| top3_prob, top3_cat = torch.topk(output,3) | |
| probs = torch.exp(top3_prob[0]) | |
| cats = top3_cat[0] | |
| model_output = {} | |
| for i in range(3): | |
| print(probs[i].item()) | |
| model_output[all_categories[cats[i].item()]] = round(probs[i].item(),2) | |
| return model_output | |
| demo = gr.Interface(classify_lastname, | |
| inputs = "text", | |
| outputs = gr.outputs.Label(type="confidences",num_top_classes=3), | |
| title = "Classify Last Name :)", | |
| description="Classifies last name into one of 18 language of origin. Returns confidence % for the top three categories" | |
| ) | |
| demo.launch(inline=False) |