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| import torch | |
| import torch.nn.functional as F | |
| import string | |
| import gradio as gr | |
| 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'] | |
| # Find letter index from all_letters: Ex: "a" = 0 | |
| def letterToIndex(letter): | |
| return all_letters.find(letter) | |
| # Giving each charachter in name a one hot vector | |
| 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 | |
| # Loading in torchscript model | |
| my_model = torch.jit.load('name_classifier_ts.ptl') | |
| # Return output given a line_tensor | |
| def evaluate(line_tensor): | |
| hidden = torch.zeros(1,128) | |
| for i in range(line_tensor.size()[0]): | |
| output, hidden = my_model(line_tensor[i], hidden) | |
| return output | |
| # Feeding in a name and number of top predictions you want to output | |
| def predict(last_name,n_predictions=3): | |
| last_name = last_name.title() | |
| with torch.no_grad(): | |
| output = evaluate(lineToTensor(last_name)) | |
| output = F.softmax(output,dim=1) | |
| topv,topi = output.topk(n_predictions,1,True) | |
| top_3_countries = '' | |
| for i in range(n_predictions): | |
| value = topv[0] | |
| category_index = topi[0][i].item() | |
| top_3_countries += f'{all_categories[category_index]}: {round(value[i].item()*100,2)}%' | |
| top_3_countries += '\n' | |
| return top_3_countries | |
| demo = gr.Interface(predict, | |
| inputs = "text", | |
| outputs = "text", | |
| description="Classify name into language of origin. Returns top 3 languages of origin" | |
| ) | |
| demo.launch(inline=False) |