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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) |