Spaces:
Sleeping
Sleeping
Create app.py
Browse files
app.py
ADDED
@@ -0,0 +1,61 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import torch.nn.functional as F
|
3 |
+
import string
|
4 |
+
import gradio as gr
|
5 |
+
|
6 |
+
all_letters = string.ascii_letters + " .,;'"
|
7 |
+
n_letters = len(all_letters)
|
8 |
+
|
9 |
+
all_categories = ['Arabic','Chinese','Czech','Dutch','English','French','German','Greek',
|
10 |
+
'Irish','Italian','Japanese','Korean','Polish','Portuguese','Russian','Scottish',
|
11 |
+
'Spanish','Vietnamese']
|
12 |
+
|
13 |
+
# Find letter index from all_letters: Ex: "a" = 0
|
14 |
+
def letterToIndex(letter):
|
15 |
+
return all_letters.find(letter)
|
16 |
+
|
17 |
+
# Giving each charachter in name a one hot vector
|
18 |
+
def lineToTensor(line):
|
19 |
+
tensor = torch.zeros(len(line),1,n_letters)
|
20 |
+
for li,letter in enumerate(line):
|
21 |
+
tensor[li][0][letterToIndex(letter)] = 1
|
22 |
+
|
23 |
+
return tensor
|
24 |
+
|
25 |
+
# Loading in torchscript model
|
26 |
+
my_model = torch.jit.load('name_classifier_ts.ptl')
|
27 |
+
|
28 |
+
# Return output given a line_tensor
|
29 |
+
def evaluate(line_tensor):
|
30 |
+
hidden = torch.zeros(1,128)
|
31 |
+
|
32 |
+
for i in range(line_tensor.size()[0]):
|
33 |
+
output, hidden = my_model(line_tensor[i], hidden)
|
34 |
+
|
35 |
+
return output
|
36 |
+
|
37 |
+
# Feeding in a name and number of top predictions you want to output
|
38 |
+
def predict(last_name,n_predictions=3):
|
39 |
+
|
40 |
+
last_name = last_name.title()
|
41 |
+
with torch.no_grad():
|
42 |
+
output = evaluate(lineToTensor(last_name))
|
43 |
+
output = F.softmax(output,dim=1)
|
44 |
+
|
45 |
+
topv,topi = output.topk(n_predictions,1,True)
|
46 |
+
|
47 |
+
top_3_countries = ''
|
48 |
+
for i in range(n_predictions):
|
49 |
+
value = topv[0]
|
50 |
+
category_index = topi[0][i].item()
|
51 |
+
top_3_countries += f'{all_categories[category_index]}: {round(value[i].item()*100,2)}%'
|
52 |
+
top_3_countries += '\n'
|
53 |
+
return top_3_countries
|
54 |
+
|
55 |
+
demo = gr.Interface(predict,
|
56 |
+
inputs = "text",
|
57 |
+
outputs = "text",
|
58 |
+
description="Classify name into language of origin. Returns top 3 languages of origin"
|
59 |
+
)
|
60 |
+
|
61 |
+
demo.launch(inline=False)
|