Create app.py

app.py

@@ -0,0 +1,61 @@
+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)