Update app.py

app.py

@@ -148,9 +148,9 @@ all_words = [stem(w) for w in all_words if w not in ignore_words]
 all_words = sorted(set(all_words))
 tags = sorted(set(tags))
 
-print(len(xy), "patterns")
-print(len(tags), "tags:", tags)
-print(len(all_words), "unique stemmed words:", all_words)
+#print(len(xy), "patterns") #commented
+#print(len(tags), "tags:", tags) #commented
+#print(len(all_words), "unique stemmed words:", all_words) #commented
 
 # create training data
 X_train = []
@@ -173,7 +173,7 @@ learning_rate = 0.001
 input_size = len(X_train[0])
 hidden_size = 8
 output_size = len(tags)
-print(input_size, output_size)
+#print(input_size, output_size) #commented
 
 class ChatDataset(Dataset):
 
@@ -193,7 +193,7 @@ class ChatDataset(Dataset):
 import torch
 import torch.nn as nn
 
-from model import NeuralNet
+#from model import NeuralNet
 
 dataset = ChatDataset()
 train_loader = DataLoader(dataset=dataset,batch_size=batch_size,shuffle=True,num_workers=2)
@@ -226,7 +226,7 @@ for epoch in range(num_epochs):
         print (f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')
 
 
-print(f'final loss: {loss.item():.4f}')
+#print(f'final loss: {loss.item():.4f}')#commented
 
 data = {
 "model_state": model.state_dict(),
@@ -240,7 +240,7 @@ data = {
 FILE = "data.pth"
 torch.save(data, FILE)
 
-print(f'training complete. file saved to {FILE}')
+#print(f'training complete. file saved to {FILE}') #commented
 
 # !nvidia-smi 
 #https://github.com/python-engineer/pytorch-chatbot
@@ -268,7 +268,7 @@ import json
 
 import torch
 
-from model import NeuralNet
+#from model import NeuralNet
 #from nltk_utils import bag_of_words, tokenize
 
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')