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