Update chat.py
Browse files
chat.py
CHANGED
@@ -26,11 +26,29 @@ model = NeuralNet(input_size, hidden_size, output_size).to(device)
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model.load_state_dict(model_state)
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model.eval()
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bot_name = "
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# print(
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# "Hello, I am B-BOT, personal ChatBOT of Mr. Bibek. Let's chat! (type 'quit' or 'q' to exit)" # NoQA
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# )
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def generate_response(sentence):
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# sentence = input("You: ")
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@@ -52,8 +70,7 @@ def generate_response(sentence):
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probs = torch.softmax(output, dim=1)
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prob = probs[0][predicted.item()]
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if prob.item() > 0.95:
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for intent in intents["intents"]:
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if tag == intent["tag"]:
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return f"{bot_name}: {random.choice(intent['responses'])}"
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model.load_state_dict(model_state)
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model.eval()
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bot_name = "BGPT"
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# print(
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# "Hello, I am B-BOT, personal ChatBOT of Mr. Bibek. Let's chat! (type 'quit' or 'q' to exit)" # NoQA
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# )
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def generate_tag(sentence):
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# sentence = input("You: ")
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sentence = correct_typos(sentence)
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# print(sentence)
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if sentence.lower() == "quit" or sentence.lower() == "q":
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# Needs to quit
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pass
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sentence = tokenize(sentence)
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X = bag_of_words(sentence, all_words)
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X = X.reshape(1, X.shape[0])
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X = torch.from_numpy(X).to(device)
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output = model(X)
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_, predicted = torch.max(output, dim=1)
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tag = tags[predicted.item()]
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return tag
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def generate_response(sentence):
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# sentence = input("You: ")
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probs = torch.softmax(output, dim=1)
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prob = probs[0][predicted.item()]
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if prob.item() > 0.8:
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for intent in intents["intents"]:
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if tag == intent["tag"]:
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return f"{bot_name}: {random.choice(intent['responses'])}"
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