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import random
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import json
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import torch
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import pyttsx3
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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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with open('intents.json', 'r') as json_data:
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intents = json.load(json_data)
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FILE = "data.pth"
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data = torch.load(FILE)
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input_size = data["input_size"]
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hidden_size = data["hidden_size"]
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output_size = data["output_size"]
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all_words = data['all_words']
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tags = data['tags']
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model_state = data["model_state"]
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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 = "Surya"
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print("Let's chat! Note : It Can Be Make A Some Mistakes Because It Is in still under devolopment(type 'quit' to exit)")
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engine = pyttsx3.init()
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engine.say("Hello Sir Iam Surya What can i help you today ?")
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while True:
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sentence = input("You: ")
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if sentence == "quit":
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break
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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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probs = torch.softmax(output, dim=1)
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prob = probs[0][predicted.item()]
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if prob.item() > 0.75:
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for intent in intents['intents']:
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if tag == intent["tag"]:
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response = random.choice(intent['responses'])
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print(f"{bot_name}: {response}")
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engine.say(response)
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engine.runAndWait()
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else:
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print(f"{bot_name}: I do not understand...")
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engine.say("I do not understand.")
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engine.runAndWait()
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