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main.py
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import random
import json
import torch
from Train.Brain import NeuralNet
from Train.NeuralNetwork import bag_of_words ,tokenize
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
with open("Dataset\\intents.json",'r') as json_data:
intents = json.load(json_data)
FILE = "Dataset\\TrainData.pth"
data = torch.load(FILE)
input_size = data["input_size"]
hidden_size = data["hidden_size"]
output_size = data["output_size"]
all_words = data["all_words"]
tags = data["tags"]
model_state = data["model_state"]
model = NeuralNet(input_size,hidden_size,output_size).to(device)
model.load_state_dict(model_state)
model.eval()
#--------------------------------------------------------
Name = "Rony"
from Extention.Listen import Listen
from Extention.Speak import Say
from Task import InputExecution
from Task import NonInputExecution
def Main():
sentence = Listen()
result = str(sentence)
if sentence == "bye":
exit()
sentence = tokenize(sentence)
X = bag_of_words(sentence,all_words)
X = X.reshape(1,X.shape[0])
X = torch.from_numpy(X).to(device)
output = model(X)
_ , predicted = torch.max(output,dim=1)
tag = tags[predicted.item()]
probs = torch.softmax(output,dim=1)
prob = probs[0][predicted.item()]
if prob.item() > 0.75:
for intent in intents['intents']:
if tag == intent["tag"]:
reply = random.choice(intent["responses"])
if "time" in reply:
NonInputExecution(reply)
elif "date" in reply:
NonInputExecution(reply)
elif "day" in reply:
NonInputExecution(reply)
elif "wikipedia" in reply:
InputExecution(reply,result)
elif "google" in reply:
InputExecution(reply,result)
elif "YouTube search" in reply:
InputExecution(reply,result)
elif "solar system" in reply:
InputExecution(reply,result)
elif "how to" in reply:
InputExecution(reply,result)
elif "Amazon" in reply:
InputExecution(reply,result)
elif "Flipkart" in reply:
InputExecution(reply,result)
elif "temperature" in reply:
InputExecution(reply,result)
else:
Say(reply)
while True:
Main()