chotu.py

import random
import json
from unittest import result
import torch
from Brain import NeuralNet
from NeuralNetwork import bag_of_words,tokenize

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

with open("intents.json",'r') as json_data:
    intents = json.load(json_data)


FILE = "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 = "chotu"
from listen import Listen
from speak import say
from task import NonInputExecution,InputExecution
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,sentence)
                elif "google" in reply:
                    InputExecution(reply,result)

                else:
                    say(reply)



while True:
    Main()