main.py

import chatterbot
from chatterbot import ChatBot
from chatterbot.trainers import ListTrainer

import speech_recognition as sr
from time import ctime
import time
import os
from gtts import gTTS
import wikipedia
import wolframalpha
from yandex_speech import TTS
import os
class main:
    "this class contains all the functions required for the "
    APPID="LTQUWQ-YLHV4696XJ";
    client = wolframalpha.Client(APPID);


# initializing the chatbots
    lamp = ChatBot("Lamp")
    lamp.set_trainer(ListTrainer)

    lamp = ChatBot(
        'lamp',
        trainer='chatterbot.trainers.ListTrainer',
        storage_adapter='chatterbot.storage.SQLStorageAdapter',
        logic_adapters=[
            "chatterbot.logic.BestMatch",
            "chatterbot.logic.MathematicalEvaluation",
            {
            'import_path': 'chatterbot.logic.LowConfidenceAdapter',
            'threshold': 0.40,
            'default_response': 'I am sorry, but I do not understand.'
        }
            ],
        #filters=[
            #'chatterbot.filters.RepetitiveResponseFilter'
           # ],
        database='./databaselampit.sqlite3'
        )

#VOICE

    def speak(audioString):
        tts = TTS("ermil", "mp3", "25d87483-720a-46ea-82bd-7f89d4c95bbd",lang='en-US',emotion="good")
        tts.generate(audioString+" ")
        tts.save()
        os.system("mpg321 --stereo speech.mp3 ")
            
            
        
#trainer for the list available in the folder input the filename

    def train_file(filename):
        #speak("I am learinig and launching myself soon");
        path=filename+"";
        file = open(path, 'r')
        n_lines=0;
        for line in file:
            n_lines+=1
        file.close();
        print(n_lines)
        count=0
        file = open(path, 'r')
        for i in range(0,n_lines):
            question=file.readline().replace("- ","").strip();
            print(question);
            answer=file.readline().replace("- ","").strip();
            print(answer);
            main.lamp.train([question,answer]);
            i+=1;

# funtion to know if the bot is calibarted or not
#returns a bool value true means need to calibrate else note required to calibrate ini.txt is 0 if calibrated

    def isCalibrated():
        file=open("ini.txt",'r')
        s=int(file.read());
        file.close();
        if (s==0):
            return False;
        else:
            return True;
    print(isCalibrated());


# function for self calibration
    def self_calibrate():
        path_Q="calibrate.txt"
        Q= open(path_Q, 'r')
        A= open("self_calibrate.txt",'w')
        n_lines=0;
        for line in Q:
            n_lines+=1
        Q.close();
        print(n_lines)
        count=0
        Q = open(path_Q, 'r')
        for i in range(0,n_lines):
            question=Q.readline().replace("- ","").strip();
            print("Q :"+question);
            answer=input("answer")
            A.write(question+"\n");
            A.write(answer+"\n");
            lamp.train([question,answer]);
            i+=1;
        
        
### add the code to change the initial state to zero once the file had been calibrated


# In[6]:


    if(isCalibrated()):
        print("calibarting");
        #self_Calibrated();
        train_file("self_calibrate.txt");
    else:
        print("dont calibrate");


# In[28]:





# function to call for wikipedia and wolfromalpha

# In[7]:



#fucntion to call wikipedia and wolfrom
    def online(ask):
    #try and except
        try:
            
        #wolf
            import wolframalpha
            APPID="LTQUWQ-YLHV4696XJ";
            client = wolframalpha.Client(APPID);
            res=client.query(ask);
            answer=next(res.results).text;
            #print(answer);
        except:
                try:
                    wikipedia.set_lang("en");
                    answer=wikipedia.summary(ask,sentences =2);
                except:
                    answer="error";
        return answer;


#voice recognition functions and its setting up

    def recordAudio():
   # Record Audio
        r = sr.Recognizer()
        r.dynamic_energy_threshold = True
        with sr.Microphone() as source:
            r.adjust_for_ambient_noise(source, duration = 1);
            print("Say something!")
            audio = r.listen(source)

   # Speech recognition using Google Speech Recognition
        data = ""
        try:
       # Uses the default API key
       # To use another API key: `r.recognize_google(audio, key="GOOGLE_SPEECH_RECOGNITION_API_KEY")`
            data = r.recognize_google(audio)
            print("You said: " + data)
        except sr.UnknownValueError:
            print("Google Speech Recognition could not understand audio")
        except sr.RequestError as e:
            print("Could not request results from Google Speech Recognition service; {0}".format(e))
            #data=r.recognize_sphinx(audio);
            #print("Sphinx thinks you said " + data)

        return data