python-with-AI

create import pyttsx3 #pip install pyttsx3 import speech_recognition as sr #pip intall speech recongnition import datetime import wikipedia #pip install wikipedia import webbrowser import os import smtplib

engine = pyttsx3.init('sapi5') voices = engine.getProperty('voices')

print(voices[1].id)

engine.setProperty('voice', voices[0].id)

def speak(audio): engine.say(audio) engine.runAndWait()

def wishMe(): hour = int(datetime.datetime.now().hour) if hour>=0 and hour<12: speak("Good Morning!")

elif hour>=12 and hour<18:
    speak("Good Afternoon!")   

else:
    speak("Good Evening!")  

speak("I am Jarvis Sir. Please tell me how may I help you")

def takeCommand(): #It takes microphone input from the user and returns string output

r = sr.Recognizer()
with sr.Microphone() as source:
    print("Listening...")
    r.pause_threshold = 1
    audio = r.listen(source)

try:
    print("Recognizing...")    
    query = r.recognize_google(audio, language='en-in')
    print(f"User said: {query}\n")

except Exception as e:
    # print(e)    
    print("Say that again please...")  
    return "None"
return query

def sendEmail(to, content): server = smtplib.SMTP('smtp.gmail.com', 587) server.ehlo() server.starttls() server.login('[email protected]', 'your-password') server.sendmail('[email protected]', to, content) server.close()

if name == "main": wishMe() while True: # if 1: query = takeCommand().lower()

    # Logic for executing tasks based on query
    if 'wikipedia' in query:
        speak('Searching Wikipedia...')
        query = query.replace("wikipedia", "")
        results = wikipedia.summary(query, sentences=2)
        speak("According to Wikipedia")
        print(results)
        speak(results)

    elif 'open youtube' in query:
        webbrowser.open("youtube.com")

    elif 'open google' in query:
        webbrowser.open("google.com")

    elif 'open stackoverflow' in query:
        webbrowser.open("stackoverflow.com")   


    elif 'play music' in query:
        music_dir = 'D:\\Non Critical\\songs\\Favorite Songs2'
        songs = os.listdir(music_dir)
        print(songs)    
        os.startfile(os.path.join(music_dir, songs[0]))

    elif 'the time' in query:
        strTime = datetime.datetime.now().strftime("%H:%M:%S")    
        speak(f"Sir, the time is {strTime}")

    elif 'open code' in query:
        codePath = "C:\\Users\\harsh\\AppData\\Local\\Programs\\Microsoft VS Code\\Code.exe"
        os.startfile(codePath)

    elif 'email to harry' in query:
        try:
            speak("What should I say?")
            content = takeCommand()
            to = "[email protected]"    
            sendEmail(to, content)
            speak("Email has been sent!")
        except Exception as e:
            print(e)
            speak("Sorry my friend harsh bhai. I am not able to send this email")

Have you ever wondered how cool it would be to have your own A.I

Related tags

Overview

python-with-AI

print(voices[1].id)

Owner

Harsh Gupta

Deep Learning Tutorial for Kaggle Ultrasound Nerve Segmentation competition, using Keras

Code for the paper "A Study of Face Obfuscation in ImageNet"

Python module providing a framework to trace individual edges in an image using Gaussian process regression.

Improving adversarial robustness by a coupling rejection strategy

Emblaze - Interactive Embedding Comparison

Interpretable-contrastive-word-mover-s-embedding

Official PyTorch Implementation of paper "NeLF: Neural Light-transport Field for Single Portrait View Synthesis and Relighting", EGSR 2021.

Pixel-level Crack Detection From Images Of Levee Systems : A Comparative Study

Duke Machine Learning Winter School: Computer Vision 2022

Official code for the paper "Self-Supervised Prototypical Transfer Learning for Few-Shot Classification"

Code for paper "Multi-level Disentanglement Graph Neural Network"

A list of multi-task learning papers and projects.

🏅 The Most Comprehensive List of Kaggle Solutions and Ideas 🏅

TAug :: Time Series Data Augmentation using Deep Generative Models

BOVText: A Large-Scale, Multidimensional Multilingual Dataset for Video Text Spotting

Differential fuzzing for the masses!

This code is a toolbox that uses Torch library for training and evaluating the ERFNet architecture for semantic segmentation.

Code for paper Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

Unofficial implementation of Google's FNet: Mixing Tokens with Fourier Transforms

[CVPR 2021] VirTex: Learning Visual Representations from Textual Annotations