AI assistant

AI assistant: Building Your Own J.A.R.V.I.S

Ever dreamed of having your own personal AI assistant like Tony Stark‘s J.A.R.V.I.S from the Iron Man movies? Well, with Python and a bit of know-how, you can turn that dream into a reality. In this beginner-friendly guide, we’ll walk you through the process of building your very own AI assistant inspired by J.A.R.V.I.S.

You’ll learn how it listens to your voice commands, understands what you’re saying, and even remembers past conversations – all using simple Python code.

AI assistant: Meet J.A.R.V.I.S

Our AI assistant, affectionately named J.A.R.V.I.S (Just A Rather Very Intelligent System), is designed to be your faithful digital companion. It listens to your voice commands, responds to your queries, and helps you with tasks just like a real assistant would.

J.A.R.V.I.S listens to your default microphone and answers correctly with default speech.

How J.A.R.V.I.S Works:

  1. Listening to You: J.A.R.V.I.S starts by listening to your voice commands using Python’s ‘SpeechRecognition’ library. When you speak, it captures your words through your device’s microphone and converts them into text.
  2. Understanding Your Commands: Once it hears what you’ve said, J.A.R.V.I.S uses J.A.R.V.I.S’s GPT (Generative Pre-trained Transformer) models to understand the context of your command. These models are trained on vast amounts of text data, enabling them to generate responses that make sense based on what you’ve said.
  3. Engaging in Dialogue: J.A.R.V.I.S engages in a dialogue with you, responding to your queries with voice speech and providing helpful information or assistance. It’s like having a conversation with a knowledgeable friend who’s always there to lend a hand.
  4. Remembering Conversations: What sets J.A.R.V.I.S apart is its ability to remember past conversations. It stores the conversation history in a JSON file, allowing it to maintain context and provide more personalized responses over time.

Saving Conversations

Every time you interact with J.A.R.V.I.S, it saves the conversation history to a JSON file on your computer. This file acts as a memory bank, storing details of your interactions so that J.A.R.V.I.S can refer back to them later. It’s like having a log of all your conversations with your AI assistant.

Make sure to have your Python file in its own directory where all the other necessary files will be generated.


Create an OpenAI API Key

The Python code:

import os
import json
import speech_recognition as sr
import openai
import pyttsx3
# Set up your OpenAI API key
openai.api_key = 'OPENAI API KEY HERE'
# Initialize the recognizer
recognizer = sr.Recognizer()
# Initialize the text-to-speech engine with SAPI5 driver (Windows specific)
engine = pyttsx3.init(driverName='sapi5')
# Set properties for the text-to-speech engine (optional)
engine.setProperty('rate', 150)  # Speed of speech
engine.setProperty('volume', 1)   # Volume level (0.0 to 1.0)
# Get the current working directory
script_dir = os.getcwd()
# File to store conversation history
CONVERSATION_FILE = os.path.join(script_dir, 'conversation_history.json')
# Load conversation history from file
def load_conversation_history():
    if os.path.exists(CONVERSATION_FILE):
        with open(CONVERSATION_FILE, 'r') as f:
            return json.load(f)
    return []
# Save conversation history to file
def save_conversation_history(conversation_memory):
    with open(CONVERSATION_FILE, 'w') as f:
        json.dump(conversation_memory, f)
# Load conversation history
conversation_memory = load_conversation_history()
# Function to listen to the microphone and convert speech to text
def listen_microphone():
    with sr.Microphone() as source:
        recognizer.adjust_for_ambient_noise(source)  # Adjust for ambient noise
        audio = recognizer.listen(source)  # Listen to microphone input
            text = recognizer.recognize_google(audio)  # Recognize speech using Google Speech Recognition
            print("You said:", text)
            return text.lower()
        except sr.UnknownValueError:
            print("Sorry, I couldn't understand what you said.")
            return ""
        except sr.RequestError as e:
            print("Could not request results from Google Speech Recognition service; {0}".format(e))
            return ""
# Function to generate response using ChatGPT API
def generate_response(user_input):
    global conversation_memory
    messages = [{"role": "user", "content": m["content"]} for m in conversation_memory]
    messages.append({"role": "user", "content": user_input})
    response = openai.ChatCompletion.create(
    result = response.choices[0].message.content
    conversation_memory.append({"role": "user", "content": user_input})
    conversation_memory.append({"role": "assistant", "content": result})
    # Save conversation history to file
    return result
# Main loop
while True:
    print("How can I assist you?")
    user_input = listen_microphone()
    if user_input:
        # Generate response
            jarvis_reply = generate_response(user_input)
            print("J.A.R.V.I.S:", jarvis_reply)
            # Convert text response to speech and play it
        except Exception as e:
            print("Error generating response:", e)
            continue  # Skip saving conversation history if there's an error

Taking It Further

Once you’ve mastered the basics of building your own AI assistant, J.A.R.V.I.S, get ready for a journey where you can ask J.A.R.V.I.S itself to level up its skills. It’s like having a witty genie in a digital bottle – your customization options are limitless! With Python as your toolkit and J.A.R.V.I.S as your witty sidekick, the adventure of AI customization awaits!

Building your own AI assistant like J.A.R.V.I.S is not just a fun project – it’s also a great way to learn about artificial intelligence and machine learning. With Python’s simplicity and versatility, anyone can create their own digital companion. So why not give it a try?

Who knows, you might just end up with your very own J.A.R.V.I.S by your side.

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