KaushikShresth07 · November 22, 2023 00:58 · codenik99 · Jan 26, 2024 · Aman148477 · May 31, 2024
diff --git a/AI Model Jarvis.py b/AI Model Jarvis.py
 import pyttsx3
 import speech_recognition as sr
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.svm import SVC
 import nltk
 from sklearn.model_selection import train_test_split
 import random
 import warnings
 warnings.simplefilter('ignore')

 # nltk.download("punkt")

 def speak(text):
    engine = pyttsx3.init()
    Id = r'HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Speech\Voices\Tokens\TTS_MS_EN-US_DAVID_11.0'
    engine.setProperty('voice',Id)
    print("")
    print(f"==> Jarvis AI : {text}")
    print("")
    engine.say(text=text)
    engine.runAndWait()

 def speechrecognition():
    r = sr.Recognizer()
    with sr.Microphone() as source:
        print("Listening.....")
        r.pause_threshold = 1
        audio = r.listen(source,0,8)

    try:
        print("Recogizing....")
        query = r.recognize_google(audio,language="en")
        print(f"==> Shresth : {query}")
        return query.lower()

    except:
        return ""

 def MainExecution(query):
    Query = str(query).lower()

    if "hello" in Query:
        speak("Hello Sir, Welcome Back!")
    
    elif "bye" in Query:
        speak("Nice to meet you sir, Have a nice day!")

    elif "time" in Query:
        from datetime import datetime
        time = datetime.now().strftime("%H:%M")
        speak(f"The Time Now Is : {time}")

    elif "bye" in Query:
        speak("Nice to meet you sir, Have a nice day!")

    elif "bye" in Query:
        speak("Nice to meet you sir, Have a nice day!")

 intents = {
    "greetings": {
        "patterns": ["hello", "hi", "hey", "howdy", "greetings", "good morning", "good afternoon", "good evening", "hi there", "hey there", "what's up", "hello there"],
        "responses": ["Hello! How can I assist you?", "Hi there!", "Hey! What can I do for you?", "Howdy! What brings you here?", "Greetings! How may I help you?", "Good morning! How can I be of service?", "Good afternoon! What do you need assistance with?", "Good evening! How may I assist you?", "Hey there! How can I help?", "Hi! What's on your mind?", "Hello there! How can I assist you today?"]
    },
    "goodbye": {
        "patterns": ["bye", "see you later", "goodbye", "farewell", "take care", "until next time", "bye bye", "catch you later", "have a good one", "so long"],
        "responses": ["Goodbye!", "See you later!", "Have a great day!", "Farewell! Take care.", "Goodbye! Until next time.", "Take care! Have a wonderful day.", "Bye bye!", "Catch you later!", "Have a good one!", "So long!"]
    },
    "gratitude": {
        "patterns": ["thank you", "thanks", "appreciate it", "thank you so much", "thanks a lot", "much appreciated"],
        "responses": ["You're welcome!", "Happy to help!", "Glad I could assist.", "Anytime!", "You're welcome! Have a great day.", "No problem!"]
    },
    "apologies": {
        "patterns": ["sorry", "my apologies", "apologize", "I'm sorry"],
        "responses": ["No problem at all.", "It's alright.", "No need to apologize.", "That's okay.", "Don't worry about it.", "Apology accepted."]
    },
    "positive_feedback": {
        "patterns": ["great job", "well done", "awesome", "fantastic", "amazing work", "excellent"],
        "responses": ["Thank you! I appreciate your feedback.", "Glad to hear that!", "Thank you for the compliment!", "I'm glad I could meet your expectations.", "Your words motivate me!", "Thank you for your kind words."]
    },
    "negative_feedback": {
        "patterns": ["not good", "disappointed", "unsatisfied", "poor service", "needs improvement", "could be better"],
        "responses": ["I'm sorry to hear that. Can you please provide more details so I can assist you better?", "I apologize for the inconvenience. Let me help resolve the issue.", "I'm sorry you're not satisfied. Please let me know how I can improve.", "Your feedback is valuable. I'll work on improving."]
    },
    "weather": {
        "patterns": ["what's the weather like?", "weather forecast", "is it going to rain today?", "temperature today", "weather report"],
        "responses": ["The weather today is [weather_description].", "Currently, it's [temperature] degrees with [weather_description].", "The forecast predicts [weather_forecast].", "It might rain today. Don't forget your umbrella!", "The temperature today is [temperature] degrees."]
    },
    "help": {
        "patterns": ["help", "can you help me?", "I need assistance", "support"],
        "responses": ["Sure, I'll do my best to assist you.", "Of course, I'm here to help!", "How can I assist you?", "I'll help you with your query."]
    },
    "time": {
        "patterns": ["what's the time?", "current time", "time please", "what time is it?"],
        "responses": ["It's [current_time].", "The current time is [current_time].", "Right now, it's [current_time]."]
    },
    "jokes": {
        "patterns": ["tell me a joke", "joke please", "got any jokes?", "make me laugh"],
        "responses": ["Why don't we ever tell secrets on a farm? Because the potatoes have eyes and the corn has ears!", "What do you get when you cross a snowman and a vampire? Frostbite!", "Why was the math book sad? Because it had too many problems!"]
    },
    "music": {
        "patterns": ["play music", "music please", "song recommendation", "music suggestion"],
        "responses": ["Sure, playing some music for you!", "Here's a song you might like: [song_name]", "How about some music?"]
    },
    "food": {
        "patterns": ["recommend a restaurant", "food places nearby", "what's good to eat?", "restaurant suggestion"],
        "responses": ["Sure, here are some recommended restaurants: [restaurant_names]", "Hungry? Let me find some good food places for you!", "I can suggest some great places to eat nearby."]
    },
    "news": {
        "patterns": ["latest news", "news updates", "what's happening?", "current events"],
        "responses": ["Let me fetch the latest news for you.", "Here are the top headlines: [news_headlines]", "Stay updated with the latest news!"]
    },
    "movies": {
        "patterns": ["movie suggestions", "recommend a movie", "what should I watch?", "best movies"],
        "responses": ["How about watching [movie_name]?", "Here's a movie suggestion for you.", "Let me recommend some great movies!"]
    },
    "sports": {
        "patterns": ["sports news", "score updates", "latest sports events", "upcoming games"],
        "responses": ["I'll get you the latest sports updates.", "Stay updated with the current sports events!", "Let me check the sports scores for you."]
    },
    "gaming": {
        "patterns": ["video game recommendations", "best games to play", "recommend a game", "gaming suggestions"],
        "responses": ["How about trying out [game_name]?", "Here are some gaming suggestions for you!", "Let me recommend some fun games to play!"]
    },
        "tech_support": {
        "patterns": ["technical help", "computer issues", "troubleshooting", "IT support"],
        "responses": ["I can assist with technical issues. What problem are you facing?", "Let's troubleshoot your technical problem together.", "Tell me about the technical issue you're experiencing."]
    },
    "book_recommendation": {
        "patterns": ["recommend a book", "good books to read", "book suggestions", "what should I read?"],
        "responses": ["How about reading [book_title]?", "I've got some great book recommendations for you!", "Let me suggest some interesting books for you to read."]
    },
    "fitness_tips": {
        "patterns": ["fitness advice", "workout tips", "exercise suggestions", "healthy habits"],
        "responses": ["Staying fit is important! Here are some fitness tips: [fitness_tips]", "I can help you with workout suggestions and fitness advice.", "Let me provide some exercise recommendations for you."]
    },
    "travel_recommendation": {
        "patterns": ["travel suggestions", "places to visit", "recommend a destination", "travel ideas"],
        "responses": ["Looking for travel recommendations? Here are some great destinations: [travel_destinations]", "I can suggest some amazing places for your next travel adventure!", "Let me help you with travel destination ideas."]
    },
    "education": {
        "patterns": ["learning resources", "study tips", "education advice", "academic help"],
        "responses": ["I can assist with educational queries. What subject are you studying?", "Let's explore learning resources together.", "Tell me about your educational goals or questions."]
    },
    "pet_advice": {
        "patterns": ["pet care tips", "animal advice", "pet health", "taking care of pets"],
        "responses": ["Pets are wonderful! Here are some pet care tips: [pet_care_tips]", "I can provide advice on pet health and care.", "Let's talk about your pet and their well-being."]
    },
    "shopping": {
        "patterns": ["online shopping", "buying something", "shopping advice", "product recommendations"],
        "responses": ["I can help you with online shopping. What are you looking to buy?", "Let's find the perfect item for you!", "Tell me what you're interested in purchasing."]
    },
    "career_advice": {
        "patterns": ["job search help", "career guidance", "career change advice", "professional development"],
        "responses": ["I can provide career advice. What specific guidance do you need?", "Let's explore career opportunities together.", "Tell me about your career goals or concerns."]
    },
    "relationship_advice": {
        "patterns": ["relationship help", "love advice", "dating tips", "relationship problems"],
        "responses": ["Relationships can be complex. How can I assist you?", "I can offer advice on relationships and dating.", "Tell me about your relationship situation."]
    },
    "mental_health": {
        "patterns": ["mental health support", "coping strategies", "stress relief tips", "emotional well-being"],
        "responses": ["Mental health is important. How can I support you?", "I can provide guidance for managing stress and emotions.", "Let's talk about strategies for maintaining mental well-being."]
    },
    "language_learning": {
        "patterns": ["language learning tips", "language practice", "learning new languages", "language study advice"],
        "responses": ["Learning a new language can be exciting! How can I assist you?", "I can help with language learning tips and practice.", "Tell me which language you're interested in learning."]
    },
    "finance_advice": {
        "patterns": ["financial planning help", "money management tips", "investment advice", "budgeting assistance"],
        "responses": ["I can provide guidance on financial matters. What specific advice do you need?", "Let's discuss your financial goals and plans.", "Tell me about your financial situation or goals."]
    },
 }

 training_data = []
 labels = []

 for intent , data in intents.items():
    for pattern in data['patterns']:
        training_data.append(pattern.lower())
        labels.append(intent)

 # print(training_data)
 # print(labels)

 Vectorizer = TfidfVectorizer(tokenizer=nltk.word_tokenize,stop_words="english",max_df=0.8,min_df=1)
 X_train = Vectorizer.fit_transform(training_data)
 X_train,X_test,Y_train,Y_test = train_test_split(X_train,labels,test_size=0.4,random_state=42,stratify=labels)

 model = SVC(kernel='linear', probability=True, C=1.0)
 model.fit(X_train, Y_train)

 predictions = model.predict(X_test)

 def predict_intent(user_input):
    user_input = user_input.lower()
    input_vector = Vectorizer.transform([user_input])
    intent = model.predict(input_vector)[0]
    return intent

 print("AI Assistant: Hello! How can I assist you?")
 while True:
    user_input = speechrecognition()
    if user_input.lower() == 'exit':
        print("AI Assistant: Goodbye!")
        break

    intent = predict_intent(user_input)
    if intent in intents:
        responses = intents[intent]['responses']
        response = random.choice(responses)
        speak(response)

    else:
        speak("AI Assistant: Sorry, I'm not sure how to respond to that.")
	import pyttsx3
	import speech_recognition as sr
	from sklearn.feature_extraction.text import TfidfVectorizer
	from sklearn.svm import SVC
	import nltk
	from sklearn.model_selection import train_test_split
	import random
	import warnings
	warnings.simplefilter('ignore')

	# nltk.download("punkt")

	def speak(text):
	engine = pyttsx3.init()
	Id = r'HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Speech\Voices\Tokens\TTS_MS_EN-US_DAVID_11.0'
	engine.setProperty('voice',Id)
	print("")
	print(f"==> Jarvis AI : {text}")
	print("")
	engine.say(text=text)
	engine.runAndWait()

	def speechrecognition():
	r = sr.Recognizer()
	with sr.Microphone() as source:
	print("Listening.....")
	r.pause_threshold = 1
	audio = r.listen(source,0,8)

	try:
	print("Recogizing....")
	query = r.recognize_google(audio,language="en")
	print(f"==> Shresth : {query}")
	return query.lower()

	except:
	return ""

	def MainExecution(query):
	Query = str(query).lower()

	if "hello" in Query:
	speak("Hello Sir, Welcome Back!")

	elif "bye" in Query:
	speak("Nice to meet you sir, Have a nice day!")

	elif "time" in Query:
	from datetime import datetime
	time = datetime.now().strftime("%H:%M")
	speak(f"The Time Now Is : {time}")

	elif "bye" in Query:
	speak("Nice to meet you sir, Have a nice day!")

	elif "bye" in Query:
	speak("Nice to meet you sir, Have a nice day!")

	intents = {
	"greetings": {
	"patterns": ["hello", "hi", "hey", "howdy", "greetings", "good morning", "good afternoon", "good evening", "hi there", "hey there", "what's up", "hello there"],
	"responses": ["Hello! How can I assist you?", "Hi there!", "Hey! What can I do for you?", "Howdy! What brings you here?", "Greetings! How may I help you?", "Good morning! How can I be of service?", "Good afternoon! What do you need assistance with?", "Good evening! How may I assist you?", "Hey there! How can I help?", "Hi! What's on your mind?", "Hello there! How can I assist you today?"]
	},
	"goodbye": {
	"patterns": ["bye", "see you later", "goodbye", "farewell", "take care", "until next time", "bye bye", "catch you later", "have a good one", "so long"],
	"responses": ["Goodbye!", "See you later!", "Have a great day!", "Farewell! Take care.", "Goodbye! Until next time.", "Take care! Have a wonderful day.", "Bye bye!", "Catch you later!", "Have a good one!", "So long!"]
	},
	"gratitude": {
	"patterns": ["thank you", "thanks", "appreciate it", "thank you so much", "thanks a lot", "much appreciated"],
	"responses": ["You're welcome!", "Happy to help!", "Glad I could assist.", "Anytime!", "You're welcome! Have a great day.", "No problem!"]
	},
	"apologies": {
	"patterns": ["sorry", "my apologies", "apologize", "I'm sorry"],
	"responses": ["No problem at all.", "It's alright.", "No need to apologize.", "That's okay.", "Don't worry about it.", "Apology accepted."]
	},
	"positive_feedback": {
	"patterns": ["great job", "well done", "awesome", "fantastic", "amazing work", "excellent"],
	"responses": ["Thank you! I appreciate your feedback.", "Glad to hear that!", "Thank you for the compliment!", "I'm glad I could meet your expectations.", "Your words motivate me!", "Thank you for your kind words."]
	},
	"negative_feedback": {
	"patterns": ["not good", "disappointed", "unsatisfied", "poor service", "needs improvement", "could be better"],
	"responses": ["I'm sorry to hear that. Can you please provide more details so I can assist you better?", "I apologize for the inconvenience. Let me help resolve the issue.", "I'm sorry you're not satisfied. Please let me know how I can improve.", "Your feedback is valuable. I'll work on improving."]
	},
	"weather": {
	"patterns": ["what's the weather like?", "weather forecast", "is it going to rain today?", "temperature today", "weather report"],
	"responses": ["The weather today is [weather_description].", "Currently, it's [temperature] degrees with [weather_description].", "The forecast predicts [weather_forecast].", "It might rain today. Don't forget your umbrella!", "The temperature today is [temperature] degrees."]
	},
	"help": {
	"patterns": ["help", "can you help me?", "I need assistance", "support"],
	"responses": ["Sure, I'll do my best to assist you.", "Of course, I'm here to help!", "How can I assist you?", "I'll help you with your query."]
	},
	"time": {
	"patterns": ["what's the time?", "current time", "time please", "what time is it?"],
	"responses": ["It's [current_time].", "The current time is [current_time].", "Right now, it's [current_time]."]
	},
	"jokes": {
	"patterns": ["tell me a joke", "joke please", "got any jokes?", "make me laugh"],
	"responses": ["Why don't we ever tell secrets on a farm? Because the potatoes have eyes and the corn has ears!", "What do you get when you cross a snowman and a vampire? Frostbite!", "Why was the math book sad? Because it had too many problems!"]
	},
	"music": {
	"patterns": ["play music", "music please", "song recommendation", "music suggestion"],
	"responses": ["Sure, playing some music for you!", "Here's a song you might like: [song_name]", "How about some music?"]
	},
	"food": {
	"patterns": ["recommend a restaurant", "food places nearby", "what's good to eat?", "restaurant suggestion"],
	"responses": ["Sure, here are some recommended restaurants: [restaurant_names]", "Hungry? Let me find some good food places for you!", "I can suggest some great places to eat nearby."]
	},
	"news": {
	"patterns": ["latest news", "news updates", "what's happening?", "current events"],
	"responses": ["Let me fetch the latest news for you.", "Here are the top headlines: [news_headlines]", "Stay updated with the latest news!"]
	},
	"movies": {
	"patterns": ["movie suggestions", "recommend a movie", "what should I watch?", "best movies"],
	"responses": ["How about watching [movie_name]?", "Here's a movie suggestion for you.", "Let me recommend some great movies!"]
	},
	"sports": {
	"patterns": ["sports news", "score updates", "latest sports events", "upcoming games"],
	"responses": ["I'll get you the latest sports updates.", "Stay updated with the current sports events!", "Let me check the sports scores for you."]
	},
	"gaming": {
	"patterns": ["video game recommendations", "best games to play", "recommend a game", "gaming suggestions"],
	"responses": ["How about trying out [game_name]?", "Here are some gaming suggestions for you!", "Let me recommend some fun games to play!"]
	},
	"tech_support": {
	"patterns": ["technical help", "computer issues", "troubleshooting", "IT support"],
	"responses": ["I can assist with technical issues. What problem are you facing?", "Let's troubleshoot your technical problem together.", "Tell me about the technical issue you're experiencing."]
	},
	"book_recommendation": {
	"patterns": ["recommend a book", "good books to read", "book suggestions", "what should I read?"],
	"responses": ["How about reading [book_title]?", "I've got some great book recommendations for you!", "Let me suggest some interesting books for you to read."]
	},
	"fitness_tips": {
	"patterns": ["fitness advice", "workout tips", "exercise suggestions", "healthy habits"],
	"responses": ["Staying fit is important! Here are some fitness tips: [fitness_tips]", "I can help you with workout suggestions and fitness advice.", "Let me provide some exercise recommendations for you."]
	},
	"travel_recommendation": {
	"patterns": ["travel suggestions", "places to visit", "recommend a destination", "travel ideas"],
	"responses": ["Looking for travel recommendations? Here are some great destinations: [travel_destinations]", "I can suggest some amazing places for your next travel adventure!", "Let me help you with travel destination ideas."]
	},
	"education": {
	"patterns": ["learning resources", "study tips", "education advice", "academic help"],
	"responses": ["I can assist with educational queries. What subject are you studying?", "Let's explore learning resources together.", "Tell me about your educational goals or questions."]
	},
	"pet_advice": {
	"patterns": ["pet care tips", "animal advice", "pet health", "taking care of pets"],
	"responses": ["Pets are wonderful! Here are some pet care tips: [pet_care_tips]", "I can provide advice on pet health and care.", "Let's talk about your pet and their well-being."]
	},
	"shopping": {
	"patterns": ["online shopping", "buying something", "shopping advice", "product recommendations"],
	"responses": ["I can help you with online shopping. What are you looking to buy?", "Let's find the perfect item for you!", "Tell me what you're interested in purchasing."]
	},
	"career_advice": {
	"patterns": ["job search help", "career guidance", "career change advice", "professional development"],
	"responses": ["I can provide career advice. What specific guidance do you need?", "Let's explore career opportunities together.", "Tell me about your career goals or concerns."]
	},
	"relationship_advice": {
	"patterns": ["relationship help", "love advice", "dating tips", "relationship problems"],
	"responses": ["Relationships can be complex. How can I assist you?", "I can offer advice on relationships and dating.", "Tell me about your relationship situation."]
	},
	"mental_health": {
	"patterns": ["mental health support", "coping strategies", "stress relief tips", "emotional well-being"],
	"responses": ["Mental health is important. How can I support you?", "I can provide guidance for managing stress and emotions.", "Let's talk about strategies for maintaining mental well-being."]
	},
	"language_learning": {
	"patterns": ["language learning tips", "language practice", "learning new languages", "language study advice"],
	"responses": ["Learning a new language can be exciting! How can I assist you?", "I can help with language learning tips and practice.", "Tell me which language you're interested in learning."]
	},
	"finance_advice": {
	"patterns": ["financial planning help", "money management tips", "investment advice", "budgeting assistance"],
	"responses": ["I can provide guidance on financial matters. What specific advice do you need?", "Let's discuss your financial goals and plans.", "Tell me about your financial situation or goals."]
	},
	}

	training_data = []
	labels = []

	for intent , data in intents.items():
	for pattern in data['patterns']:
	training_data.append(pattern.lower())
	labels.append(intent)

	# print(training_data)
	# print(labels)

	Vectorizer = TfidfVectorizer(tokenizer=nltk.word_tokenize,stop_words="english",max_df=0.8,min_df=1)
	X_train = Vectorizer.fit_transform(training_data)
	X_train,X_test,Y_train,Y_test = train_test_split(X_train,labels,test_size=0.4,random_state=42,stratify=labels)

	model = SVC(kernel='linear', probability=True, C=1.0)
	model.fit(X_train, Y_train)

	predictions = model.predict(X_test)

	def predict_intent(user_input):
	user_input = user_input.lower()
	input_vector = Vectorizer.transform([user_input])
	intent = model.predict(input_vector)[0]
	return intent

	print("AI Assistant: Hello! How can I assist you?")
	while True:
	user_input = speechrecognition()
	if user_input.lower() == 'exit':
	print("AI Assistant: Goodbye!")
	break

	intent = predict_intent(user_input)
	if intent in intents:
	responses = intents[intent]['responses']
	response = random.choice(responses)
	speak(response)

	else:
	speak("AI Assistant: Sorry, I'm not sure how to respond to that.")