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Powered Personal Assistant Development

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"Experience seamless productivity with AI-Powered Personal Assistant Development by Emojoy. Elevate your efficiency as our cutting-edge technology learns and adapts to your preferences, streamlining tasks, scheduling, and communication. Unlock a new era of personalized support, making every interaction intuitive, efficient, and delightful. Your assistant, reimagined by Emojoy."
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Over 1 month ago

Prompt Details

Model
Chat - GPT-3.5 (gpt-3.5-turbo)
Token size
116 ($0.00010 / call)
Example input
[specific user needs]: productivity enhancement, daily reminders, information retrieval [task automation]: scheduling appointments, setting reminders, sending emails [natural language processing]: understanding user commands and queries [integration capabilities]: connecting with calendar apps, email clients, and informational databases [voice recognition]: converting spoken words into actionable commands [basic task execution]: executing simple commands like setting reminders
Example output
Certainly! Let's start by defining the key functionalities and implementing voice recognition for converting spoken words into actionable commands using Python. We'll use a library called SpeechRecognition for this purpose. ```python # Install SpeechRecognition library # pip install SpeechRecognition import speech_recognition as sr def listen(): recognizer = sr.Recognizer() with sr.Microphone() as source: print("Listening...") recognizer.adjust_for_ambient_noise(source) audio = recognizer.listen(source) try: print("Recognizing...") command = recognizer.recognize_google(audio).lower() print(f"User said: {command}") return command except sr.UnknownValueError: print("Sorry, could not understand audio.") return None except sr.RequestError as e: print(f"Could not request results from Google Speech Recognition service; {e}") return None # Test voice recognition user_command = listen() print("User command:", user_command) ``` This code defines a `listen` function that captures audio from the user's microphone, performs noise adjustment, and uses Google Speech Recognition to convert the audio into text. The resulting text is then printed as the user's command. Continue Now, let's move on to the next step, which is task automation for setting reminders. We'll use the `datetime` module to handle dates and times, and we'll implement a simple function to set reminders. ```python import datetime def set_reminder(): print("Sure, let's set a reminder.") print("When do you want to be reminded? (e.g., 'tomorrow at 3 PM')") user_time = listen() if user_time: try: reminder_time = datetime.datetime.strptime(user_time, "%Y-%m-%d %H:%M:%S") current_time = datetime.datetime.now() if reminder_time > current_time: time_difference = reminder_time - current_time seconds_difference = time_difference.total_seconds() print(f"Reminder set for {user_time}.") # You can integrate further actions here, like sending a notification or storing the reminder. else: print("Please provide a future date and time.") except ValueError: print("Sorry, I couldn't understand the date and time. Please try again.") # Test setting a reminder set_reminder() ``` This code defines a `set_reminder` function that prompts the user for a time when they want to be reminded, converts the user's spoken words into a datetime object, and checks if it's a future date and time. If it is, it calculates the time difference and acknowledges the reminder. Continue Great! Now, let's implement a basic email sending functionality. We'll use the `smtplib` library to send emails. Note that you'll need to provide your email credentials for this example. Ensure that you enable "Less secure app access" in your email account settings for this to work. ```python import smtplib from email.mime.text import MIMEText from email.mime.multipart import MIMEMultipart def send_email(): print("Sure, let's send an email.") # Replace these values with your email credentials sender_email = "your_email@gmail.com" sender_password = "your_password" recipient_email = "recipient_email@example.com" print("What should be the subject of the email?") email_subject = listen() print("What message would you like to include in the email?") email_message = listen() # Create message message = MIMEMultipart() message['From'] = sender_email message['To'] = recipient_email message['Subject'] = email_subject # Attach the text to the message message.attach(MIMEText(email_message, 'plain')) try: # Connect to the server server = smtplib.SMTP('smtp.gmail.com', 587) server.starttls() # Login to your email account server.login(sender_email, sender_password) # Send email server.sendmail(sender_email, recipient_email, message.as_string()) print("Email sent successfully.") except Exception as e: print(f"Error sending email: {e}") finally: # Disconnect from the server server.quit() # Test sending an email send_email() ``` Remember to replace "your_email@gmail.com" and "your_password" with your actual email address and password. Also, replace "recipient_email@example.com" with the recipient's email address. This code defines a `send_email` function that prompts the user for the email subject and message, then uses the SMTP protocol to send the email. Continue Now, let's enhance the natural language processing capabilities using a more advanced library like spaCy. spaCy allows us to extract structured information from text, such as entities and intents. Install spaCy using the following command: ```bash # Install spaCy # pip install spacy # Download the English language model # python -m spacy download en_core_web_sm ``` Now, we'll use spaCy to extract information from the user's command and determine the intent. ```python import spacy # Load the English language model nlp = spacy.load('en_core_web_sm') def process_command(command): doc = nlp(command) # Extract entities and intent entities = [(ent.text, ent.label_) for ent in doc.ents] intent = None # You can customize the intent detection based on your specific use case if "set reminder" in command: intent = "Set Reminder" elif "send email" in command: intent = "Send Email" return intent, entities # Test natural language processing user_command = listen() intent, entities = process_command(user_command) print("Intent:", intent) print("Entities:", entities) ``` This code defines a `process_command` function that uses spaCy to extract entities and determine the intent of the user's command. The intent is a high-level action the user wants to perform, such as setting a reminder or sending an email. Continue Now, let's integrate the natural language processing capabilities into our personal assistant to provide a more dynamic and context-aware interaction. ```python def main(): print("Hello! I'm your personal assistant.") while True: print("\nListening...") user_command = listen() intent, entities = process_command(user_command) if intent == "Set Reminder": set_reminder() elif intent == "Send Email": send_email() elif "exit" in user_command: print("Goodbye!") break else: print("I'm sorry, I didn't understand that command.") # Run the personal assistant main() ``` This `main` function creates a loop where the personal assistant continuously listens to user commands, processes them using natural language processing, and performs the corresponding action based on the detected intent. The assistant recognizes the intent to set a reminder or send an email, and it includes the functionalities we implemented earlier. Feel free to expand and customize this code further based on your specific use case and requirements. You can add more functionalities, improve natural language processing, and enhance the overall user experience.
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