Nlp Development Tutorials, Guides & Insights

import datetime
import pywhatkit

def process_command(command):
    if "time" in command:
        now = datetime.datetime.now().strftime("%H:%M")
        speak(f"The time is {now}")
    elif "search for" in command:
        query = command.replace("search for", "").strip()
        speak(f"Searching for {query}")
        pywhatkit.search(query)
    elif "play" in command:
        song = command.replace("play", "").strip()
        speak(f"Playing {song}")
        pywhatkit.playonyt(song)
    else:
        speak("I'm sorry, I can't perform that task yet.")

Integrate a weather API (e.g., OpenWeatherMap) to fetch current weather information.

• Use Larger Models: For better quality responses, consider using larger models like microsoft/DialoGPT-medium or microsoft/DialoGPT-large.
• Fine-tuning: You can fine-tune the model on specific conversation data to make it more suited for particular domains or topics.
• User Interface: Integrate the chatbot into a web or mobile application for more interactive experiences.

By following this tutorial, you have created a simple AI chatbot using Python and a pre-trained transformer model. This setup provides a solid foundation for building more advanced conversational agents and exploring the capabilities of NLP technologies.