Natural Language Processing Development Tutorials, Guides & Insights

import random

def chatbot_response(user_input):
    user_input = preprocess(user_input)
    for question, response in qa_pairs.items():
        if question in user_input:
            return response
    return "عذراً، لا أفهم سؤالك. هل يمكنك إعادة صياغته؟"

حان وقت التفاعل مع الروبوت:

• Model Loading: We use the transformers library to load the DialoGPT model, which is fine-tuned for conversational tasks.
• Tokenization: The tokenizer converts input text into tokens that the model can process.
• Chat History: We maintain chat history to allow contextually relevant responses.
• Generating Responses: The model generates responses based on the input and chat history.

• 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.