An end-to-end chatbot powered by transformer-based language models. This project demonstrates how to build a simple conversational agent capable of context-aware, natural responses using Hugging Face pipelines.
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Conversational AI
Real-time interaction using pre-trained transformer models. -
Plug-and-Play
Minimal setup with Hugging Face’s pipeline interface. -
Customizable Response Logic
Simple rules integrated with model outputs for better user experience. -
Scalable Base
Ready to be extended into more advanced systems (like RAG or agent-based bots).
- Python 3
- Hugging Face Transformers (e.g.,
DialoGPT) - NLP Pipeline (text input → tokenization → model → output)
- Jupyter Notebook (for testing)
Uses transformer models such as microsoft/DialoGPT or gpt2 for conversational response generation. Models are loaded with Hugging Face's high-level pipeline interface.
You: Hello, who are you?
Bot: I'm a language model here to chat with you!
You: What's the capital of France?
Bot: Paris.
This notebook showcases the integration of LLMs into a real-time chatbot flow. Ideal for building personal assistants, customer support bots, or testing dialogue models in prototyping phases.
- Freelance chatbot development
- LLM integration demos
- Building foundations for advanced agents
- Portfolio showcasing NLP capabilities