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Building a Multimodal Gradio Chatbot with Llama 3.2 Using the Ollama API
- In this tutorial, we will learn how to build an engaging Gradio chatbot powered by Llama 3.2 and Ollama.
- Gradio is an open-source Python library that enables developers to create user-friendly and interactive web applications effortlessly.
- Ollama is an open-source framework that enables developers to run large language models (LLMs) like Llama 3.2 Vision locally on their machines.
- Llama 3.2 represents a significant advancement in large language models (LLMs), introducing multimodal capabilities that enable the processing of both text and images.
- The app.py script is the driver of the multimodal chatbot application. It integrates the Chatbot class from chatbot.py with the Gradio Blocks interface to provide an interactive user interface.
- To follow this guide, you need to have the following Python packages installed: ollama==0.3.3, pillow==11.0.0 (PIL), and gradio==5.5.0.
- The code is modularized across three key files: utils.py, chatbot.py, and app.py. Each file has a specific purpose, making the implementation extensible and easy to understand.
- The result was a sophisticated chatbot capable of generating detailed, concise, or creative responses based on user preferences while maintaining conversation history.
- This project showcased the seamless integration of Gradio, Llama 3.2 Vision, and Ollama’s API, offering developers a practical framework for creating advanced multimodal AI applications.
- In conclusion, this tutorial explains how to build deep learning chatbot with practical examples and detailed walkthrough.
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