Mastering AI Vision Chatbot Development with Ollama & Streamlit

Welcome to our comprehensive tutorial on the multimodal llava model where we'll guide you through the process of building an image analyzer chatbot using Ollama and LLava - two powerful open-source tools. Whether you're a seasoned developer or just starting out, this step-by-step walkthrough will help you grasp the concepts and implement them effectively.
In this tutorial, we'll cover everything you need to know, from installing Ollama on your computer to developing your chatbot using Streamlit. With LLava natural language processing capabilities and vision capabilities , you'll be able to create a chatbot that can analyze images and respond intelligently to user queries. You can run LLava on Ollama using a Windows PC or Mac.
Key Topics Covered:
* Introduction to Ollama and LLava
* Installing Ollama on your computer
* Pulling Ollama models down to your computer
* Building the image analyzer chatbot with Streamlit
* Tutorial how to use model ollama - llava
By the end of this tutorial, you'll have a fully functional image analyzer chatbot that you can customize to fit your needs. So, let's dive in and start building together!
Don't forget to like, share, and subscribe for more tutorials on AI, machine learning, and software development. Happy coding! 🤖🚀
Chapters:
00:00:00 - Intro
00:00:20 - Installing Ollama
00:02:09 - Ollama Commands
00:02:05 - Ollama Models
00:05:05 - Dowloading LLava - ollama multimodal model
00:07:58 - Testing LLava model
00:10:05 - Coding Image Chatbot
00:31:09 - Testing Complete Image Chatbot
Project Code:
🧑‍💻 Starter Project branch - github.com/AIDevBytes/LLava-I...
👩‍💻 Completed code - github.com/AIDevBytes/LLava-I...
🔗 Websites
Streamlit - streamlit.com
Ollama - ollama.com
🔗 Other Videos
Quick Start: 🚀 Set Up Your Streamlit Dev Environment in Under 5 Minutes! • How to Setup a Streaml...
Ollama & Windows | Run FREE Local UNCENSORED AI Models on Windows with Ollama • OLLAMA | Want To Run U...
#Ollama #LLava #Chatbot #ImageAnalysis #AI #OpenSource #MachineLearning #streamlit #codingtutorial
_____________________________________
🔔 / @aidevbytes Subscribe to our channel for more tutorials and coding tips
👍 Like this video if you found it helpful!
💬 Share your thoughts and questions in the comments section below!
GitHub: github.com/AIDevBytes
🏆 My Goals for the Channel 🏆
_____________________________________
My goal for this channel is to share the knowledge I have gained over 20+ years in the field of technology in an easy-to-consume way. My focus will be on offering tutorials related to cloud technology, development, generative AI, and security-related topics.
I'm also considering expanding my content to include short videos focused on tech career advice, particularly aimed at individuals aspiring to enter "Big Tech." Drawing from my experiences as both an individual contributor and a manager at Amazon Web Services, where I currently work, I aim to share insights and guidance to help others navigate their career paths in the tech industry.
_____________________________________

[@AIDevBytes]

Comment below what other types of videos/video topics you would like to see on the channel.

[@KadirErturk]

This is great, adding voice to this app might be a good idea

[@AIDevBytes]

👍 Thanks, I'll be creating a text-to-speech bot in the future.

[@sania3631]

Thanks from Morocco 🇲🇦! Great video and concise explanations. I had first downloaded the llava model via LM Studio and struggled to pull it later on from Ollama. Your tutorial was very helpful.

[@MillaGamer]

Tks from brazil, keep posting videos.

[@AIDevBytes]

More to come!
Also, what other types of videos would you like to see? I like to get direct feedback from viewers.

[@theh1ve]

This is fab thank you, how could this be updated so that if you upload an image it switches to say llava model, however if you don't you can use it as a normal chatbot with llama? That would be cool.

[@AIDevBytes]

glad you liked the video.
You could update the code to look something like this. Example code snippet 👇:
if uploaded_file is None:
# calling regular model with chat helper function
llm_stream = chat(user_prompt, model=model)
# streams the response back to the screen
stream_output = st.write_stream(llm_stream)
else:
# calling the image model
stream = analyze_image_file(uploaded_file, model=image_model, user_prompt=chat_input)
stream_output = st.write_stream(stream)
Hope this helps.
Here is a video for creating just a regular Ollama chatbot. A lot of the concepts are the same. th-cam.com/video/vDD_L0ab-FY/w-d-xo.html