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Streamlit Integration with Ollama and LLAVA

Presenter​

Karim Lalani is a local LLM and voice integration expert at Infinidigm LLC, based in Leander, TX. His pioneering work in Ollama implementations and local LLM deployment strategies has helped the Austin LangChain community build sophisticated AI applications that run entirely on local infrastructure.

"We use OLAMA inference. This is quite an interesting project. If you have a Mac or Linux machine, you can use quite a few locales, even on your laptop... They use the Docker philosophy, where you have a model file where you can describe your language model, your prompts, your templates, and any parameters, and you can create new derivative language models."

Connect with Karim:

Lab Overview​

Learn how to integrate Streamlit with Ollama and LLAVA for creating powerful web interfaces with local LLM capabilities. This lab focuses on multimodal processing using locally-run models, demonstrating how to build sophisticated AI applications without relying on cloud services.

Key Features​

  • Local LLM processing with Ollama
  • LLAVA multimodal capabilities
  • Image analysis and description
  • Real-time processing
  • Docker-style model management

Technical Components​

  • Ollama setup and configuration
  • LLAVA model integration
  • Streamlit interface development
  • Local model management
  • Image processing pipeline

Implementation Details​

"Olama, because it is designed to help work locally, the default model magnitude is 4G. Which needs to be small enough to run on the biggest modern laptops, even on CPUs."

Model Configuration​

  • Uses Baklava model for multimodal processing
  • Supports CPU and GPU execution
  • Default 4GB model size for broad compatibility
  • Docker-style model management system

Integration Steps​

  1. Ollama Service Setup

    • Download latest binary
    • Start Ollama service
    • Configure API endpoints

  2. Model Management

    • Pull required models
    • Configure model parameters
    • Set up templates and prompts

  3. Streamlit Interface

    • Configure file upload utilities
    • Set up image display
    • Implement chat interface
    • Handle session state

  4. Image Processing

    • Image loading and preprocessing
    • Context preservation
    • Response generation
    • Result visualization

Best Practices​

  • Configure proper model paths
  • Manage memory usage effectively
  • Implement error handling
  • Optimize for local processing
  • Cache responses when appropriate

System Requirements​

"It runs on an Alienware testtop that runs on an NVIDIA 4090 graphics card. Olama, because it is designed to help work locally, the default model magnitude is 4G."

  • Supported Operating Systems:
    • macOS
    • Linux
    • Windows (via WSL-2)
  • Minimum 4GB RAM for base models
  • GPU optional but recommended for performance
  • NVIDIA CUDA support for GPU acceleration

Key Insights​

"You can use this multimodel without transferring img. I turned to this to make it work only under imig transmissions. This gives you a glimpse of the possibilities, not just from multimodel crayfish, but the image you can build with this framework, and how you can work locally."

The system can:

  • Process images locally without cloud dependencies
  • Generate natural language descriptions of images
  • Handle multiple types of visual inputs
  • Scale based on available hardware

Resources​