SELF-HOSTED LLM INTERFACE AND IMAGE GENERATOR
- The frontend provides an intuitive interface to submit prompts and visualize LLM-generated responses, allowing users to assess language model performance interactively.
- An innovative gate mechanism lets users apply custom rules or “gates” to evaluate outputs for correctness, relevance, or specific metrics, aiding both human and automated evaluation workflows.
- Users benefit from instant feedback on model responses, with tracked accuracy, pass/fail rates, and analytics that help benchmark improvements or regressions during model development.
- A Python Flask API module supports on-demand AI-generated imagery (leveraging popular libraries like Pillow, torch, and diffusers). This adds multimodal testing capabilities for vision-language models, unlocking scenarios such as visual chat or creative prompt illustration.
- The solution’s Docker Compose setup enables isolated, reproducible environments for the frontend, backend (image server), and local LLM service (Ollama). This ensures smooth deployment on local machines, cloud instances, or production with minimal friction.
- With support for environment configuration, Nginx reverse proxy, SSL, and AWS Lightsail deployment scripts, the stack is designed to scale securely from prototype to production. Clear modular code and well-defined APIs make it easy to extend with additional models, evaluation gates, or analytics dashboards.