# VLM Demo

> *VLM Demo*: Lightweight repo for chatting with models loaded into *VLM Bench*.

---

## Installation

This repository can be installed as follows:

```bash
git clone git@github.com:TRI-ML/vlm-demo.git
cd vlm-demo
pip install -e .
```

This repository also requires that the `vlm-bench` package (`vlbench`) and 
`prismatic-vlms` package (`prisma`) are installed in the current environment.
These can both be installed from source from the following git repos:

`vlm-bench`: `https://github.com/TRI-ML/vlm-bench`
`prismatic-vlms`: `https://github.com/TRI-ML/prismatic-vlms`

## Usage

The main script to run is `interactive_demo.py`, while the implementation of 
the Gradio Controller (`serve/gradio_controller.py`) and Gradio Web Server
(`serve/gradio_web_server.py`) are within `serve`. All of this code is heavily
adapted from the [LLaVA Github Repo:](https://github.com/haotian-liu/LLaVA/blob/main/llava/serve/).
More details on how this code was modified from the original LLaVA repo is provided in the 
relevant source files.

To run the demo, run the following commands:

+ Start Gradio Controller: `python -m serve.controller --host 0.0.0.0 --port 10000`
+ Start Gradio Web Server: `python -m serve.gradio_web_server --controller http://localhost:10000 --model-list-mode reload --share`
+ Run interactive demo: `CUDA_VISIBLE_DEVICES=0 python -m interactive_demo  --port 40000  --model_dir <PATH TO MODEL CKPT>`

## Contributing

Before committing to the repository, *make sure to set up your dev environment!*

Here are the basic development environment setup guidelines:

+ Fork/clone the repository, performing an editable installation. Make sure to install with the development dependencies
  (e.g., `pip install -e ".[dev]"`); this will install `black`, `ruff`, and `pre-commit`.

+ Install `pre-commit` hooks (`pre-commit install`).

+ Branch for the specific feature/issue, issuing PR against the upstream repository for review.