GPT-WEB-JAVA is a storefront with a chat backend, and the README says so first

The repository is six files and a store front

The tracked tree at the root is short enough to read in one look: .gitignore, LICENSE, README.md, pom.xml, src/ and start.sh. There are no published releases. The README states that the demo address is closed, that a version 3.0 ships a new UI with mobile adaptation for both the client and the admin side, and that purchasing gets you into a VIP group for continued updates. That framing is the first fact about the project, and it is visible in the top third of the page rather than buried.

The declared stack comes from badges at the top: OpenJDK 8, MySQL 8.0 and Redis 7. The GitHub description is written in Chinese and lists the commercial surface, including WeChat public account integration, card key redemption, a payment gateway, public account traffic and email registration. The topics on the repository are a different register again, listing bard-api, chatgpt, google, midjourney-api, spark and stable-diffusion.

So there are three different descriptions of the same repository: a Java 8 and MySQL 8 and Redis 7 backend, a commercial bundle with payment and card keys, and a topic list centered on image generation. None of them is wrong. They just answer different questions, and the one to read carefully before installing is the first, because the second and third assume you already have accounts on someone else's platform.

Streaming works only if the reverse proxy stops buffering

The GPT integration is described as SSE based streaming over a WebSocket style push, with support for the 3.5 and 4.0 model families, official or third party API addresses, GPT-4.0 image recognition and DALL-E 3 image generation. The same streaming treatment is described for the Spark models at 2.0, 3.0 and 3.5.

The operational note is the one worth remembering.

In the original the instruction reads that if you deploy behind Nginx you must turn the buffer cache off, because SSE delivers its value only if bytes reach the browser as they are produced. A default Nginx configuration will hold responses in a buffer and the interface will look like a slower version of a normal request, which sends people looking for a code bug that does not exist. The README also tells you to put the API key in the gpt_key configuration entry and to watch the difference between 3.5 and 4.0 keys, and it points at a proxy tutorial for networks where the upstream API is not directly reachable, plus a Cloudflare option for the same problem.

Four entries in the image configuration table carry most of the operational weight, and the same pattern repeats for Stable Diffusion.

sys_config
img_upload_url
img_return_url
is_open_sd
+

+ +Reading that list as a set of names rather than as code keeps it honest, but it does show the shape: one table for system settings, separate tables for Stable Diffusion models and LoRAs, and a switch that turns the feature on. Everything this system can do is a row in a MySQL table, and the README walks through populating them.

Image storage is three fields and a date folder

The image upload instructions are the most concrete part of the README, and they describe a seven step procedure rather than a feature.

You create a directory on the server, such as /usr/local/upload. You put that path into the img_upload_url field of the sys_config table, and the README insists the trailing slash matters. You point Nginx at that directory as a static resource location. You set img_return_url to the domain or IP that Nginx serves, for example https://www.yourdomain.com. Uploads then create a subfolder named after the current date, and the final absolute path a client receives is img_return_url plus img_upload_url plus the file name.

Two naming rules follow from that. Midjourney outputs are named after the task ID, and everything else is named with a current timestamp. Task ID naming is the useful one, because it means the filename in the URL is the same identifier you use to look up progress or failures, so a broken image can be traced without a database query.

Storage itself is pluggable between local disk and Aliyun OSS, and the README says the choice can be switched dynamically from the admin side. A system that lets you move from local disk to object storage at runtime is making a claim about its configuration layer, and the sys_config table is the place where that claim gets tested.

Midjourney arrives through a Discord token you extract yourself

The Midjourney support is the widest feature list in the README and also the one with the most operational consequence. The documented surface includes /imagine for text to image, /describe for image to text, redo, the --relax and --fast mode switches, U upscale, V variation, Strong and Subtle styles, U2x and U4x, ZoomOut 2x and ZoomOut 1.5x, position offsets, /shorten for prompt parsing, /blend for image mixing, reference images, and account pool management.

The setup procedure tells you to create your own private Midjourney server or channel, invite the bot to it, and read the connection address out of the browser, where a URL of the form https://discord.com/channels/123/456 gives you mj_guild_id as 123 and mj_channel_id as 456. The third credential, mj_user_token, is obtained by logging into the Discord web client, opening developer tools, and capturing the Authorization header value from any request in the network panel.

Both facts belong in the same sentence, because together they describe the integration: the system acts as a Discord user rather than as an official API client, and the token it needs is a session credential from a logged in browser. That has two consequences worth planning for. A session token can expire or be invalidated, which is the likely reason the README includes account pool management in the feature list, and the behavior you get is bound by Discord's terms rather than by a documented API contract. If you deploy this, budget for token maintenance instead of assuming an integration you configure once.

Stable Diffusion is a local WebUI you have to start in API mode

Stable Diffusion support is a smaller feature set, described as model selection, LoRA selection, high resolution fix and reference images. The setup is four steps and the fourth one is a trap for people who already have a working WebUI.

Configure models in the sd_model table with the full file suffix and a preview image. Configure LoRAs in sd_lora the same way. Set is_open_sd to 1 in sys_config. Then set sd_url, where the local default is usually http://127.0.0.1:7860, and the README warns in bold that you must start the WebUI with the api flag.

Without that flag the WebUI serves a human interface and refuses API calls, so the symptom is a feature that looks configured and never returns anything. Nothing in the model table reveals the problem, because the table is fine. This is the shape of configuration errors in this project generally: the failure surfaces at the boundary to another system, and the README answers it with a deployment instruction rather than with an error message in the product.

There is also a Baidu translation and content moderation integration, described as doing text safety review on input for all three image and chat backends, plus automatic translation of image prompts. Application flows for both the translation API and the text moderation platform are in the wiki rather than in the README.

What is missing is the code

The gap between what the README describes and what the repository contains is worth stating plainly, because it changes how you should evaluate the project.

There is a pom.xml, which means the build is Maven based and the dependency list is inside it rather than in anything the README shows. There is a start.sh, which is where the real runtime assumptions live, including the database and Redis connection details, the port, and whether the JAR is built or downloaded. There is a src directory. And there are no published releases, which means there is no artifact to inspect before you build, and no release notes telling you what changed.

Apache 2.0 is the declared license, both in the repository metadata and in the README's license line. That governs what you may do with the code that is present. It does not tell you what you received, and here the received set is a build file, a start script and documentation.

So the practical evaluation order is: read start.sh first because it defines the deployment contract, then read pom.xml for the dependency versions, and only then look at the feature list and decide whether the configuration approach fits what you are building. The 778 stars and 197 forks are consistent with a project whose value has mostly been distributed through its README and its paid support channel, which is a reasonable thing for the author to have built and a poor substitute for source you can inspect.

Editorial conclusion

Treat this repository as a product listing that happens to include a build file, not as an open source Java project you can build and extend. There is a pom.xml and a start.sh at the root and a src directory, but the tracked file list is six entries with no published release and no source files, so there is nothing here to read before you buy. What the README does deliver is an unusually complete integration inventory: SSE streaming with the buffer cache turned off at the proxy, an image pipeline whose final URL is assembled from three separate configuration fields, a Stable Diffusion WebUI that has to be started with the api flag, and a Discord bot token that you are told to lift out of a browser request header. Every one of those is a deployment decision you will have to make, and the wiki pages the README links are where the detail sits.

Frequently asked questions

Official sources

1. a616567126/GPT-WEB-JAVA on GitHub
2. Issues
3. License: Apache-2.0
4. README