Gera Tools

Structured LLM Output → HTML Form

Turn a JSON schema or LLM object output into a rendered HTML form.

Paste a JSON Schema or a sample JSON object from an LLM and the tool generates a matching HTML form — text, number, checkbox, select, and textarea fields inferred from types and enums — so you can build admin UIs around LLM-extracted structured data. It runs free in your browser on Gera Tools, with nothing uploaded.

Last updated Source: Gera Tools

Does it accept JSON Schema or plain JSON?

Both. If the input has a top-level \"properties\" object it is treated as JSON Schema and uses each property's type, enum, and description. Otherwise it is treated as a sample object and infers the field type from each value (string, number, boolean, or array).

Structured LLM output → HTML form

When you extract structured data with an LLM — using a JSON Schema or a function / tool definition — you usually need a UI for a human to review and edit the result. Hand-building that form for every schema is repetitive. This tool reads your JSON Schema or a sample object and instantly generates a matching, interactive HTML form, then hands you the markup to drop into an admin panel.

The problem this solves

Structured LLM output is useful — a model classifies a support ticket and returns { "category": "billing", "priority": "high", "summary": "User cannot access invoice" }. But that data usually needs a human review step before it enters a workflow: confirm the classification, edit the summary, set a flag. Without a form UI, that review happens in raw JSON or in a manually built admin panel. This tool generates the form from the schema automatically, skipping the build step entirely.

How it works

The tool first decides what you gave it: if the JSON has a top-level properties object, it is parsed as JSON Schema and each property contributes a field driven by its type, optional enum, and description. Otherwise the input is treated as a sample object and the field type is inferred from each value — strings become text inputs (long strings become textareas), numbers become number inputs, booleans become checkboxes, and arrays render as a comma-separated text field. Any property with an enum becomes a <select>. The form is rendered live so you can interact with it, and a Copy HTML button emits clean, framework-agnostic markup with proper labels and name attributes.

Example: schema input vs sample input

From a JSON Schema:

{
  "type": "object",
  "properties": {
    "category": { "type": "string", "enum": ["billing", "technical", "general"] },
    "priority":  { "type": "string", "enum": ["low", "medium", "high"] },
    "summary":   { "type": "string" }
  }
}

Generates: two <select> dropdowns (for category and priority) and a <textarea> for summary (the field name signals a description-length value).

From a sample object:

{ "score": 87, "passed": true, "notes": "Passed all checks" }

Generates: a <input type="number"> for score, a <input type="checkbox"> for passed, and a <textarea> for notes.

Tips and notes

  • Schema beats sample. A JSON Schema gives you enums, descriptions, and explicit types, producing a richer form than inferring from a single sample.
  • Long strings get textareas. Values over ~60 characters render as a multi-line field automatically, which is right for descriptions and bodies.
  • Names come from keys. The generated name attributes match your property keys, so wiring the form back to your data model is direct.
  • Everything is local. No data leaves your browser.
  • The generated HTML is framework-agnostic vanilla markup — paste it into React, Vue, or plain HTML and add your own submit handler.