# How to Find Specific GPT-Image2 Case Examples by Category: A Complete Guide

> Discover GPT-Image2 case examples by category. This guide shows how to filter GPT-Image2 examples using JavaScript, Python, or the web interface, leveraging the structured data/cases.json file.

- Repository: [苍何/awesome-gpt-image-2](https://github.com/freestylefly/awesome-gpt-image-2)
- Tags: how-to-guide
- Published: 2026-09-01

---

**GPT-Image2 case examples are stored in the centralized [`data/cases.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/cases.json) file with structured category metadata, enabling precise filtering via JavaScript, Python, or the live web interface at gpt-image2.canghe.ai.**

The freestylefly/awesome-gpt-image-2 repository hosts 544 curated prompt cases for GPT-Image2, organized into distinct visual categories. Whether you're researching UI design patterns or historical Chinese art styles, understanding how to find specific GPT-Image2 case examples by category will streamline your workflow. The repository's architecture separates data storage from presentation, offering multiple pathways to access categorized content.


## Understanding the Repository Structure

The repository maintains all case data in a single source of truth located at [`data/cases.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/cases.json). This JSON array contains objects with standardized fields including **id**, **title**, **category**, and **description**.

According to the source code, the **Category Overview** section in [`README.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/README.md) (lines 76-84) defines thirteen high-level groups: UI & Interfaces, Charts & Infographics, Posters & Typography, Products & E-commerce, Brand & Logos, Architecture & Spaces, Photography & Realism, Illustration & Art, Characters & People, Scenes & Storytelling, History & Classical Chinese Themes, Documents & Publishing, and Other Use Cases.

The build pipeline regenerates this dataset automatically. The script `scripts/generate-site-data.mjs` parses the source markdown and updates [`data/cases.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/cases.json) whenever new cases are added, ensuring the category mappings remain current.


## Method 1: Programmatic Filtering by Category

For developers integrating these cases into tools or research pipelines, direct JSON manipulation offers the most flexibility.

### JavaScript and Node.js Implementation

Fetch the raw JSON directly from GitHub and filter using the `category` property:

```javascript
// Fetch the canonical case database
fetch('https://raw.githubusercontent.com/freestylefly/awesome-gpt-image-2/main/data/cases.json')
  .then(response => response.json())
  .then(cases => {
    // Filter to specific design discipline
    const uiCases = cases.filter(c => c.category === 'UI & Interfaces');
    console.log(`Located ${uiCases.length} UI cases`);
    
    uiCases.forEach(c => {
      console.log(`Case #${c.id}: ${c.title}`);
      // Direct link to gallery documentation
      console.log(`→ https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/gallery-part-1.md#case-${c.id}`);
    });
  })
  .catch(err => console.error('Data fetch failed:', err));

```

### Python Data Retrieval

Use standard library modules to categorize cases locally:

```python
import requests
import json

def load_cases():
    url = "https://raw.githubusercontent.com/freestylefly/awesome-gpt-image-2/main/data/cases.json"
    return json.loads(requests.get(url).text)

def filter_by_category(cases, category_name):
    return [c for c in cases if c["category"] == category_name]

cases = load_cases()
architecture_cases = filter_by_category(cases, "Architecture & Spaces")

print(f"Architecture cases available: {len(architecture_cases)}")
for case in architecture_cases[:5]:  # Preview first 5

    print(f'#{case["id"]} - {case["title"]}')

```


## Method 2: Browsing Markdown Galleries

The repository provides human-readable galleries for manual exploration. The file [`docs/gallery.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/gallery.md) contains anchor links for each category (e.g., `#cat-ui`, `#cat-poster`), enabling quick navigation.

For performance optimization, the gallery splits into two parts:

- [`docs/gallery-part-1.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/gallery-part-1.md): Cases 1-165
- [`docs/gallery-part-2.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/gallery-part-2.md): Cases 166-544

Both files maintain consistent category anchor identifiers derived from the classification system outlined in [`README.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/README.md) lines 85-115. Access a specific category directly using URL fragments like `gallery.md#cat-ui` for UI & Interfaces.


## Method 3: Using the Live Web Interface

The production deployment at `https://gpt-image2.canghe.ai/` implements client-side filtering. The entry point [`src/main.jsx`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/src/main.jsx) fetches [`data/cases.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/cases.json) at runtime and renders an interactive interface.

To find cases by category without writing code:

1. Navigate to the gallery page
2. Select the **Category** dropdown menu
3. Choose your target category (e.g., "Photography & Realism")
4. The interface filters the 544 cases instantly on the client side

This approach leverages the same underlying JSON structure but requires no API calls or repository cloning.


## Complete Category Reference

When filtering programmatically or browsing documentation, use these exact category strings as they appear in the `category` field of [`data/cases.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/cases.json):

- **UI & Interfaces**
- **Charts & Infographics**
- **Posters & Typography**
- **Products & E-commerce**
- **Brand & Logos**
- **Architecture & Spaces**
- **Photography & Realism**
- **Illustration & Art**
- **Characters & People**
- **Scenes & Storytelling**
- **History & Classical Chinese Themes**
- **Documents & Publishing**
- **Other Use Cases**


## Summary

- **Centralized data**: All 544 cases reside in [`data/cases.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/cases.json) with consistent metadata fields including `id`, `title`, and `category`.
- **Three access methods**: Query via JavaScript/Python using the raw JSON endpoint, browse static markdown galleries ([`docs/gallery.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/gallery.md) and parts), or use the live web UI at gpt-image2.canghe.ai.
- **Thirteen categories**: Cases organize into specific visual disciplines ranging from UI design to Classical Chinese themes.
- **Build automation**: The `scripts/generate-site-data.mjs` script ensures category assignments remain synchronized when new cases are added.


## Frequently Asked Questions

### How often is the case database updated?

The [`data/cases.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/cases.json) file regenerates automatically via `scripts/generate-site-data.mjs` whenever the repository builds. New cases added to the documentation are immediately reflected in the JSON structure, making the dataset consistently current with the repository's markdown source files.

### Can I link directly to a specific category within the gallery documentation?

Yes. The markdown galleries use standardized anchor identifiers. Append fragment identifiers like `#cat-ui` for UI & Interfaces or `#cat-poster` for Posters & Typography to the gallery URLs (e.g., `docs/gallery.md#cat-ui`). These anchors appear in both [`gallery.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/gallery.md) and the paginated part files ([`gallery-part-1.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/gallery-part-1.md) and [`gallery-part-2.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/gallery-part-2.md)).

### What fields are available for each case in the JSON data?

Each case object in [`data/cases.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/cases.json) contains a numeric **id**, string **title**, **category** classification matching the thirteen standard groups, and a descriptive text field. This structure enables filtering, sorting, and mapping operations in both frontend applications ([`src/main.jsx`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/src/main.jsx)) and external scripts.

### Is there a rate limit when fetching cases.json from GitHub raw content?

While GitHub's raw content delivery generally permits standard repository access patterns, implement local caching for production applications. The file size for 544 cases remains lightweight, but consider cloning the repository or implementing a caching layer if making frequent programmatic queries to avoid potential IP-based throttling.