# marker-api

Flask API wrapper around [marker](https://github.com/VikParuchuri/marker) — converts PDFs, DOCX, XLSX, PPTX, EPUB, images (PNG/JPG/BMP/GIF/TIFF/WEBP/HEIC), and HTML to Markdown (JSON/HTML/chunks).

---

## Quick Start

### Container (recommended)

```bash
# Build and run with Podman/Docker
podman build -t marker-api -f Containerfile .
podman run -d --rm \
  --name marker-api \
  -p 8000:8000 \
  -e AMD_COMPUTE=false \
  -e TORCH_DEVICE=cpu \
  marker-api

# Or use docker-compose
docker-compose up -d
```

### Bare-metal

```bash
git clone https://github.com/your-org/marker-api.git /app/marker-api
pip install -e /app/marker-api/marker[full] flask gunicorn
PORT=8000 python /app/marker-api/app.py
```

### Health Check

```bash
curl http://localhost:8000/health
# {"status":"ok","ollama":"http://localhost:11434","torch_device":"cpu","supported_formats":["docx","epub","html","jpg",...]}
```

---

## Endpoints

### `GET /`

HTML documentation page listing all endpoints and parameters.

### `GET /health`

Returns server status, torch device, supported file formats, and configuration.

### `POST /marker`

Convert a single file. Accepts **multipart/form-data** or **application/json**.

#### Multipart form-data

```bash
curl -X POST http://localhost:8000/marker \
  -F "file=@document.pdf" \
  -F "output_format=markdown" \
  -F "force_ocr=false"
```

When `output_format=markdown`, the response is the raw `.md` content as a file download. All other formats return JSON.

#### JSON body (base64)

```bash
curl -X POST http://localhost:8000/marker \
  -H "Content-Type: application/json" \
  -d '{
    "file_b64": "<base64>",
    "filename": "document.pdf",
    "output_format": "markdown",
    "force_ocr": false
  }'
```

### `POST /v1/conversions`

Same as `/marker` but always returns JSON, wrapping the result with an `id` (UUID) field and `filename`.

### `POST /v1/files/convert`

Same as `/marker` but always returns JSON with `filename`, `format`, `output`, `images_b64`, and `metadata`.

---

## Parameters

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `file` / `file_b64` | file / string | **required** | The document to convert |
| `output_format` | string | `markdown` | `markdown`, `json`, `html`, `chunks` |
| `force_ocr` | bool | `false` | Force OCR on all pages (fixes garbled text) |
| `paginate_output` | bool | `false` | Separate pages with horizontal rules |
| `page_range` | string | all | Comma-separated pages/ranges: `"0,5-10"` |
| `disable_image_extraction` | bool | `false` | Skip embedded image extraction |
| `processors` | string | auto | Comma-separated full module paths for custom processors |
| `config_json` | string | none | Path to JSON config file |
| `converter_cls` | string | auto | Full module path of converter (e.g. `marker.converters.table.TableConverter`) |
| `use_llm` | bool | `false` | Use an LLM to improve accuracy |
| `llm_service` | string | `marker.services.ollama.OllamaService` | LLM service class: `gemini`, `vertex`, `claude`, `openai`, `azure_openai`, `ollama` |
| `block_correction_prompt` | string | none | Custom prompt for LLM block correction |
| `redo_inline_math` | bool | `false` | Re-process inline math with LLM |
| `strip_existing_ocr` | bool | `false` | Remove existing OCR text and re-OCR |
| `debug` | bool | `false` | Enable debug logging |

---

## Supported Formats

PDF, DOCX, XLSX, PPTX, EPUB, PNG, JPG, BMP, GIF, TIFF, WEBP, HEIC, HTML, HTM.

---

## Environment Variables

| Variable | Default | Description |
|----------|---------|-------------|
| `OLLAMA_HOST` | `http://localhost:11434` | Ollama instance for OCR fallback / LLM |
| `DEESEEK_OCR_MODEL` | `deepseek-ocr` | OCR model name in Ollama |
| `AMD_COMPUTE` | `false` | Enable AMD ROCm GPU support |
| `TORCH_DEVICE` | auto | PyTorch device: `rocm`, `cuda`, `cpu` |
| `MODEL_DTYPE` | `float32` | Model dtype: `float32`, `bfloat16` |
| `PORT` | `8000` | Listening port |
| `HOST` | `0.0.0.0` | Listening host |

---

## PowerShell Batch Script

A PowerShell script (`marker-convert-powershell/marker-convert.ps1`) is provided for batch conversion.

### Usage

```powershell
.\marker-convert-powershell\marker-convert.ps1 -TargetFolder .\documents

# With options
.\marker-convert-powershell\marker-convert.ps1 .\documents `
  -Force -MaxConcurrency 8 -OutputFormat json -UseLlm -LlmService marker.services.ollama.OllamaService

# Remote API
.\marker-convert-powershell\marker-convert.ps1 .\documents -ApiUrl http://10.0.0.5:8000/marker
```

### Parameters

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `-TargetFolder` | string | (required) | Folder to scan recursively |
| `-ApiUrl` | uri | `http://localhost:8000/marker` | Marker API endpoint |
| `-OutputFormat` | string | `markdown` | `markdown`, `json`, `html`, `chunks` |
| `-Force` | switch | off | Overwrite existing `.md` files |
| `-MaxConcurrency` | int | 4 | Concurrent workers (1-32) |
| `-Timeout` | int | 300 | HTTP timeout in seconds (30-1800) |
| `-PageRange` | string | "" | Page range e.g. `"0,5-10"` |
| `-ForceOcr` | switch | off | Force OCR on all pages |
| `-DisableImageExtraction` | switch | off | Skip image extraction |
| `-UseLlm` | switch | off | Enable LLM enhancement |
| `-LlmService` | string | `marker.services.ollama.OllamaService` | LLM service class |
| `-Processors` | string | "" | Custom processor module paths |
| `-ConfigJson` | string | "" | Path to JSON config file |
| `-ConverterCls` | string | "" | Custom converter class path |

The script writes a CSV report (`_marker_convert_results.csv`) next to each converted file.

---

## Deployment

### AMD GPU (ROCm)

Set `AMD_COMPUTE=true` and `TORCH_DEVICE=cuda` in the container environment. 

#### Vega 20 / GFX906 Support (e.g., powermac)
For older AMD GPU architectures like Vega 20 (GFX906), AMD dropped standard ROCm PyTorch support. This project fully supports GFX906 out of the box by using a patched GFX906 PyTorch base image (`mixa3607/pytorch-gfx906:v2.7.1-rocm-6.3.3`).

To run the container on GFX906 GPUs with hardware acceleration:
```bash
docker run -d --name marker-api \
  --network=host \
  --device /dev/kfd --device /dev/dri \
  --group-add 984 --group-add 988 \
  -e AMD_COMPUTE=true \
  -e TORCH_DEVICE=cuda \
  -e CUDA_VISIBLE_DEVICES=1 \
  -e HSA_OVERRIDE_GFX_VERSION=9.0.6 \
  marker-api:latest \
  gunicorn --bind 0.0.0.0:8000 --workers 1 --timeout 300 --worker-class gthread --threads 4 app:app_instance
```

*Note on first run:* The very first conversion on a ROCm GPU will take 2-3 minutes as MIOpen compiles convolution kernels for your exact GPU. Subsequent runs are fully cached and take ~4-5 seconds per document.
*Note on memory:* If other services (e.g., llama-servers) are utilizing GPU 0, restrict `marker-api` to GPU 1 (`CUDA_VISIBLE_DEVICES=1`) and use a single worker (`--workers 1`) to prevent HIP Out-Of-Memory errors.

### CPU-only

```bash
TORCH_DEVICE=cpu AMD_COMPUTE=false podman run ... marker-api
```

### GPU (CUDA)

Override the PyTorch index URL during build, or use the existing ROCm image with `TORCH_DEVICE=cuda`.

---

## Response Codes

| Code | Meaning |
|------|---------|
| `200` | Successful conversion (markdown returned as file download, others as JSON) |
| `400` | Missing file or invalid body |
| `500` | Conversion error (details in JSON body) |

Error responses have the shape:

```json
{"success": false, "error": "error message"}
```