From 7ff8aa79acd793928a3b580a2a3035699e763741 Mon Sep 17 00:00:00 2001 From: Michele Dolfi Date: Tue, 10 Dec 2024 14:30:15 +0100 Subject: [PATCH] api v1alpha1 Signed-off-by: Guillaume Moutier --- .dockerignore | 40 + Containerfile | 63 +- Makefile | 4 +- README.md | 239 +++- docling_serve/.env.example | 3 + docling_serve/app.py | 435 +++---- docling_serve/docling_conversion.py | 335 +++++ docling_serve/gradio_ui.py | 629 ++++++++++ docling_serve/helper_functions.py | 30 + docling_serve/response_preparation.py | 237 ++++ img/swagger.png | Bin 0 -> 33481 bytes img/ui-input.png | Bin 0 -> 86478 bytes img/ui-output.png | Bin 0 -> 170197 bytes models_download.py | 36 + os-packages.txt | 8 + poetry.lock | 1676 +++++++++++++------------ pyproject.toml | 30 +- tests/2206.01062v1.pdf | Bin 0 -> 4310680 bytes tests/2408.09869v5.pdf | Bin 0 -> 5566575 bytes tests/test_1-file-all-outputs.py | 129 ++ tests/test_1-url-all-outputs.py | 120 ++ tests/test_2-files-all-outputs.py | 74 ++ tests/test_2-urls-all-outputs.py | 65 + 23 files changed, 3076 insertions(+), 1077 deletions(-) create mode 100644 .dockerignore create mode 100644 docling_serve/.env.example create mode 100644 docling_serve/docling_conversion.py create mode 100644 docling_serve/gradio_ui.py create mode 100644 docling_serve/helper_functions.py create mode 100644 docling_serve/response_preparation.py create mode 100644 img/swagger.png create mode 100644 img/ui-input.png create mode 100644 img/ui-output.png create mode 100644 models_download.py create mode 100644 os-packages.txt create mode 100644 tests/2206.01062v1.pdf create mode 100644 tests/2408.09869v5.pdf create mode 100644 tests/test_1-file-all-outputs.py create mode 100644 tests/test_1-url-all-outputs.py create mode 100644 tests/test_2-files-all-outputs.py create mode 100644 tests/test_2-urls-all-outputs.py diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..231e71f --- /dev/null +++ b/.dockerignore @@ -0,0 +1,40 @@ +# Ignore Python cache files +__pycache__/ +**/__pycache__/ +*.pyc +*.pyo +*.pyd + +# Ignore virtual environments +env/ +venv/ + +# Ignore development artifacts +*.log +*.db +*.sqlite3 + +# Ignore configuration and sensitive files +**/.env +*.env +*.ini +*.cfg + +# Ignore IDE and editor settings +.vscode/ +.idea/ +*.swp +*.swo + +# Ignore Git files +.git/ +.gitignore + +# Ignore Docker files themselves (optional if not needed in the image) +.dockerignore +Dockerfile* + +# Ignore build artifacts (if applicable) +build/ +dist/ +*.egg-info diff --git a/Containerfile b/Containerfile index 61ba2ba..3da08df 100644 --- a/Containerfile +++ b/Containerfile @@ -1,32 +1,59 @@ -FROM python:3.11-slim-bookworm +ARG BASE_IMAGE=quay.io/sclorg/python-312-c9s:c9s + +FROM ${BASE_IMAGE} ARG CPU_ONLY=false -WORKDIR /docling-serve -RUN apt-get update \ - && apt-get install -y libgl1 libglib2.0-0 curl wget git \ - && apt-get clean +USER 0 -RUN pip install --no-cache-dir poetry +################################################################################################### +# OS Layer # +################################################################################################### -COPY pyproject.toml poetry.lock README.md /docling-serve/ +RUN --mount=type=bind,source=os-packages.txt,target=/tmp/os-packages.txt \ + dnf -y install --best --nodocs --setopt=install_weak_deps=False dnf-plugins-core && \ + dnf config-manager --best --nodocs --setopt=install_weak_deps=False --save && \ + dnf config-manager --enable crb && \ + dnf -y update && \ + dnf install -y $(cat /tmp/os-packages.txt) && \ + dnf -y clean all && \ + rm -rf /var/cache/dnf -RUN if [ "$CPU_ONLY" = "true" ]; then \ - poetry install --no-root --with cpu; \ - else \ - poetry install --no-root; \ - fi +ENV TESSDATA_PREFIX=/usr/share/tesseract/tessdata/ -ENV HF_HOME=/tmp/ -ENV TORCH_HOME=/tmp/ +################################################################################################### +# Docling layer # +################################################################################################### -RUN poetry run python -c 'from docling.pipeline.standard_pdf_pipeline import StandardPdfPipeline; artifacts_path = StandardPdfPipeline.download_models_hf(force=True);' +USER 1001 + +WORKDIR /opt/app-root/src # On container environments, always set a thread budget to avoid undesired thread congestion. ENV OMP_NUM_THREADS=4 -COPY ./docling_serve /docling-serve/docling_serve +ENV LANG=en_US.UTF-8 +ENV LC_ALL=en_US.UTF-8 +ENV PYTHONIOENCODING=utf-8 + +COPY --chown=1001:0 pyproject.toml poetry.lock models_download.py README.md ./ + +RUN pip install --no-cache-dir poetry && \ + # We already are in a virtual environment, so we don't need to create a new one, only activate it. + poetry config virtualenvs.create false && \ + source /opt/app-root/bin/activate && \ + if [ "$CPU_ONLY" = "true" ]; then \ + poetry install --no-root --no-cache --no-interaction --all-extras --with cpu --without dev; \ + else \ + poetry install --no-root --no-cache --no-interaction --all-extras --without dev; \ + fi && \ + echo "Downloading models..." && \ + python models_download.py && \ + chown -R 1001:0 /opt/app-root/src && \ + chmod -R g=u /opt/app-root/src + +COPY --chown=1001:0 --chmod=664 ./docling_serve ./docling_serve -EXPOSE 5000 +EXPOSE 8080 -CMD ["poetry", "run", "uvicorn", "--port", "5000", "--host", "0.0.0.0", "docling_serve.app:app"] +CMD ["python", "docling_serve/app.py"] diff --git a/Makefile b/Makefile index 6525afd..1e01f53 100644 --- a/Makefile +++ b/Makefile @@ -25,14 +25,14 @@ md-lint-file: $(CMD_PREFIX) touch .markdown-lint .PHONY: docling-serve-cpu-image -docling-serve-cpu-image: Containerfile ## Build docling-serve "cpu only" continaer image +docling-serve-cpu-image: Containerfile ## Build docling-serve "cpu only" container image $(ECHO_PREFIX) printf " %-12s Containerfile\n" "[docling-serve CPU ONLY]" $(CMD_PREFIX) docker build --build-arg CPU_ONLY=true -f Containerfile --platform linux/amd64 -t ghcr.io/ds4sd/docling-serve-cpu:$(TAG) . $(CMD_PREFIX) docker tag ghcr.io/ds4sd/docling-serve-cpu:$(TAG) ghcr.io/ds4sd/docling-serve-cpu:main $(CMD_PREFIX) docker tag ghcr.io/ds4sd/docling-serve-cpu:$(TAG) quay.io/ds4sd/docling-serve-cpu:main .PHONY: docling-serve-gpu-image -docling-serve-gpu-image: Containerfile ## Build docling-serve continaer image with GPU support +docling-serve-gpu-image: Containerfile ## Build docling-serve container image with GPU support $(ECHO_PREFIX) printf " %-12s Containerfile\n" "[docling-serve with GPU]" $(CMD_PREFIX) docker build --build-arg CPU_ONLY=false -f Containerfile --platform linux/amd64 -t ghcr.io/ds4sd/docling-serve:$(TAG) . $(CMD_PREFIX) docker tag ghcr.io/ds4sd/docling-serve:$(TAG) ghcr.io/ds4sd/docling-serve:main diff --git a/README.md b/README.md index 041e9a9..9dd4faa 100644 --- a/README.md +++ b/README.md @@ -3,54 +3,251 @@ Running [Docling](https://github.com/DS4SD/docling) as an API service. > [!NOTE] -> This is an unstable draft implementation which will quickly evolve. +> This is an alpha version of the API that will quickly evolve. ## Development -Install the dependencies +### CPU only ```sh # Install poetry if not already available curl -sSL https://install.python-poetry.org | python3 - +# Install dependencies +poetry install --with cpu + +# Run the server +poetry run python docling_serve/app.py +``` + +### Cuda GPU + +For GPU support use the following command: + +```sh # Install dependencies poetry install # Run the server -poetry run uvicorn docling_serve.app:app --reload +poetry run python docling_serve/app.py +``` + +### Environment variables + +The following variables are available: + +`TESSDATA_PREFIX`: Tesseract data location, example `/usr/share/tesseract/tessdata/`. +`UVICORN_WORKERS`: Number of workers to use. +`RELOAD`: If `True`, this will enable auto-reload when you modify files, useful for development. + +## Usage + +The API provides two endpoints: one for urls, one for files. This is necessary to send files directly in binary format instead of base64-encoded strings. + +### Common parameters + +On top of the source of file (see below), both endpoints support the same parameters, which are the same as the Docling CLI. + +- `from_format` (Optional[Union[List[str], str]]): Input format(s) to convert from. Allowed values: `docx`, `pptx`, `html`, `image`, `pdf`, `asciidoc`, `md`. Defaults to all formats. +- `to_format` (Optional[Union[List[str], str]]): Output format(s) to convert to. Allowed values: `md`, `json`, `html`, `text`, `doctags`. Defaults to `md`. +- `do_ocr` (Optional[bool]): If enabled, the bitmap content will be processed using OCR. Defaults to `True`. +- `image_export_mode`: Image export mode for the document (only in case of JSON, Markdown or HTML). Allowed values: embedded, placeholder, referenced. Optional, defaults to `embedded`. +- `force_ocr` (Optional[bool]): If enabled, replace any existing text with OCR-generated text over the full content. Defaults to `False`. +- `ocr_engine` (Optional[str]): OCR engine to use. Allowed values: `easyocr`, `tesseract_cli`, `tesseract`, `rapidocr`, `ocrmac`. Defaults to `easyocr`. +- `ocr_lang` (Optional[Union[List[str], str]]): List of languages used by the OCR engine. Note that each OCR engine has different values for the language names. Defaults to empty. +- `pdf_backend` (Optional[str]): PDF backend to use. Allowed values: `pypdfium2`, `dlparse_v1`, `dlparse_v2`. Defaults to `dlparse_v2`. +- `table_mode` (Optional[str]): Table mode to use. Allowed values: `fast`, `accurate`. Defaults to `fast`. +- `abort_on_error` (Optional[bool]): If enabled, abort on error. Defaults to false. +- `return_as_file` (Optional[bool]): If enabled, return the output as a file. Defaults to false. +- `do_table_structure` (Optional[bool]): If enabled, the table structure will be extracted. Defaults to true. +- `include_images` (Optional[bool]): If enabled, images will be extracted from the document. Defaults to true. +- `images_scale` (Optional[float]): Scale factor for images. Defaults to 2.0. + +### URL endpoint + +The endpoint is `/v1alpha/convert/url`, listening for POST requests of JSON payloads. + +On top of the above parameters, you must send the URL(s) of the document you want process with the `input_sources` field. You can provide URL(s) as a String (comma separated if multiple ones) or as a List of Strings. + +Payload example: + +```json +{ + "from_formats": ["docx", "pptx", "html", "image", "pdf", "asciidoc", "md", "xlsx"], + "to_formats": ["md", "json", "html", "text", "doctags"], + "image_export_mode": "placeholder", + "do_ocr": True, + "force_ocr": False, + "ocr_engine": "easyocr", + "ocr_lang": "en", + "pdf_backend": "dlparse_v2", + "table_mode": "fast", + "abort_on_error": False, + "return_as_file": False, + "input_sources": "https://arxiv.org/pdf/2206.01062" +} ``` -Example payload (http source): +CURL example: ```sh curl -X 'POST' \ - 'http://127.0.0.1:8000/convert' \ + 'http://localhost:8080/v1alpha/convert/url' \ -H 'accept: application/json' \ -H 'Content-Type: application/json' \ -d '{ - "http_source": { - "url": "https://arxiv.org/pdf/2206.01062" - } + "from_formats": [ + "docx", + "pptx", + "html", + "image", + "pdf", + "asciidoc", + "md", + "xlsx" + ], + "to_formats": ["md", "json", "html", "text", "doctags"], + "image_export_mode": "placeholder", + "do_ocr": true, + "force_ocr": false, + "ocr_engine": "easyocr", + "ocr_lang": [ + "fr", + "de", + "es", + "en" + ], + "pdf_backend": "dlparse_v2", + "table_mode": "fast", + "abort_on_error": false, + "return_as_file": false, + "do_table_structure": true, + "include_images": true, + "images_scale": 2, + "input_sources": "https://arxiv.org/pdf/2206.01062" }' ``` -### Cuda GPU Support +Python example: + +```python +import httpx + +async_client = httpx.AsyncClient(timeout=60.0) +url = "http://localhost:8080/v1alpha/convert/url" +payload = { + "from_formats": ["docx", "pptx", "html", "image", "pdf", "asciidoc", "md", "xlsx"], + "to_formats": ["md", "json", "html", "text", "doctags"], + "image_export_mode": "placeholder", + "do_ocr": True, + "force_ocr": False, + "ocr_engine": "easyocr", + "ocr_lang": "en", + "pdf_backend": "dlparse_v2", + "table_mode": "fast", + "abort_on_error": False, + "return_as_file": False, + "input_sources": "https://arxiv.org/pdf/2206.01062" +} + +response = await async_client_client.post(url, json=payload) + +data = response.json() +``` + +### File endpoint -For GPU support try the following: +The endpoint is: `/v1alpha/convert/file`, listening for POST requests of Form payloads (necessary as the files are sent as multipart/form data). You can send one or multiple files. + +CURL example: ```sh -# Create a virtual env -python3 -m venv venv +curl -X 'POST' \ + 'http://127.0.0.1:8080/v1alpha/convert/file' \ + -H 'accept: application/json' \ + -H 'Content-Type: multipart/form-data' \ + -F 'ocr_engine=easyocr' \ + -F 'pdf_backend=dlparse_v2' \ + -F 'from_formats=pdf,docx' \ + -F 'force_ocr=false' \ + -F 'image_export_mode=embedded' \ + -F 'ocr_lang=en' \ + -F 'table_mode=fast' \ + -F 'files=@2206.01062v1.pdf;type=application/pdf' \ + -F 'abort_on_error=false' \ + -F 'to_formats=md' \ + -F 'return_as_file=false' \ + -F 'do_ocr=true' +``` -# Activate the venv -source venv/bin/active +Python example: + +```python +import httpx + +async_client = httpx.AsyncClient(timeout=60.0) +url = "http://localhost:8080/v1alpha/convert/file" +parameters = { +"from_formats": ["docx", "pptx", "html", "image", "pdf", "asciidoc", "md", "xlsx"], +"to_formats": ["md", "json", "html", "text", "doctags"], +"image_export_mode": "placeholder", +"do_ocr": True, +"force_ocr": False, +"ocr_engine": "easyocr", +"ocr_lang": "en", +"pdf_backend": "dlparse_v2", +"table_mode": "fast", +"abort_on_error": False, +"return_as_file": False +} + +current_dir = os.path.dirname(__file__) +file_path = os.path.join(current_dir, '2206.01062v1.pdf') + +files = { + 'files': ('2206.01062v1.pdf', open(file_path, 'rb'), 'application/pdf'), +} + +response = await async_client.post(url, files=files, data={"parameters": json.dumps(parameters)}) +assert response.status_code == 200, "Response should be 200 OK" + +data = response.json() +``` -# Install torch with the special index -pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124 +### Response format -# Install the package -pip install -e . +The response can be a JSON Document or a File. -# Run the server -poetry run uvicorn docling_serve.app:app --reload -``` +- If you process only one file, the response will be a JSON document with the following format: + + ```json + { + "document": { + "md_content": "", + "json_content": "", + "html_content": "", + "text_content": "", + "doctags_content": "" + }, + "processing_time": "" + } + ``` + + Depending on the value you set in `output_formats`, the different items will be populated with their respective results or empty. + + `processing_time` is the Docling processing time in seconds. + +- If you set the parameter `return_as_file` to True, the response will be a zip file. +- If multiple files are generated (multiple inputs, or one input but multiple outputs with `return_as_file` True), the response will be a zip file. + +## Helpers + +- A full Swagger UI is available at the `/docs` endpoint. + +![swagger.png](img/swagger.png) + +- An easy to use UI is available at the `/ui` endpoint. + +![ui-input.png](img/ui-input.png) + +![ui-output.png](img/ui-output.png) diff --git a/docling_serve/.env.example b/docling_serve/.env.example new file mode 100644 index 0000000..a15cbcc --- /dev/null +++ b/docling_serve/.env.example @@ -0,0 +1,3 @@ +TESSDATA_PREFIX=/usr/share/tesseract/tessdata/ +UVICORN_WORKERS=2 +RELOAD=True \ No newline at end of file diff --git a/docling_serve/app.py b/docling_serve/app.py index 0a2b31d..1e48928 100644 --- a/docling_serve/app.py +++ b/docling_serve/app.py @@ -1,177 +1,80 @@ -import base64 -import hashlib +import logging +import os +import shutil +import tempfile from contextlib import asynccontextmanager -from enum import Enum -from io import BytesIO -from typing import Any, Dict, List, Optional, Tuple, Union - -import httpx -from docling.datamodel.base_models import ( - ConversionStatus, - DocumentStream, - ErrorItem, - InputFormat, +from pathlib import Path +from typing import List, Optional, Union + +import gradio as gr +from docling.datamodel.base_models import InputFormat +from docling.document_converter import DocumentConverter +from docling_conversion import ( + ConvertDocumentsParameters, + ConvertDocumentsRequest, + convert_documents, + converters, + get_pdf_pipeline_opts, ) -from docling.datamodel.document import ConversionResult -from docling.datamodel.pipeline_options import ( - EasyOcrOptions, - OcrOptions, - PdfPipelineOptions, - RapidOcrOptions, - TesseractOcrOptions, +from dotenv import load_dotenv +from fastapi import Depends, FastAPI, File, Form, UploadFile +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import RedirectResponse +from gradio_ui import ui as gradio_ui +from helper_functions import _str_to_bool, _to_list_of_strings +from pydantic import BaseModel +from response_preparation import process_results +from uvicorn import run + +# Load local env vars if present +load_dotenv() + + +# Set up custom logging as we'll be intermixes with FastAPI/Uvicorn's logging +class ColoredLogFormatter(logging.Formatter): + COLOR_CODES = { + logging.DEBUG: "\033[94m", # Blue + logging.INFO: "\033[92m", # Green + logging.WARNING: "\033[93m", # Yellow + logging.ERROR: "\033[91m", # Red + logging.CRITICAL: "\033[95m", # Magenta + } + RESET_CODE = "\033[0m" + + def format(self, record): + color = self.COLOR_CODES.get(record.levelno, "") + record.levelname = f"{color}{record.levelname}{self.RESET_CODE}" + return super().format(record) + + +logging.basicConfig( + level=logging.INFO, # Set the logging level + format="%(levelname)s:\t%(asctime)s - %(name)s - %(message)s", + datefmt="%H:%M:%S", ) -from docling.document_converter import DocumentConverter, PdfFormatOption -from docling.utils.profiling import ProfilingItem -from docling_core.types.doc import DoclingDocument, ImageRefMode -from docling_core.utils.file import resolve_remote_filename -from fastapi import FastAPI, HTTPException, Response -from pydantic import AnyHttpUrl, BaseModel +# Override the formatter with the custom ColoredLogFormatter +root_logger = logging.getLogger() # Get the root logger +for handler in root_logger.handlers: # Iterate through existing handlers + if handler.formatter: + handler.setFormatter(ColoredLogFormatter(handler.formatter._fmt)) -# TODO: import enum from Docling, once it is exposed -class OcrEngine(str, Enum): - EASYOCR = "easyocr" - TESSERACT = "tesseract" - RAPIDOCR = "rapidocr" - - -class ConvertOptions(BaseModel): - output_docling_document: bool = True - output_markdown: bool = False - output_html: bool = False - do_ocr: bool = True - ocr_engine: OcrEngine = OcrEngine.EASYOCR - ocr_lang: Optional[List[str]] = None - force_ocr: bool = False - do_table_structure: bool = True - include_images: bool = True - images_scale: float = 2.0 - - -class DocumentConvertBase(BaseModel): - options: ConvertOptions = ConvertOptions() - - -class HttpSource(BaseModel): - url: str - headers: Dict[str, Any] = {} - - -class FileSource(BaseModel): - base64_string: str - filename: str - - -class ConvertDocumentHttpSourceRequest(DocumentConvertBase): - http_source: HttpSource - - -class ConvertDocumentFileSourceRequest(DocumentConvertBase): - file_source: FileSource - - -class DocumentResponse(BaseModel): - markdown: Optional[str] = None - docling_document: Optional[DoclingDocument] = None - html: Optional[str] = None - - -class ConvertDocumentResponse(BaseModel): - document: DocumentResponse - status: ConversionStatus - errors: List[ErrorItem] = [] - timings: Dict[str, ProfilingItem] = {} - - -class ConvertDocumentErrorResponse(BaseModel): - status: ConversionStatus - # errors: List[ErrorItem] = [] - - -ConvertDocumentRequest = Union[ - ConvertDocumentFileSourceRequest, ConvertDocumentHttpSourceRequest -] - - -class MarkdownTextResponse(Response): - media_type = "text/markdown" - - -class HealthCheckResponse(BaseModel): - status: str = "ok" - - -def get_pdf_pipeline_opts(options: ConvertOptions) -> Tuple[PdfPipelineOptions, str]: - - if options.ocr_engine == OcrEngine.EASYOCR: - try: - import easyocr # noqa: F401 - except ImportError: - raise HTTPException( - status_code=400, - detail="The requested OCR engine" - f" (ocr_engine={options.ocr_engine.value})" - " is not available on this system. Please choose another OCR engine " - "or contact your system administrator.", - ) - ocr_options: OcrOptions = EasyOcrOptions(force_full_page_ocr=options.force_ocr) - elif options.ocr_engine == OcrEngine.TESSERACT: - try: - import tesserocr # noqa: F401 - except ImportError: - raise HTTPException( - status_code=400, - detail="The requested OCR engine" - f" (ocr_engine={options.ocr_engine.value})" - " is not available on this system. Please choose another OCR engine " - "or contact your system administrator.", - ) - ocr_options = TesseractOcrOptions(force_full_page_ocr=options.force_ocr) - elif options.ocr_engine == OcrEngine.RAPIDOCR: - try: - from rapidocr_onnxruntime import RapidOCR # noqa: F401 - except ImportError: - raise HTTPException( - status_code=400, - detail="The requested OCR engine" - f" (ocr_engine={options.ocr_engine.value})" - " is not available on this system. Please choose another OCR engine " - "or contact your system administrator.", - ) - ocr_options = RapidOcrOptions(force_full_page_ocr=options.force_ocr) - else: - raise RuntimeError(f"Unexpected OCR engine type {options.ocr_engine}") - - if options.ocr_lang is not None: - ocr_options.lang = options.ocr_lang - - pipeline_options = PdfPipelineOptions( - do_ocr=options.do_ocr, - ocr_options=ocr_options, - do_table_structure=options.do_table_structure, - generate_page_images=options.include_images, - generate_picture_images=options.include_images, - images_scale=options.images_scale, - ) - - options_hash = hashlib.sha1(pipeline_options.model_dump_json().encode()).hexdigest() - - return pipeline_options, options_hash - - -converters: Dict[str, DocumentConverter] = {} +_log = logging.getLogger(__name__) +# Context manager to initialize and clean up the lifespan of the FastAPI app @asynccontextmanager async def lifespan(app: FastAPI): # settings = Settings() # Converter with default options - pipeline_options, options_hash = get_pdf_pipeline_opts(ConvertOptions()) + pdf_format_option, options_hash = get_pdf_pipeline_opts( + ConvertDocumentsParameters() + ) converters[options_hash] = DocumentConverter( format_options={ - InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options), - InputFormat.IMAGE: PdfFormatOption(pipeline_options=pipeline_options), + InputFormat.PDF: pdf_format_option, + InputFormat.IMAGE: pdf_format_option, } ) @@ -180,100 +83,174 @@ async def lifespan(app: FastAPI): yield converters.clear() + gradio_ui.close() + +################################## +# App creation and configuration # +################################## app = FastAPI( title="Docling Serve", lifespan=lifespan, ) +origins = ["*"] +methods = ["*"] +headers = ["*"] -@app.get("/health") -def health() -> HealthCheckResponse: - return HealthCheckResponse() +app.add_middleware( + CORSMiddleware, + allow_origins=origins, + allow_credentials=True, + allow_methods=methods, + allow_headers=headers, +) +# Mount the Gradio app +tmp_output_dir = Path(tempfile.mkdtemp()) +gradio_ui.gradio_output_dir = tmp_output_dir +app = gr.mount_gradio_app( + app, gradio_ui, path="/ui", allowed_paths=["./logo.png", tmp_output_dir] +) -def _convert_document( - body: ConvertDocumentRequest, -) -> ConversionResult: - filename: str - buf: BytesIO +############################# +# API Endpoints definitions # +############################# - if isinstance(body, ConvertDocumentFileSourceRequest): - buf = BytesIO(base64.b64decode(body.file_source.base64_string)) - filename = body.file_source.filename - elif isinstance(body, ConvertDocumentHttpSourceRequest): - http_res = httpx.get(body.http_source.url, headers=body.http_source.headers) - buf = BytesIO(http_res.content) - filename = resolve_remote_filename( - http_url=AnyHttpUrl(body.http_source.url), - response_headers=dict(**http_res.headers), - ) - doc_input = DocumentStream(name=filename, stream=buf) +# Favicon +@app.get("/favicon.ico", include_in_schema=False) +async def favicon(): + response = RedirectResponse(url="https://ds4sd.github.io/docling/assets/logo.png") + return response - pipeline_options, options_hash = get_pdf_pipeline_opts(body.options) - if options_hash not in converters: - converters[options_hash] = DocumentConverter( - format_options={ - InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options), - InputFormat.IMAGE: PdfFormatOption(pipeline_options=pipeline_options), - } - ) - result: ConversionResult = converters[options_hash].convert(doc_input) +# Status +class HealthCheckResponse(BaseModel): + status: str = "ok" - if result is None or result.status == ConversionStatus.SKIPPED: - raise HTTPException(status_code=400, detail=result.errors) - if result is None or result.status not in { - ConversionStatus.SUCCESS, - }: - raise HTTPException( - status_code=500, detail={"errors": result.errors, "status": result.status} - ) +@app.get("/health") +def health() -> HealthCheckResponse: + return HealthCheckResponse() - return result +# API readiness compatibility for OpenShift AI Workbench +@app.get("/api", include_in_schema=False) +def api_check() -> HealthCheckResponse: + return HealthCheckResponse() -@app.post( - "/convert", -) -def convert_document( - body: ConvertDocumentRequest, -) -> ConvertDocumentResponse: - result = _convert_document(body=body) +# Convert a document from URL(s) +@app.post("/v1alpha/convert/url") +def process_url(conversion_request: ConvertDocumentsRequest): + # Note: results are only an iterator->lazy evaluation + results = convert_documents(conversion_request) + + # The real processing will happen here + response = process_results( + conversion_request=conversion_request, conv_results=results + ) - image_mode = ( - ImageRefMode.EMBEDDED - if body.options.include_images - else ImageRefMode.PLACEHOLDER + return response + + +# Convert a document from file(s) + + +# Parameters parser: Form object needed for the file(s) conversion endpoint +def _parse_parameters( + from_formats: Optional[Union[List[str], str]] = Form(["pdf", "docx"]), + to_formats: Optional[Union[List[str], str]] = Form(["md"]), + image_export_mode: Optional[str] = Form("embedded"), + do_ocr: Optional[Union[bool, str]] = Form("true"), + force_ocr: Optional[Union[bool, str]] = Form("false"), + ocr_engine: Optional[str] = Form("easyocr"), + ocr_lang: Optional[str] = Form("en"), + pdf_backend: Optional[str] = Form("dlparse_v2"), + table_mode: Optional[str] = Form("fast"), + abort_on_error: Optional[Union[bool, str]] = Form("false"), + return_as_file: Optional[Union[bool, str]] = Form("false"), + do_table_structure: Optional[Union[bool, str]] = Form("true"), + include_images: Optional[Union[bool, str]] = Form("true"), + images_scale: Optional[float] = Form(2.0), +) -> ConvertDocumentsParameters: + return ConvertDocumentsParameters( + from_formats=_to_list_of_strings(from_formats) if from_formats else None, + to_formats=_to_list_of_strings(to_formats) if to_formats else None, + image_export_mode=image_export_mode.strip() if image_export_mode else None, + ocr=_str_to_bool(do_ocr), + force_ocr=_str_to_bool(force_ocr), + ocr_engine=ocr_engine.strip() if ocr_engine else None, + ocr_lang=ocr_lang.strip() if ocr_lang else None, + pdf_backend=pdf_backend.strip() if pdf_backend else None, + table_mode=table_mode.strip() if table_mode else None, + abort_on_error=_str_to_bool(abort_on_error), + return_as_file=_str_to_bool(return_as_file), + do_table_structure=_str_to_bool(do_table_structure), + include_images=_str_to_bool(include_images), + images_scale=images_scale, ) - doc_resp = DocumentResponse() - if body.options.output_docling_document: - doc_resp.docling_document = result.document - if body.options.output_markdown: - doc_resp.markdown = result.document.export_to_markdown(image_mode=image_mode) - if body.options.output_html: - doc_resp.html = result.document.export_to_html(image_mode=image_mode) - - return ConvertDocumentResponse( - document=doc_resp, status=result.status, timings=result.timings + + +@app.post("/v1alpha/convert/file") +async def process_file( + files: List[UploadFile] = File(...), + parameters: ConvertDocumentsParameters = Depends(_parse_parameters), +): + + _log.info(f"Received {len(files)} files for processing.") + + # Create a temporary directory to store the file(s) + tmp_input_dir = Path(tempfile.mkdtemp()) + + # Save the uploaded files to the temporary directory + file_paths = [] + for file in files: + file_location = tmp_input_dir / file.filename # type: ignore [operator] + with open(file_location, "wb") as f: + shutil.copyfileobj(file.file, f) + file_paths.append(str(file_location)) + + # Process the files + conversion_request = ConvertDocumentsRequest( + input_sources=file_paths, + from_formats=parameters.from_formats, + to_formats=parameters.to_formats, + image_export_mode=parameters.image_export_mode, + ocr=parameters.do_ocr, + force_ocr=parameters.force_ocr, + ocr_engine=parameters.ocr_engine, + ocr_lang=parameters.ocr_lang, + pdf_backend=parameters.pdf_backend, + table_mode=parameters.table_mode, + abort_on_error=parameters.abort_on_error, + return_as_file=parameters.return_as_file, ) + results = convert_documents(conversion_request) -@app.post("/convert/markdown", response_class=MarkdownTextResponse) -def convert_document_md( - body: ConvertDocumentRequest, -) -> MarkdownTextResponse: - result = _convert_document(body=body) - image_mode = ( - ImageRefMode.EMBEDDED - if body.options.include_images - else ImageRefMode.PLACEHOLDER + response = process_results( + conversion_request=conversion_request, + conv_results=results, + tmp_input_dir=tmp_input_dir, ) - return MarkdownTextResponse( - result.document.export_to_markdown(image_mode=image_mode) + + return response + + +# Launch the FastAPI server +if __name__ == "__main__": + port = int(os.getenv("PORT", "8080")) + workers = int(os.getenv("UVICORN_WORKERS", "1")) + reload = _str_to_bool(os.getenv("RELOAD", "False")) + run( + "app:app", + host="0.0.0.0", + port=port, + workers=workers, + timeout_keep_alive=600, + reload=reload, ) diff --git a/docling_serve/docling_conversion.py b/docling_serve/docling_conversion.py new file mode 100644 index 0000000..35a3181 --- /dev/null +++ b/docling_serve/docling_conversion.py @@ -0,0 +1,335 @@ +import hashlib +import json +import logging +from typing import Dict, Iterator, List, Optional, Tuple, Type, Union + +from docling.backend.docling_parse_backend import DoclingParseDocumentBackend +from docling.backend.docling_parse_v2_backend import DoclingParseV2DocumentBackend +from docling.backend.pdf_backend import PdfDocumentBackend +from docling.backend.pypdfium2_backend import PyPdfiumDocumentBackend +from docling.datamodel.base_models import InputFormat, OutputFormat +from docling.datamodel.document import ConversionResult +from docling.datamodel.pipeline_options import ( + EasyOcrOptions, + OcrEngine, + OcrOptions, + PdfBackend, + PdfPipelineOptions, + RapidOcrOptions, + TableFormerMode, + TesseractOcrOptions, +) +from docling.document_converter import DocumentConverter, FormatOption, PdfFormatOption +from docling_core.types.doc import ImageRefMode +from fastapi import HTTPException +from helper_functions import _to_list_of_strings +from pydantic import BaseModel, Field + +_log = logging.getLogger(__name__) + + +# Define the input options for the API +class ConvertDocumentsParameters(BaseModel): + from_formats: Optional[Union[List[str], str]] = Field( + ["docx", "pptx", "html", "image", "pdf", "asciidoc", "md", "xlsx"], + description=( + "Input format(s) to convert from. String or list of strings. " + "Allowed values: docx, pptx, html, image, pdf, asciidoc, md, xlsx. " + "Optional, defaults to all formats." + ), + examples=[["docx", "pptx", "html", "image", "pdf", "asciidoc", "md", "xlsx"]], + ) + to_formats: Optional[Union[List[str], str]] = Field( + ["md"], + description=( + "Output format(s) to convert to. String or list of strings. " + "Allowed values: md, docling (json), html, text, doctags. " + "Optional, defaults to Markdown." + ), + examples=["md"], + ) + image_export_mode: Optional[str] = Field( + "embedded", + description=( + "Image export mode for the document (in case of JSON, Markdown or HTML). " + "Allowed values: embedded, placeholder, referenced. " + "Optional, defaults to Embedded." + ), + examples=["embedded"], + pattern="embedded|placeholder|referenced", + ) + do_ocr: Optional[bool] = Field( + True, + description=( + "If enabled, the bitmap content will be processed using OCR. " + "Boolean. Optional, defaults to true" + ), + examples=[True], + ) + force_ocr: Optional[bool] = Field( + False, + description=( + "If enabled, replace existing text with OCR-generated text over content. " + "Boolean. Optional, defaults to false." + ), + examples=[False], + ) + ocr_engine: Optional[str] = Field( + OcrEngine.EASYOCR, + description=( + "The OCR engine to use. String. " + "Allowed values: easyocr, tesseract, rapidocr. " + "Optional, defaults to easyocr." + ), + examples=["easyocr"], + pattern="easyocr|tesseract|rapidocr", + ) + ocr_lang: Optional[Union[List[str], str]] = Field( + None, + description=( + "List of languages used by the OCR engine. " + "Note that each OCR engine has " + "different values for the language names. String or list of strings. " + "Optional, defaults to empty." + ), + examples=[["fr", "de", "es", "en"]], + ) + pdf_backend: Optional[str] = Field( + PdfBackend.DLPARSE_V2, + description=( + "The PDF backend to use. String. " + "Allowed values: pypdfium2, dlparse_v1, dlparse_v2. " + "Optional, defaults to dlparse_v2." + ), + examples=["dlparse_v2"], + pattern="pypdfium2|dlparse_v1|dlparse_v2", + ) + table_mode: Optional[str] = Field( + TableFormerMode.FAST, + description=( + "Mode to use for table structure, String. " + "Allowed values: fast, accurate. Optional, defaults to fast." + ), + examples=["fast"], + pattern="fast|accurate", + ) + abort_on_error: Optional[bool] = Field( + False, + description=( + "Abort on error if enabled. " "Boolean. Optional, defaults to false." + ), + examples=[False], + ) + return_as_file: Optional[bool] = Field( + False, + description=( + "Return the output as a zip file " + "(will happen anyway if multiple files are generated). " + "Boolean. Optional, defaults to false." + ), + examples=[False], + ) + do_table_structure: Optional[bool] = Field( + True, + description=( + "If enabled, the table structure will be extracted. " + "Boolean. Optional, defaults to true." + ), + examples=[True], + ) + include_images: Optional[bool] = Field( + True, + description=( + "If enabled, images will be extracted from the document. " + "Boolean. Optional, defaults to true." + ), + examples=[True], + ) + images_scale: Optional[float] = Field( + 2.0, + description="Scale factor for images. Float. Optional, defaults to 2.0.", + examples=[2.0], + ) + + +class ConvertDocumentsRequest(ConvertDocumentsParameters): + input_sources: Union[List[str], str] = Field( + ..., + description="Source(s) to process.", + examples=["https://arxiv.org/pdf/2206.01062"], + ) + + +class DocumentResponse(BaseModel): + filename: Optional[str] = None + md_content: Optional[str] = None + json_content: Optional[dict] = None + html_content: Optional[str] = None + text_content: Optional[str] = None + doctags_content: Optional[str] = None + + +class ConvertDocumentResponse(BaseModel): + document: Optional[DocumentResponse] = None + processing_time: Optional[float] = None + + +# Document converters will be preloaded and stored in a dictionary +converters: Dict[str, DocumentConverter] = {} + + +# Custom serializer for PdfFormatOption +# (model_dump_json does not work with some classes) +def _serialize_pdf_format_option(pdf_format_option: PdfFormatOption) -> str: + data = pdf_format_option.model_dump() + + # pipeline_options are not fully serialized by model_dump, dedicated pass + if pdf_format_option.pipeline_options: + data["pipeline_options"] = pdf_format_option.pipeline_options.model_dump() + + # Replace `pipeline_cls` with a string representation + data["pipeline_cls"] = repr(data["pipeline_cls"]) + + # Replace `backend` with a string representation + data["backend"] = repr(data["backend"]) + + # Handle `device` in `accelerator_options` + if "accelerator_options" in data and "device" in data["accelerator_options"]: + data["accelerator_options"]["device"] = repr( + data["accelerator_options"]["device"] + ) + + # Serialize the dictionary to JSON with sorted keys to have consistent hashes + return json.dumps(data, sort_keys=True) + + +# Computes the PDF pipeline options and returns the PdfFormatOption and its hash +def get_pdf_pipeline_opts( + request: ConvertDocumentsParameters, +) -> Tuple[PdfFormatOption, str]: + + if request.ocr_engine == OcrEngine.EASYOCR: + try: + import easyocr # noqa: F401 + except ImportError: + raise HTTPException( + status_code=400, + detail="The requested OCR engine" + f" (ocr_engine={request.ocr_engine.value})" + " is not available on this system. Please choose another OCR engine " + "or contact your system administrator.", + ) + ocr_options: OcrOptions = EasyOcrOptions(force_full_page_ocr=request.force_ocr) + elif request.ocr_engine == OcrEngine.TESSERACT: + try: + import tesserocr # noqa: F401 + except ImportError: + raise HTTPException( + status_code=400, + detail="The requested OCR engine" + f" (ocr_engine={request.ocr_engine.value})" + " is not available on this system. Please choose another OCR engine " + "or contact your system administrator.", + ) + ocr_options = TesseractOcrOptions(force_full_page_ocr=request.force_ocr) + elif request.ocr_engine == OcrEngine.RAPIDOCR: + try: + from rapidocr_onnxruntime import RapidOCR # noqa: F401 + except ImportError: + raise HTTPException( + status_code=400, + detail="The requested OCR engine" + f" (ocr_engine={request.ocr_engine.value})" + " is not available on this system. Please choose another OCR engine " + "or contact your system administrator.", + ) + ocr_options = RapidOcrOptions(force_full_page_ocr=request.force_ocr) + else: + raise RuntimeError(f"Unexpected OCR engine type {request.ocr_engine}") + + if request.ocr_lang is not None: + if isinstance(request.ocr_lang, str): + ocr_options.lang = _to_list_of_strings(request.ocr_lang) + else: + ocr_options.lang = request.ocr_lang + + pipeline_options = PdfPipelineOptions( + do_ocr=request.do_ocr, + ocr_options=ocr_options, + do_table_structure=request.do_table_structure, + ) + pipeline_options.table_structure_options.do_cell_matching = True # do_cell_matching + pipeline_options.table_structure_options.mode = TableFormerMode(request.table_mode) + + if request.image_export_mode != ImageRefMode.PLACEHOLDER: + pipeline_options.generate_page_images = True + pipeline_options.generate_picture_images = ( + True # FIXME: to be deprecated in version 3 + ) + if request.images_scale: + pipeline_options.images_scale = request.images_scale + + if request.pdf_backend == PdfBackend.DLPARSE_V1: + backend: Type[PdfDocumentBackend] = DoclingParseDocumentBackend + elif request.pdf_backend == PdfBackend.DLPARSE_V2: + backend = DoclingParseV2DocumentBackend + elif request.pdf_backend == PdfBackend.PYPDFIUM2: + backend = PyPdfiumDocumentBackend + else: + raise RuntimeError(f"Unexpected PDF backend type {request.pdf_backend}") + + pdf_format_option = PdfFormatOption( + pipeline_options=pipeline_options, + backend=backend, # pdf_backend + ) + + serialized_data = _serialize_pdf_format_option(pdf_format_option) + + options_hash = hashlib.sha1(serialized_data.encode()).hexdigest() + + return pdf_format_option, options_hash + + +def convert_documents( + conversion_request: ConvertDocumentsRequest, +): + + # Initialize some values if missing + # (None, empty string, empty List, List of empty strings) + if not conversion_request.from_formats or all( + not item for item in conversion_request.from_formats + ): + conversion_request.from_formats = [e for e in InputFormat] + + if not conversion_request.to_formats or all( + not item for item in conversion_request.to_formats + ): + conversion_request.to_formats = OutputFormat.MARKDOWN + + # Sanitize some parameters as they can be a string or a list + conversion_request.input_sources = _to_list_of_strings( + conversion_request.input_sources + ) + conversion_request.from_formats = _to_list_of_strings( + conversion_request.from_formats + ) + conversion_request.to_formats = _to_list_of_strings(conversion_request.to_formats) + if conversion_request.ocr_lang is not None: + conversion_request.ocr_lang = _to_list_of_strings(conversion_request.ocr_lang) + + pdf_format_option, options_hash = get_pdf_pipeline_opts(conversion_request) + + if options_hash not in converters: + format_options: Dict[InputFormat, FormatOption] = { + InputFormat.PDF: pdf_format_option, + InputFormat.IMAGE: pdf_format_option, + } + + converters[options_hash] = DocumentConverter(format_options=format_options) + _log.info(f"We now have {len(converters)} converters in memory.") + + results: Iterator[ConversionResult] = converters[options_hash].convert_all( + conversion_request.input_sources + ) + + return results diff --git a/docling_serve/gradio_ui.py b/docling_serve/gradio_ui.py new file mode 100644 index 0000000..8eda84c --- /dev/null +++ b/docling_serve/gradio_ui.py @@ -0,0 +1,629 @@ +import json +import logging +import os +import tempfile +from pathlib import Path + +import gradio as gr +import requests + +logger = logging.getLogger(__name__) + +################# +# CSS and theme # +################# + +css = """ +#logo { + border-style: none; + background: none; + box-shadow: none; + min-width: 80px; +} +#dark_mode_column { + display: flex; + align-content: flex-end; +} +#title { + text-align: left; + display:block; + height: auto; + padding-top: 5px; + line-height: 0; +} +.title-text h1 > p, .title-text p { + margin-top: 0px !important; + margin-bottom: 2px !important; +} +#custom-container { + border: 0.909091px solid; + padding: 10px; + border-radius: 4px; +} +#custom-container h4 { + font-size: 14px; +} +#file_input_zone { + height: 140px; +} +""" + +theme = gr.themes.Default( + text_size="md", + spacing_size="md", + font=[ + gr.themes.GoogleFont("Red Hat Display"), + "ui-sans-serif", + "system-ui", + "sans-serif", + ], + font_mono=[ + gr.themes.GoogleFont("Red Hat Mono"), + "ui-monospace", + "Consolas", + "monospace", + ], +) + +############# +# Variables # +############# + +gradio_output_dir = None # Will be set by FastAPI when mounted +file_output_path = None # Will be set when a new file is generated + +############# +# Functions # +############# + + +def health_check(): + response = requests.get(f"http://localhost:{int(os.getenv("PORT", "8080"))}/health") + if response.status_code == 200: + return "Healthy" + return "Unhealthy" + + +def set_options_visibility(x): + return gr.Accordion("Options", open=x) + + +def set_outputs_visibility_direct(x, y): + content = gr.Row(visible=x) + file = gr.Row(visible=y) + return content, file + + +def set_outputs_visibility_process(x): + content = gr.Row(visible=not x) + file = gr.Row(visible=x) + return content, file + + +def set_download_button_label(label_text: gr.State): + return gr.DownloadButton(label=str(label_text), scale=1) + + +def clear_outputs(): + markdown_content = "" + json_content = "" + html_content = "" + text_content = "" + doctags_content = "" + + return ( + markdown_content, + markdown_content, + json_content, + html_content, + html_content, + text_content, + doctags_content, + ) + + +def clear_url_input(): + return "" + + +def clear_file_input(): + return None + + +def auto_set_return_as_file(url_input, file_input, image_export_mode): + # If more than one input source is provided, return as file + if ( + (len(url_input.split(",")) > 1) + or (file_input and len(file_input) > 1) + or (image_export_mode == "referenced") + ): + return True + else: + return False + + +def change_ocr_lang(ocr_engine): + if ocr_engine == "easyocr": + return "en,fr,de,es" + elif ocr_engine == "tesseract_cli": + return "eng,fra,deu,spa" + elif ocr_engine == "tesseract": + return "eng,fra,deu,spa" + elif ocr_engine == "rapidocr": + return "english,chinese" + + +def process_url( + input_sources, + to_formats, + image_export_mode, + ocr, + force_ocr, + ocr_engine, + ocr_lang, + pdf_backend, + table_mode, + abort_on_error, + return_as_file, +): + parameters = { + "input_sources": input_sources.split(","), + "to_formats": to_formats, + "image_export_mode": image_export_mode, + "ocr": ocr, + "force_ocr": force_ocr, + "ocr_engine": ocr_engine, + "ocr_lang": ocr_lang, + "pdf_backend": pdf_backend, + "table_mode": table_mode, + "abort_on_error": abort_on_error, + "return_as_file": return_as_file, + } + if ( + not parameters["input_sources"] + or len(parameters["input_sources"]) == 0 + or parameters["input_sources"][0] == "" + ): + logger.error("No input sources provided.") + raise gr.Error("No input sources provided.", print_exception=False) + try: + response = requests.post( + f"http://localhost:{int(os.getenv("PORT", "8080"))}/v1alpha/convert/url", + json=parameters, + ) + except Exception as e: + logger.error(f"Error processing URL: {e}") + raise gr.Error(f"Error processing URL: {e}", print_exception=False) + if response.status_code != 200: + data = response.json() + error_message = data.get("detail", "An unknown error occurred.") + logger.error(f"Error processing file: {error_message}") + raise gr.Error(f"Error processing file: {error_message}", print_exception=False) + output = response_to_output(response, return_as_file) + return output + + +def process_file( + files, + to_formats, + image_export_mode, + ocr, + force_ocr, + ocr_engine, + ocr_lang, + pdf_backend, + table_mode, + abort_on_error, + return_as_file, +): + if not files or len(files) == 0 or files[0] == "": + logger.error("No files provided.") + raise gr.Error("No files provided.", print_exception=False) + files_data = [("files", (file.name, open(file.name, "rb"))) for file in files] + # Because we send files as form data, we need to flatten + # the list of tuples and convert booleans to strings + parameters = { + "to_formats": ",".join(to_formats), + "image_export_mode": image_export_mode, + "ocr": str(ocr).lower(), + "force_ocr": str(force_ocr).lower(), + "ocr_engine": ocr_engine, + "ocr_lang": ocr_lang, + "pdf_backend": pdf_backend, + "table_mode": table_mode, + "abort_on_error": str(abort_on_error).lower(), + "return_as_file": str(return_as_file).lower(), + } + try: + response = requests.post( + f"http://localhost:{int(os.getenv("PORT", "8080"))}/v1alpha/convert/file", + files=files_data, + data=parameters, + ) + except Exception as e: + logger.error(f"Error processing file(s): {e}") + raise gr.Error(f"Error processing file(s): {e}", print_exception=False) + if response.status_code != 200: + data = response.json() + error_message = data.get("detail", "An unknown error occurred.") + logger.error(f"Error processing file: {error_message}") + raise gr.Error(f"Error processing file: {error_message}", print_exception=False) + output = response_to_output(response, return_as_file) + return output + + +def response_to_output(response, return_as_file): + markdown_content = "" + json_content = "" + html_content = "" + text_content = "" + doctags_content = "" + download_button = gr.DownloadButton(visible=False, label="Download Output", scale=1) + if return_as_file: + filename = ( + response.headers.get("Content-Disposition").split("filename=")[1].strip('"') + ) + tmp_output_dir = Path(tempfile.mkdtemp(dir=gradio_output_dir, prefix="ui_")) + file_output_path = f"{tmp_output_dir}/{filename}" + # logger.info(f"Saving file to: {file_output_path}") + with open(file_output_path, "wb") as f: + f.write(response.content) + download_button = gr.DownloadButton( + visible=True, label=f"Download {filename}", scale=1, value=file_output_path + ) + else: + full_content = response.json() + markdown_content = full_content.get("document").get("md_content") + json_content = json.dumps( + full_content.get("document").get("json_content"), indent=2 + ) + html_content = full_content.get("document").get("html_content") + text_content = full_content.get("document").get("text_content") + doctags_content = full_content.get("document").get("doctags_content") + return ( + markdown_content, + markdown_content, + json_content, + html_content, + html_content, + text_content, + doctags_content, + download_button, + ) + + +############ +# UI Setup # +############ + +with gr.Blocks( + css=css, + theme=theme, + title="Docling Serve", + delete_cache=(3600, 3600), # Delete all files older than 1 hour every hour +) as ui: + + # Constants stored in states to be able to pass them as inputs to functions + processing_text = gr.State("Processing your document(s), please wait...") + true_bool = gr.State(True) + false_bool = gr.State(False) + + # Banner + with gr.Row(elem_id="check_health"): + # Logo + with gr.Column(scale=1, min_width=90): + gr.Image( + "https://ds4sd.github.io/docling/assets/logo.png", + height=80, + width=80, + show_download_button=False, + show_label=False, + show_fullscreen_button=False, + container=False, + elem_id="logo", + scale=0, + ) + # Title + with gr.Column(scale=1, min_width=200): + gr.Markdown( + "# Docling Serve \n(docling version: 2.14.0)", + elem_id="title", + elem_classes=["title-text"], + ) + # Dark mode button + with gr.Column(scale=16, elem_id="dark_mode_column"): + dark_mode_btn = gr.Button("Dark/Light Mode", scale=0) + dark_mode_btn.click( + None, + None, + None, + js="""() => { + if (document.querySelectorAll('.dark').length) { + document.querySelectorAll('.dark').forEach( + el => el.classList.remove('dark') + ); + } else { + document.querySelector('body').classList.add('dark'); + } + }""", + show_api=False, + ) + + # URL Processing Tab + with gr.Tab("Convert URL(s)"): + with gr.Row(): + with gr.Column(scale=4): + url_input = gr.Textbox( + label="Input Sources (comma-separated URLs)", + placeholder="https://arxiv.org/pdf/2206.01062", + ) + with gr.Column(scale=1): + url_process_btn = gr.Button("Process URL(s)", scale=1) + url_reset_btn = gr.Button("Reset", scale=1) + + # File Processing Tab + with gr.Tab("Convert File(s)"): + with gr.Row(): + with gr.Column(scale=4): + file_input = gr.File( + elem_id="file_input_zone", + label="Upload Files", + file_types=[ + ".pdf", + ".docx", + ".pptx", + ".html", + ".xlsx", + ".asciidoc", + ".txt", + ".md", + ".jpg", + ".jpeg", + ".png", + ".gif", + ], + file_count="multiple", + scale=4, + ) + with gr.Column(scale=1): + file_process_btn = gr.Button("Process File(s)", scale=1) + file_reset_btn = gr.Button("Reset", scale=1) + + # Options + with gr.Accordion("Options") as options: + with gr.Row(): + with gr.Column(scale=1): + to_formats = gr.CheckboxGroup( + [ + ("Markdown", "md"), + ("Docling (JSON)", "json"), + ("HTML", "html"), + ("Plain Text", "text"), + ("Doc Tags", "doctags"), + ], + label="To Formats", + value=["md"], + ) + with gr.Column(scale=1): + image_export_mode = gr.Radio( + [ + ("Embedded", "embedded"), + ("Placeholder", "placeholder"), + ("Referenced", "referenced"), + ], + label="Image Export Mode", + value="embedded", + ) + with gr.Row(): + with gr.Column(scale=1, min_width=200): + ocr = gr.Checkbox(label="Enable OCR", value=True) + force_ocr = gr.Checkbox(label="Force OCR", value=False) + with gr.Column(scale=1): + ocr_engine = gr.Radio( + [ + ("EasyOCR", "easyocr"), + ("Tesseract", "tesseract"), + ("RapidOCR", "rapidocr"), + ], + label="OCR Engine", + value="easyocr", + ) + with gr.Column(scale=1, min_width=200): + ocr_lang = gr.Textbox( + label="OCR Language (beware of the format)", value="en,fr,de,es" + ) + ocr_engine.change(change_ocr_lang, inputs=[ocr_engine], outputs=[ocr_lang]) + with gr.Row(): + with gr.Column(scale=2): + pdf_backend = gr.Radio( + ["pypdfium2", "dlparse_v1", "dlparse_v2"], + label="PDF Backend", + value="dlparse_v2", + ) + with gr.Column(scale=2): + table_mode = gr.Radio( + ["fast", "accurate"], label="Table Mode", value="fast" + ) + with gr.Column(scale=1): + abort_on_error = gr.Checkbox(label="Abort on Error", value=False) + return_as_file = gr.Checkbox(label="Return as File", value=False) + + # Document output + with gr.Row(visible=False) as content_output: + with gr.Tab("Markdown"): + output_markdown = gr.Code( + language="markdown", wrap_lines=True, show_label=False + ) + with gr.Tab("Markdown-Rendered"): + output_markdown_rendered = gr.Markdown(label="Response") + with gr.Tab("Docling (JSON)"): + output_json = gr.Code(language="json", wrap_lines=True, show_label=False) + with gr.Tab("HTML"): + output_html = gr.Code(language="html", wrap_lines=True, show_label=False) + with gr.Tab("HTML-Rendered"): + output_html_rendered = gr.HTML(label="Response") + with gr.Tab("Text"): + output_text = gr.Code(wrap_lines=True, show_label=False) + with gr.Tab("DocTags"): + output_doctags = gr.Code(wrap_lines=True, show_label=False) + + # File download output + with gr.Row(visible=False) as file_output: + download_file_btn = gr.DownloadButton(label="Placeholder", scale=1) + + ############## + # UI Actions # + ############## + + # Handle Return as File + url_input.change( + auto_set_return_as_file, + inputs=[url_input, file_input, image_export_mode], + outputs=[return_as_file], + ) + file_input.change( + auto_set_return_as_file, + inputs=[url_input, file_input, image_export_mode], + outputs=[return_as_file], + ) + image_export_mode.change( + auto_set_return_as_file, + inputs=[url_input, file_input, image_export_mode], + outputs=[return_as_file], + ) + + # URL processing + url_process_btn.click( + set_options_visibility, inputs=[false_bool], outputs=[options] + ).then( + set_download_button_label, inputs=[processing_text], outputs=[download_file_btn] + ).then( + set_outputs_visibility_process, + inputs=[return_as_file], + outputs=[content_output, file_output], + ).then( + clear_outputs, + inputs=None, + outputs=[ + output_markdown, + output_markdown_rendered, + output_json, + output_html, + output_html_rendered, + output_text, + output_doctags, + ], + ).then( + process_url, + inputs=[ + url_input, + to_formats, + image_export_mode, + ocr, + force_ocr, + ocr_engine, + ocr_lang, + pdf_backend, + table_mode, + abort_on_error, + return_as_file, + ], + outputs=[ + output_markdown, + output_markdown_rendered, + output_json, + output_html, + output_html_rendered, + output_text, + output_doctags, + download_file_btn, + ], + ) + + url_reset_btn.click( + clear_outputs, + inputs=None, + outputs=[ + output_markdown, + output_markdown_rendered, + output_json, + output_html, + output_html_rendered, + output_text, + output_doctags, + ], + ).then(set_options_visibility, inputs=[true_bool], outputs=[options]).then( + set_outputs_visibility_direct, + inputs=[false_bool, false_bool], + outputs=[content_output, file_output], + ).then( + clear_url_input, inputs=None, outputs=[url_input] + ) + + # File processing + file_process_btn.click( + set_options_visibility, inputs=[false_bool], outputs=[options] + ).then( + set_download_button_label, inputs=[processing_text], outputs=[download_file_btn] + ).then( + set_outputs_visibility_process, + inputs=[return_as_file], + outputs=[content_output, file_output], + ).then( + clear_outputs, + inputs=None, + outputs=[ + output_markdown, + output_markdown_rendered, + output_json, + output_html, + output_html_rendered, + output_text, + output_doctags, + ], + ).then( + process_file, + inputs=[ + file_input, + to_formats, + image_export_mode, + ocr, + force_ocr, + ocr_engine, + ocr_lang, + pdf_backend, + table_mode, + abort_on_error, + return_as_file, + ], + outputs=[ + output_markdown, + output_markdown_rendered, + output_json, + output_html, + output_html_rendered, + output_text, + output_doctags, + download_file_btn, + ], + ) + + file_reset_btn.click( + clear_outputs, + inputs=None, + outputs=[ + output_markdown, + output_markdown_rendered, + output_json, + output_html, + output_html_rendered, + output_text, + output_doctags, + ], + ).then(set_options_visibility, inputs=[true_bool], outputs=[options]).then( + set_outputs_visibility_direct, + inputs=[false_bool, false_bool], + outputs=[content_output, file_output], + ).then( + clear_file_input, inputs=None, outputs=[file_input] + ) diff --git a/docling_serve/helper_functions.py b/docling_serve/helper_functions.py new file mode 100644 index 0000000..e7a3c7d --- /dev/null +++ b/docling_serve/helper_functions.py @@ -0,0 +1,30 @@ +import re +from typing import List, Union + + +def _to_list_of_strings(input_value: Union[str, List[str]]) -> List[str]: + def split_and_strip(value: str) -> List[str]: + if re.search(r"[;,]", value): + return [item.strip() for item in re.split(r"[;,]", value)] + else: + return [value.strip()] + + if isinstance(input_value, str): + return split_and_strip(input_value) + elif isinstance(input_value, list): + result = [] + for item in input_value: + result.extend(split_and_strip(str(item))) + return result + else: + raise ValueError("Invalid input: must be a string or a list of strings.") + + +# Helper functions to parse inputs coming as Form objects +def _str_to_bool(value: Union[str, bool]) -> bool: + if isinstance(value, bool): + return value # Already a boolean, return as-is + if isinstance(value, str): + value = value.strip().lower() # Normalize input + return value in ("true", "1", "yes") + return False # Default to False if none of the above matches diff --git a/docling_serve/response_preparation.py b/docling_serve/response_preparation.py new file mode 100644 index 0000000..9bd81bb --- /dev/null +++ b/docling_serve/response_preparation.py @@ -0,0 +1,237 @@ +import logging +import os +import shutil +import tempfile +import time +from pathlib import Path +from typing import Iterable, Optional + +from docling.datamodel.base_models import OutputFormat +from docling.datamodel.document import ConversionResult, ConversionStatus +from docling_conversion import ( + ConvertDocumentResponse, + ConvertDocumentsRequest, + DocumentResponse, +) +from docling_core.types.doc import ImageRefMode +from fastapi import BackgroundTasks, HTTPException +from fastapi.responses import FileResponse, JSONResponse + +_log = logging.getLogger(__name__) + + +def _export_document_as_content( + conv_result: ConversionResult, + export_json: bool, + export_html: bool, + export_md: bool, + export_txt: bool, + export_doctags: bool, + image_mode: ImageRefMode, +): + + document = DocumentResponse() + document.filename = conv_result.input.file.stem + + if conv_result.status == ConversionStatus.SUCCESS: + document.filename = conv_result.input.file.stem + new_doc = conv_result.document._make_copy_with_refmode(Path(), image_mode) + + # Create the different formats + if export_json: + document.json_content = new_doc.export_to_dict() + if export_html: + document.html_content = new_doc.export_to_html(image_mode=image_mode) + if export_txt: + document.text_content = new_doc.export_to_markdown( + strict_text=True, image_mode=image_mode + ) + if export_md: + document.md_content = new_doc.export_to_markdown(image_mode=image_mode) + if export_doctags: + document.doctags_content = new_doc.export_to_document_tokens() + + return document + + +def _export_documents_as_files( + conv_results: Iterable[ConversionResult], + output_dir: Path, + export_json: bool, + export_html: bool, + export_md: bool, + export_txt: bool, + export_doctags: bool, + image_export_mode: ImageRefMode, +): + + success_count = 0 + failure_count = 0 + + for conv_res in conv_results: + if conv_res.status == ConversionStatus.SUCCESS: + success_count += 1 + doc_filename = conv_res.input.file.stem + + # Export JSON format: + if export_json: + fname = output_dir / f"{doc_filename}.json" + _log.info(f"writing JSON output to {fname}") + conv_res.document.save_as_json( + filename=fname, image_mode=image_export_mode + ) + + # Export HTML format: + if export_html: + fname = output_dir / f"{doc_filename}.html" + _log.info(f"writing HTML output to {fname}") + conv_res.document.save_as_html( + filename=fname, image_mode=image_export_mode + ) + + # Export Text format: + if export_txt: + fname = output_dir / f"{doc_filename}.txt" + _log.info(f"writing TXT output to {fname}") + conv_res.document.save_as_markdown( + filename=fname, + strict_text=True, + image_mode=ImageRefMode.PLACEHOLDER, + ) + + # Export Markdown format: + if export_md: + fname = output_dir / f"{doc_filename}.md" + _log.info(f"writing Markdown output to {fname}") + conv_res.document.save_as_markdown( + filename=fname, image_mode=image_export_mode + ) + + # Export Document Tags format: + if export_doctags: + fname = output_dir / f"{doc_filename}.doctags" + _log.info(f"writing Doc Tags output to {fname}") + conv_res.document.save_as_document_tokens(filename=fname) + + else: + _log.warning(f"Document {conv_res.input.file} failed to convert.") + failure_count += 1 + + _log.info( + f"Processed {success_count + failure_count} docs, " + f"of which {failure_count} failed" + ) + + +def process_results( + conversion_request: ConvertDocumentsRequest, + conv_results: Iterable[ConversionResult], + tmp_input_dir: Optional[str] = None, +) -> JSONResponse | FileResponse: + + # Let's start by processing the documents + try: + start_time = time.monotonic() + + # Convert the iterator to a list to count the number of results and get timings + # As it's an iterator (lazy evaluation), it will also start the conversion + conv_results = list(conv_results) + + processing_time = time.monotonic() - start_time + + _log.info( + f"Processed {len(conv_results)} docs in {processing_time:.2f} seconds." + ) + + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + if len(conv_results) == 0: + raise HTTPException( + status_code=500, detail="No documents were generated by Docling." + ) + + # We have some results, let's prepare the response + + # Cleanup tasks holder + background_tasks = BackgroundTasks() + + # Cleanup the temporary input directory after sending the response if needed + if tmp_input_dir: + background_tasks.add_task(shutil.rmtree, tmp_input_dir, ignore_errors=True) + + # Booleans to know what to export + export_json = OutputFormat.JSON in conversion_request.to_formats + export_html = OutputFormat.HTML in conversion_request.to_formats + export_md = OutputFormat.MARKDOWN in conversion_request.to_formats + export_txt = OutputFormat.TEXT in conversion_request.to_formats + export_doctags = OutputFormat.DOCTAGS in conversion_request.to_formats + + # Only 1 document was processed, and we are not returning it as a file + if len(conv_results) == 1 and not conversion_request.return_as_file: + document = _export_document_as_content( + conv_results[0], + export_json=export_json, + export_html=export_html, + export_md=export_md, + export_txt=export_txt, + export_doctags=export_doctags, + image_mode=conversion_request.image_export_mode, + ) + + content = ConvertDocumentResponse( + document=document, processing_time=processing_time + ) + response = JSONResponse(content=content.model_dump()) + + # Multiple documents were processed, or we are forced returning as a file + else: + # Temporary directory to store the outputs + output_dir = Path(tempfile.mkdtemp(prefix="docling_")) + + # Worker pid to use in archive identification as we may have multiple workers + worker_pid = os.getpid() + + # Export the documents + _export_documents_as_files( + conv_results=conv_results, + output_dir=output_dir, + export_json=export_json, + export_html=export_html, + export_md=export_md, + export_txt=export_txt, + export_doctags=export_doctags, + image_export_mode=conversion_request.image_export_mode, + ) + + files = os.listdir(output_dir) + + if len(files) == 0: + raise HTTPException(status_code=500, detail="No documents were exported.") + + # Always return a zip file + else: + file_path = f"/tmp/{worker_pid}converted_docs.zip" + shutil.make_archive( + base_name=f"/tmp/{worker_pid}converted_docs", + format="zip", + root_dir=output_dir, + ) + + # Other cleanups after the response is sent + # Output directory + background_tasks.add_task(shutil.rmtree, output_dir, ignore_errors=True) + # Existing /tmp/{worker_pid}converted_docs.zip if it exists + if os.path.exists(f"/tmp/{worker_pid}converted_docs.zip"): + background_tasks.add_task(os.remove, f"/tmp/{worker_pid}converted_docs.zip") + + # Remove {worker_pid} from the filename + filename = os.path.basename(file_path).replace(f"{worker_pid}", "") + response = FileResponse( # type: ignore [assignment] + file_path, filename=filename, media_type="application/zip" + ) + + # Attach the cleanup background tasks + response.background = background_tasks + + return response diff --git a/img/swagger.png 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models +StandardPdfPipeline.download_models_hf(force=True) +load_pretrained_nlp_models(verbose=True) + +# Download EasyOCR models +urls = [ + "https://github.com/JaidedAI/EasyOCR/releases/download/v1.3/latin_g2.zip", + "https://github.com/JaidedAI/EasyOCR/releases/download/pre-v1.1.6/craft_mlt_25k.zip" +] + +local_zip_paths = [ + "/opt/app-root/src/latin_g2.zip", + "/opt/app-root/src/craft_mlt_25k.zip" +] + +extract_path = "/opt/app-root/src/.EasyOCR/model/" + +for url, local_zip_path in zip(urls, local_zip_paths): + # Download the file + response = requests.get(url) + with open(local_zip_path, "wb") as file: + file.write(response.content) + + # Unzip the file + with zipfile.ZipFile(local_zip_path, "r") as zip_ref: + zip_ref.extractall(extract_path) + + # Clean up the zip file + os.remove(local_zip_path) \ No newline at end of file diff --git a/os-packages.txt b/os-packages.txt new file mode 100644 index 0000000..ec3e929 --- /dev/null +++ b/os-packages.txt @@ -0,0 +1,8 @@ +tesseract 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"pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"] +dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"] docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"] tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"] tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"] @@ -136,13 +147,13 @@ uvloop = ["uvloop (>=0.15.2)"] [[package]] name = "certifi" -version = "2024.8.30" +version = "2024.12.14" description = "Python package for providing Mozilla's CA Bundle." optional = false python-versions = ">=3.6" files = [ - {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"}, - {file = "certifi-2024.8.30.tar.gz", hash = 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