docling-serve/docling_serve/app.py

import logging
import tempfile
from contextlib import asynccontextmanager
from io import BytesIO
from pathlib import Path
from typing import Annotated, Any, Dict, List, Optional, Union

from docling.datamodel.base_models import DocumentStream, InputFormat
from docling.document_converter import DocumentConverter
from fastapi import BackgroundTasks, FastAPI, UploadFile
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import RedirectResponse
from pydantic import BaseModel

from docling_serve.docling_conversion import (
    ConvertDocumentFileSourcesRequest,
    ConvertDocumentsOptions,
    ConvertDocumentsRequest,
    convert_documents,
    converters,
    get_pdf_pipeline_opts,
)
from docling_serve.helper_functions import FormDepends
from docling_serve.response_preparation import ConvertDocumentResponse, process_results
from docling_serve.settings import docling_serve_settings


# 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",
)

# 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))

_log = logging.getLogger(__name__)


# Context manager to initialize and clean up the lifespan of the FastAPI app
@asynccontextmanager
async def lifespan(app: FastAPI):

    # Converter with default options
    pdf_format_option, options_hash = get_pdf_pipeline_opts(ConvertDocumentsOptions())
    converters[options_hash] = DocumentConverter(
        format_options={
            InputFormat.PDF: pdf_format_option,
            InputFormat.IMAGE: pdf_format_option,
        }
    )

    converters[options_hash].initialize_pipeline(InputFormat.PDF)

    yield

    converters.clear()
    # if WITH_UI:
    #     gradio_ui.close()


##################################
# App creation and configuration #
##################################


def create_app():
    app = FastAPI(
        title="Docling Serve",
        lifespan=lifespan,
    )

    origins = ["*"]
    methods = ["*"]
    headers = ["*"]

    app.add_middleware(
        CORSMiddleware,
        allow_origins=origins,
        allow_credentials=True,
        allow_methods=methods,
        allow_headers=headers,
    )

    # Mount the Gradio app
    if docling_serve_settings.enable_ui:

        try:
            import gradio as gr

            from docling_serve.gradio_ui import ui as gradio_ui

            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],
                root_path="/ui",
            )
        except ImportError:
            _log.warning(
                "Docling Serve enable_ui is activated, but gradio is not installed. "
                "Install it with `pip install docling-serve[ui]` "
                "or `pip install gradio`"
            )

    #############################
    # API Endpoints definitions #
    #############################

    # 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

    # Status
    class HealthCheckResponse(BaseModel):
        status: str = "ok"

    @app.get("/health")
    def health() -> HealthCheckResponse:
        return HealthCheckResponse()

    # API readiness compatibility for OpenShift AI Workbench
    @app.get("/api", include_in_schema=False)
    def api_check() -> HealthCheckResponse:
        return HealthCheckResponse()

    # Convert a document from URL(s)
    @app.post(
        "/v1alpha/convert/source",
        response_model=ConvertDocumentResponse,
        responses={
            200: {
                "content": {"application/zip": {}},
                # "description": "Return the JSON item or an image.",
            }
        },
    )
    def process_url(
        background_tasks: BackgroundTasks, conversion_request: ConvertDocumentsRequest
    ):
        sources: List[Union[str, DocumentStream]] = []
        headers: Optional[Dict[str, Any]] = None
        if isinstance(conversion_request, ConvertDocumentFileSourcesRequest):
            for file_source in conversion_request.file_sources:
                sources.append(file_source.to_document_stream())
        else:
            for http_source in conversion_request.http_sources:
                sources.append(http_source.url)
                if headers is None and http_source.headers:
                    headers = http_source.headers

        # Note: results are only an iterator->lazy evaluation
        results = convert_documents(
            sources=sources, options=conversion_request.options, headers=headers
        )

        # The real processing will happen here
        response = process_results(
            background_tasks=background_tasks,
            conversion_options=conversion_request.options,
            conv_results=results,
        )

        return response

    # Convert a document from file(s)
    @app.post(
        "/v1alpha/convert/file",
        response_model=ConvertDocumentResponse,
        responses={
            200: {
                "content": {"application/zip": {}},
            }
        },
    )
    async def process_file(
        background_tasks: BackgroundTasks,
        files: List[UploadFile],
        options: Annotated[
            ConvertDocumentsOptions, FormDepends(ConvertDocumentsOptions)
        ],
    ):

        _log.info(f"Received {len(files)} files for processing.")

        # Load the uploaded files to Docling DocumentStream
        file_sources = []
        for file in files:
            buf = BytesIO(file.file.read())
            name = file.filename if file.filename else "file.pdf"
            file_sources.append(DocumentStream(name=name, stream=buf))

        results = convert_documents(sources=file_sources, options=options)

        response = process_results(
            background_tasks=background_tasks,
            conversion_options=options,
            conv_results=results,
        )

        return response

    return app