diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 2cad5fc..b459de2 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -80,8 +80,6 @@ RUN apt update && apt install -y \ default-jre \ # Pandoc pandoc \ - # Libmagic - libmagic1 \ sqlite3 \ && apt-get autoremove -y \ && apt-get clean \ @@ -110,3 +108,4 @@ WORKDIR /workspace COPY .devcontainer/entrypoint.sh /home/$DEVCONTAINER_USER/ ENV UV_CACHE_DIR=/resources/cache/uv +ENV ALAMBIC_CONFIG=/workspace/aymurai/alambic.yml diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 2ead995..74634a0 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -11,27 +11,6 @@ "workspaceFolder": "/workspace", "customizations": { "vscode": { - "settings": { - "extensions.verifySignature": false, - "files.trimFinalNewlines": true, - "files.trimTrailingWhitespace": true, - "files.watcherExclude": { - "**/.git/objects/**": true, - "**/.git/subtree-cache/**": true - }, - "editor.formatOnSave": true, - "notebook.formatOnSave.enabled": true, - "notebook.output.scrolling": true, - "notebook.codeActionsOnSave": { - "notebook.source.organizeImports": "explicit" - }, - "[python]": { - "editor.defaultFormatter": "charliermarsh.ruff", - "editor.codeActionsOnSave": { - "source.fixAll": "never" - } - } - }, // Add the IDs of extensions you want installed when the container is created. "extensions": [ "ms-python.python", @@ -40,14 +19,13 @@ "charliermarsh.ruff", "ms-toolsai.jupyter", "GitHub.vscode-pull-request-github", - "njpwerner.autodocstring", "GrapeCity.gc-excelviewer", "tamasfe.even-better-toml", "redhat.vscode-xml", - "cweijan.vscode-database-client2", "christian-kohler.path-intellisense", "github.vscode-github-actions", - "seatonjiang.gitmoji-vscode" + "seatonjiang.gitmoji-vscode", + "dbcode.dbcode" ] } }, diff --git a/.devcontainer/docker-compose.yml b/.devcontainer/docker-compose.yml index 5e12b20..1feb29c 100644 --- a/.devcontainer/docker-compose.yml +++ b/.devcontainer/docker-compose.yml @@ -10,7 +10,6 @@ x-template: &template - /var/run/docker.sock:/var/run/docker.sock env_file: - ../.env.common - - ../.env command: /bin/sh -c "while true; do :; done" services: diff --git a/.github/workflows/build-docker-image.yml b/.github/workflows/build-docker-image.yml index 3d9af7e..dfd7c3a 100644 --- a/.github/workflows/build-docker-image.yml +++ b/.github/workflows/build-docker-image.yml @@ -15,6 +15,17 @@ jobs: - name: Checkout code uses: actions/checkout@v3 + - name: Extract tag name + id: envs + run: | + TAG_NAME="${GITHUB_REF#refs/tags/}" + echo "TAG=${TAG_NAME}" >> $GITHUB_OUTPUT + if [[ "$TAG_NAME" == *"rc"* ]]; then + echo "SUFFIX=-rc" >> $GITHUB_OUTPUT + else + echo "SUFFIX=" >> $GITHUB_OUTPUT + fi + # Step 2: Log in to GitHub Container Registry - name: Log in to GitHub Container Registry uses: docker/login-action@v2 @@ -23,22 +34,28 @@ jobs: username: ${{ github.actor }} password: ${{ secrets.GITHUB_TOKEN }} - # Step 3: Build the Docker image - - name: Build Docker Image - run: | - touch .env - docker compose build aymurai-api - docker compose build aymurai-api-full - - # Step 4: Push the Docker image to GHCR - - name: Push Docker Image - run: | - docker tag ghcr.io/aymurai/api:latest ghcr.io/aymurai/api:${{ github.ref_name }} - docker tag ghcr.io/aymurai/api:full ghcr.io/aymurai/api:full-${{ github.ref_name }} + - name: Build and push Docker image (aymurai-api) + uses: docker/build-push-action@v5 + with: + context: . + file: docker/api/Dockerfile + push: true + target: aymurai-api + build-args: | + SETUPTOOLS_SCM_PRETEND_VERSION=${{ steps.envs.outputs.TAG }} + tags: | + ghcr.io/aymurai/api:latest${{ steps.envs.outputs.SUFFIX }} + ghcr.io/aymurai/api:${{ github.ref_name }} - docker push ghcr.io/aymurai/api:full-${{ github.ref_name }} - docker push ghcr.io/aymurai/api:${{ github.ref_name }} - if [[ "${{ github.ref_name }}" =~ ^v[0-9]+\.[0-9]+\.[0-9]+$ ]]; then - docker push ghcr.io/aymurai/api:latest - docker push ghcr.io/aymurai/api:full - fi + - name: Build and push Docker image (aymurai-api-full) + uses: docker/build-push-action@v5 + with: + context: . + file: docker/api/Dockerfile + push: true + target: aymurai-api-full + build-args: | + SETUPTOOLS_SCM_PRETEND_VERSION=${{ steps.envs.outputs.TAG }} + tags: | + ghcr.io/aymurai/api:full${{ steps.envs.outputs.SUFFIX }} + ghcr.io/aymurai/api:${{ github.ref_name }}-full diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..0825654 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,45 @@ +{ + "python.analysis.typeCheckingMode": "off", + "extensions.verifySignature": false, + "files.trimFinalNewlines": true, + "files.trimTrailingWhitespace": true, + "files.watcherExclude": { + "**/.git/objects/**": true, + "**/.git/subtree-cache/**": true + }, + "python.analysis.exclude": [ + "**/node_modules", + "**/__pycache__", + "**/build", + ".git", + ".venv/**" + ], + "editor.formatOnSave": true, + "notebook.formatOnSave.enabled": true, + "notebook.output.scrolling": true, + "notebook.codeActionsOnSave": { + "notebook.source.organizeImports": "explicit" + }, + "[python]": { + "editor.defaultFormatter": "charliermarsh.ruff", + "editor.codeActionsOnSave": { + "source.fixAll": "never", + "source.organizeImports.ruff": "always" + } + }, + "ruff.format.preview": true, + "ruff.organizeImports": true, + "dbcode.connections": [ + { + "connectionId": "1oe_iPSN7LlyCERrXeOdw", + "name": "aymurai", + "driver": "sqlite", + "socket": "workspace/resources/cache/sqlite/database.db", + "readOnly": false, + "driverOptions": { + "extensions": [] + }, + "connectionType": "socket" + } + ] +} \ No newline at end of file diff --git a/Makefile b/Makefile index 0f13234..5820486 100644 --- a/Makefile +++ b/Makefile @@ -6,6 +6,7 @@ export $(shell sed 's/=.*//' .env.common) api-build: + uv build docker compose build aymurai-api-dev api-run: docker compose run --service-ports aymurai-api-dev diff --git a/aymurai/api/core.py b/aymurai/api/core.py index c922345..80bf41a 100644 --- a/aymurai/api/core.py +++ b/aymurai/api/core.py @@ -1,45 +1,28 @@ from fastapi.routing import APIRouter -from .endpoints.routers.anonymizer import anonymizer -from .endpoints.routers.anonymizer import database as anonymizer_database -from .endpoints.routers.datapublic import datapublic - -from .endpoints.routers.misc import convert, document_extract +from .endpoints.routers import anonymizer, database, datapublic, document, paragraph from .endpoints.routers.server import stats router = APIRouter() # Server -router.include_router( - stats.router, - prefix="/server/stats", - tags=["server"], -) +router.include_router(stats.router, prefix="/server/stats", tags=["server"]) -# Anonymizer -router.include_router( - anonymizer.router, - prefix="/anonymizer", - tags=["anonymization/model"], -) -# router.include_router( -# anonymizer_database.router, -# prefix="/anonymizer/database", -# tags=["anonymization/database"], -# ) +# Document +router.include_router(document.extract.router, prefix="/document", tags=["document"]) +router.include_router(document.convert.router, prefix="/document", tags=["document"]) +router.include_router(document.predict.router, tags=["document"]) -# Datapublic -router.include_router( - datapublic.router, - prefix="/datapublic", - tags=["datapublic/model"], -) +# Paragraph +router.include_router(paragraph.predict.router, tags=["paragraph"]) +# Datapublic +router.include_router(datapublic.validation.router, tags=["datapublic"]) +router.include_router(datapublic.dataset.router, tags=["datapublic"]) -# Misc -router.include_router(document_extract.router, tags=["document"], deprecated=True) -router.include_router(document_extract.router, prefix="/misc", tags=["document"]) +# Anonymizer +router.include_router(anonymizer.compilation.router, tags=["anonymizer"]) -# Document conversion -router.include_router(convert.router, tags=["Document conversion"]) +# Database +router.include_router(database.dump.router, tags=["database"]) diff --git a/aymurai/api/endpoints/routers/anonymizer/__init__.py b/aymurai/api/endpoints/routers/anonymizer/__init__.py index e69de29..8e3c091 100644 --- a/aymurai/api/endpoints/routers/anonymizer/__init__.py +++ b/aymurai/api/endpoints/routers/anonymizer/__init__.py @@ -0,0 +1,2 @@ +# ruff: noqa: F401 +from . import compilation diff --git a/aymurai/api/endpoints/routers/anonymizer/anonymizer.py b/aymurai/api/endpoints/routers/anonymizer/anonymizer.py deleted file mode 100644 index 7208fca..0000000 --- a/aymurai/api/endpoints/routers/anonymizer/anonymizer.py +++ /dev/null @@ -1,265 +0,0 @@ -import json -import os -import subprocess -import tempfile -from threading import Lock - -import torch -from fastapi import Body, Depends, Form, Query, UploadFile -from fastapi.responses import FileResponse -from fastapi.routing import APIRouter -from sqlmodel import Session -from starlette.background import BackgroundTask - -from aymurai.api.utils import load_pipeline -from aymurai.database.crud.anonymization.document import anonymization_document_create -from aymurai.database.crud.anonymization.paragraph import ( - anonymization_paragraph_batch_create_update, - anonymization_paragraph_create, - anonymization_paragraph_read, -) -from aymurai.database.schema import AnonymizationParagraph -from aymurai.database.session import get_session -from aymurai.database.utils import data_to_uuid, text_to_uuid -from aymurai.logger import get_logger -from aymurai.meta.api_interfaces import ( - DocLabel, - DocumentAnnotations, - DocumentInformation, - TextRequest, -) -from aymurai.settings import settings -from aymurai.text.anonymization import DocAnonymizer -from aymurai.text.extraction import MIMETYPE_EXTENSION_MAPPER -from aymurai.utils.misc import get_element - -logger = get_logger(__name__) - - -RESOURCES_BASEPATH = settings.RESOURCES_BASEPATH -torch.set_num_threads = 100 # FIXME: polemic ? -pipeline_lock = Lock() - - -router = APIRouter() - - -# MARK: Predict -@router.post("/predict", response_model=DocumentInformation) -async def anonymizer_paragraph_predict( - text_request: TextRequest = Body( - {"text": "Acusado: Ramiro Marrón DNI 34.555.666."} - ), - use_cache: bool = Query( - True, description="Use cache to store or retrive predictions" - ), - session: Session = Depends(get_session), -) -> DocumentInformation: - """ - Endpoint to predict anonymization for a given paragraph of text. - - Args: - text_request (TextRequest): The request body containing the text to be anonymized. - use_cache (bool): Flag to determine whether to use cache for storing or retrieving predictions. - session (Session): Database session dependency. - - Returns: - DocumentInformation: The anonymized document information including the text and labels. - """ - - logger.info("anonymization predict single") - - logger.info(f"Checking cache (use cache: {use_cache})") - text = text_request.text - paragraph_id = text_to_uuid(text) - - cached_prediction = session.get(AnonymizationParagraph, paragraph_id) - if cached_prediction and use_cache: - logger.info(f"cache loaded from key: {paragraph_id}") - logger.debug(f"{cached_prediction}") - - labels = cached_prediction.prediction - return DocumentInformation(document=cached_prediction.text, labels=labels or []) - - logger.info("Running prediction") - item = [{"path": "empty", "data": {"doc.text": text_request.text}}] - pipeline = load_pipeline( - os.path.join(RESOURCES_BASEPATH, "pipelines", "production", "flair-anonymizer") - ) - - with pipeline_lock: - processed = pipeline.preprocess(item) - processed = pipeline.predict_single(processed[0]) - processed = pipeline.postprocess([processed]) - - text = get_element(processed[0], ["data", "doc.text"]) or "" - labels = get_element(processed[0], ["predictions", "entities"]) or [] - - if use_cache: - logger.info(f"saving in cache: {paragraph_id}") - paragraph = AnonymizationParagraph( - id=paragraph_id, - text=text, - prediction=labels, - ) - paragraph = anonymization_paragraph_create(paragraph, session=session) - - return DocumentInformation(document=text, labels=paragraph.prediction) - - -# MARK: Validate -@router.post("/validation", response_model=list[DocLabel] | None) -async def anonymizer_get_paragraph_validation( - text_request: TextRequest = Body( - {"text": "Acusado: Ramiro Marrón DNI 34.555.666."} - ), - session: Session = Depends(get_session), -) -> list[DocLabel] | None: - """ - Get the validation labels for a given paragraph text. - - Args: - text_request (TextRequest): The request body containing the text to be validated. - session (Session): Database session dependency. - - Returns: - list[DocLabel] | None: A list of validation labels for the given paragraph text, or None if no validation exists. - """ - - text = text_request.text - paragraph_id = text_to_uuid(text) - - paragraph = anonymization_paragraph_read(paragraph_id, session=session) - if not paragraph: - return None - - return paragraph.validation - - -# MARK: Document Compilation -@router.post("/anonymize-document") -async def anonymizer_compile_document( - file: UploadFile, - annotations: str = Form(...), - session: Session = Depends(get_session), -) -> FileResponse: - """ - Compile Anonimized document from original file and annotations - - Args: - file (UploadFile): Original file. - annotations (str, optional): JSON with document annotations. - - Returns: - FileResponse: Anonymized document - """ - logger.info(f"receiving => {file.filename}") - extension = MIMETYPE_EXTENSION_MAPPER.get(file.content_type) - logger.info(f"detection extension: {extension} ({file.content_type})") - - # Create a temporary file - _, suffix = os.path.splitext(file.filename) - suffix = suffix if suffix == ".docx" else ".txt" - tmp_dir = tempfile.gettempdir() - - # Use delete=False to avoid the file being deleted when the NamedTemporaryFile object is closed - # This is necessary on Windows, as the file is locked by the file object and cannot be deleted - with tempfile.NamedTemporaryFile( - suffix=suffix, delete=False, dir=tmp_dir - ) as tmp_file: - tmp_filename = tmp_file.name - logger.info(f"saving temp file on local storage => {tmp_filename}") - data = file.file.read() - tmp_file.write(data) - tmp_file.flush() - tmp_file.close() - - logger.info(f"saved temp file on local storage => {tmp_filename}") - - annots_json = json.loads(annotations) - annots = DocumentAnnotations.model_validate(annots_json) - logger.info(f"processing annotations => {annots}") - - # Add paragraphs to the database - # validation MUST be at least an empty list, to remember user feedback - paragraphs = [ - AnonymizationParagraph( - id=text_to_uuid(paragraph.document), - text=paragraph.document, - validation=paragraph.labels or [], - ) - for paragraph in annots.data - ] - paragraphs = anonymization_paragraph_batch_create_update( - paragraphs, session=session - ) - - anonymization_document_create( - id=data_to_uuid(data), - name=file.filename, - paragraphs=paragraphs, - session=session, - override=False, - ) - - # Anonymize the document - doc_anonymizer = DocAnonymizer() - - if suffix == ".docx": - item = {"path": tmp_filename} - doc_anonymizer( - item, - [document_information.model_dump() for document_information in annots.data], - tmp_dir, - ) - logger.info(f"saved temp file on local storage => {tmp_filename}") - - else: - # Export as raw document - anonymized_doc = [ - doc_anonymizer.replace_labels_in_text(document_information.model_dump()) - .replace("<", "<") - .replace(">", ">") - for document_information in annots.data - ] - with open(tmp_filename, "w") as f: - f.write("\n".join(anonymized_doc)) - - # Convert to ODT - cmd = [ - settings.LIBREOFFICE_BIN, - "--headless", - "--convert-to", - "odt", - "--outdir", - tmp_dir, - tmp_filename, - ] - - logger.info(f"Executing: {' '.join(cmd)}") - - try: - output = subprocess.check_output( - cmd, shell=False, encoding="utf-8", errors="ignore" - ) - logger.info(f"LibreOffice output: {output}") - except subprocess.CalledProcessError as e: - raise RuntimeError( - f"LibreOffice conversion failed: {e.output.decode('utf-8', errors='ignore')}" - ) - - odt = tmp_filename.replace(suffix, ".odt") - logger.info(f"Expected output file path: {odt}") - - if not os.path.exists(odt): - raise RuntimeError(f"File at path {odt} does not exist.") - - # Ensure the temporary file is deleted - os.remove(tmp_filename) - - return FileResponse( - odt, - background=BackgroundTask(os.remove, odt), - media_type="application/octet-stream", - filename=f"{os.path.splitext(file.filename)[0]}.odt", - ) diff --git a/aymurai/api/endpoints/routers/anonymizer/compilation.py b/aymurai/api/endpoints/routers/anonymizer/compilation.py new file mode 100644 index 0000000..f99623b --- /dev/null +++ b/aymurai/api/endpoints/routers/anonymizer/compilation.py @@ -0,0 +1,140 @@ +import os +import subprocess +import tempfile + +from fastapi import Depends, HTTPException +from fastapi.responses import FileResponse +from fastapi.routing import APIRouter +from pydantic import UUID4, UUID5 +from sqlmodel import Session +from starlette.background import BackgroundTask + +from aymurai.database.crud.prediction import read_document_prediction_paragraphs +from aymurai.database.schema import Document, ModelType +from aymurai.database.session import get_session +from aymurai.logger import get_logger +from aymurai.settings import settings +from aymurai.text.anonymization.alignment import replace_labels_in_text +from aymurai.text.anonymization.core import anonymize_docx + +logger = get_logger(__name__) + + +router = APIRouter() + + +# MARK: Document Compilation +@router.get( + f"/pipeline/{ModelType.ANONYMIZATION}/document/{{document_id}}/compile", + response_class=FileResponse, +) +async def anonymizer_compile_document( + document_id: UUID4 | UUID5, + session: Session = Depends(get_session), +) -> FileResponse: + """ + Compile Anonymized document from original file and annotations + + Args: + document_id (UUID4): The ID of the document to be compiled. + session (Session): Database session dependency. + + Returns: + FileResponse: Anonymized document + """ + document = session.get(Document, document_id) + + # ———————— Sanity check ——————————————————————————————————————————————————————————— + # Check if the document exists + if not document: + raise HTTPException( + status_code=404, + detail=f"Document not found: {document_id}", + ) + + # ———————— Get annotations ———————————————————————————————————————————————————————— + annotations = read_document_prediction_paragraphs( + session=session, + document_id=document_id, + model_type=ModelType.ANONYMIZATION, + ) + + # ======== Document Anonymization ================================================= + # ———————— Create temporary file —————————————————————————————————————————————————— + filename = document.name + logger.info(f"Document found: {document_id} ({filename})") + + # Create a temporary file + _, suffix = os.path.splitext(filename) + suffix = suffix.lower() + suffix = suffix if suffix == ".docx" else ".txt" + tmp_dir = tempfile.gettempdir() + + # Use delete=False to avoid the file being deleted when the NamedTemporaryFile object is closed + # This is necessary on Windows, as the file is locked by the file object and cannot be deleted + with tempfile.NamedTemporaryFile(suffix=suffix, delete=False, dir=tmp_dir) as file: + tmp_filename = file.name + logger.info(f"saving temp file on local storage => {tmp_filename}") + file.write(document.data) + file.flush() + file.close() + + logger.info(f"saved temp file on local storage => {tmp_filename}") + + # ———————— Anonymize document ————————————————————————————————————————————————————— + + if suffix == ".docx": + anonymize_docx( + path=tmp_filename, paragraph_preds=annotations, output_dir=tmp_dir + ) + logger.info(f"saved temp file on local storage => {tmp_filename}") + + else: + # Export as raw document + anonymized_doc = [ + replace_labels_in_text(text=paragraph.text, prediction=paragraph.prediction) + .replace("<", "<") + .replace(">", ">") + for paragraph in annotations + ] + with open(tmp_filename, "w") as f: + f.write("\n".join(anonymized_doc)) + + # ——————— Convert to ODT —————————————————————————————————————————————————————————— + cmd = [ + settings.LIBREOFFICE_BIN, + "--headless", + "--convert-to", + "odt", + "--outdir", + tmp_dir, + tmp_filename, + ] + + logger.info(f"Executing: {' '.join(cmd)}") + + try: + output = subprocess.check_output( + cmd, shell=False, encoding="utf-8", errors="ignore" + ) + logger.info(f"LibreOffice output: {output}") + except subprocess.CalledProcessError as e: + raise RuntimeError( + f"LibreOffice conversion failed: {e.output.decode('utf-8', errors='ignore')}" + ) + + odt = tmp_filename.replace(suffix, ".odt") + logger.info(f"Expected output file path: {odt}") + + if not os.path.exists(odt): + raise RuntimeError(f"File at path {odt} does not exist.") + + # Ensure the temporary file is deleted + os.remove(tmp_filename) + + return FileResponse( + odt, + background=BackgroundTask(os.remove, odt), + media_type="application/octet-stream", + filename=f"{os.path.splitext(filename)[0]}.odt", + ) diff --git a/aymurai/api/endpoints/routers/anonymizer/database.py b/aymurai/api/endpoints/routers/anonymizer/database.py deleted file mode 100644 index 50bc19c..0000000 --- a/aymurai/api/endpoints/routers/anonymizer/database.py +++ /dev/null @@ -1,34 +0,0 @@ -from pydantic import UUID5 -from fastapi import Depends -from sqlmodel import Session -from fastapi.routing import APIRouter - -from aymurai.logger import get_logger -from aymurai.database.session import get_session - -logger = get_logger(__name__) - - -router = APIRouter() - - -@router.get("/document/list") -async def anonymizer_database_document_list( - session: Session = Depends(get_session), -): - pass - - -@router.get("/document/dump") -async def anonymizer_database_documents_dump( - session: Session = Depends(get_session), -): - pass - - -@router.get("/document/{document_id}") -async def anonymizer_database_document( - document_id: UUID5, - session: Session = Depends(get_session), -): - pass diff --git a/aymurai/api/endpoints/routers/database/__init__.py b/aymurai/api/endpoints/routers/database/__init__.py index e69de29..70e646a 100644 --- a/aymurai/api/endpoints/routers/database/__init__.py +++ b/aymurai/api/endpoints/routers/database/__init__.py @@ -0,0 +1,2 @@ +# ruff: noqa: F401 +from . import dump diff --git a/aymurai/api/endpoints/routers/database/anonymization.py b/aymurai/api/endpoints/routers/database/anonymization.py deleted file mode 100644 index 2b1b62a..0000000 --- a/aymurai/api/endpoints/routers/database/anonymization.py +++ /dev/null @@ -1,108 +0,0 @@ -from pydantic import UUID5 -from fastapi import Body, Depends -from sqlmodel import Session, select -from fastapi.routing import APIRouter, HTTPException - -from aymurai.database.utils import text_to_uuid -from aymurai.database.session import get_session -from aymurai.meta.api_interfaces import TextRequest, SuccessResponse -from aymurai.database.schema import ( - AnonymizationParagraph, - AnonymizationParagraphRead, - AnonymizationParagraphCreate, - AnonymizationParagraphUpdate, -) -from aymurai.database.crud.anonymization.paragraph import ( - anonymization_paragraph_read, - anonymization_paragraph_create, - anonymization_paragraph_delete, - anonymization_paragraph_update, -) - -router = APIRouter() - - -@router.post("/paragraph", response_model=AnonymizationParagraphRead) -async def api_paragraph_create( - paragraph_in: AnonymizationParagraphCreate, - session: Session = Depends(get_session), -): - """ - Add a new paragraph to the database - """ - - paragraph = anonymization_paragraph_create(paragraph_in, session) - return paragraph - - -@router.get("/paragraph", response_model=AnonymizationParagraphRead | None) -async def paragraph_read_by_text( - data: TextRequest, - session: Session = Depends(get_session), -) -> AnonymizationParagraphRead | None: - paragraph_id = text_to_uuid(data.text) - - return anonymization_paragraph_read(paragraph_id, session) - - -@router.post("/paragraph/text_to_uuid") -async def paragraph_get_uuid(text: str = Body(...)) -> str: - return str(text_to_uuid(text)) - - -@router.get("/paragraph/{paragraph_id}", response_model=AnonymizationParagraphRead) -async def api_paragraph_read( - paragraph_id: UUID5, - session: Session = Depends(get_session), -) -> AnonymizationParagraphRead: - statement = select(AnonymizationParagraph).where( - AnonymizationParagraph.id == paragraph_id - ) - data = session.exec(statement).first() - return data - - -@router.put("/paragraph/{paragraph_id}", response_model=AnonymizationParagraphRead) -async def api_paragraph_update( - paragraph_id: UUID5, - data: AnonymizationParagraphUpdate, - session: Session = Depends(get_session), -) -> AnonymizationParagraphRead: - try: - paragraph = anonymization_paragraph_update(paragraph_id, data, session) - except ValueError as e: - raise HTTPException(status_code=404, detail=str(e)) - return paragraph - - -@router.delete("/paragraph/{paragraph_id}", response_model=SuccessResponse) -async def api_paragraph_delete( - paragraph_id: UUID5, - session: Session = Depends(get_session), -): - try: - anonymization_paragraph_delete(paragraph_id, session) - except ValueError as e: - raise HTTPException(status_code=404, detail=str(e)) - - return SuccessResponse(id=paragraph_id, msg="Item deleted") - - -# @router.post("/document") -# async def document_add(): -# pass - - -# @router.get("/document/{document_id}") -# async def document_get(): -# pass - - -# @router.put("/document/{document_id}") -# async def document_update(): -# pass - - -# @router.delete("/document/{document_id}") -# async def document_delete(): -# pass diff --git a/aymurai/api/endpoints/routers/database/datapublic.py b/aymurai/api/endpoints/routers/database/datapublic.py deleted file mode 100644 index 0b58681..0000000 --- a/aymurai/api/endpoints/routers/database/datapublic.py +++ /dev/null @@ -1,100 +0,0 @@ -import tempfile - -import pandas as pd -from fastapi import Depends -from sqlmodel import Session, select -from fastapi.routing import APIRouter -from fastapi.responses import FileResponse - -from aymurai.database.session import get_session -from aymurai.meta.api_interfaces import SuccessResponse -from aymurai.api.endpoints.meta.database import ExportFormat -from aymurai.database.schema import ( - DataPublicDataset, - DataPublicDatasetRead, - DataPublicDatasetCreate, - DataPublicDatasetUpdate, -) - -router = APIRouter() - - -@router.get("/export") -async def database_export( - format: ExportFormat = ExportFormat.CSV, - session: Session = Depends(get_session), -) -> FileResponse: - statement = select(DataPublicDataset) - data = pd.read_sql(statement, session.bind) - - with tempfile.NamedTemporaryFile(delete=False) as temp: - if format == ExportFormat.CSV: - data.to_csv(temp.name, index=False) - extension = "csv" - elif format == ExportFormat.JSON: - data.to_json(temp.name, orient="records") - extension = "json" - else: - raise ValueError("Invalid export format") - - return FileResponse( - path=temp.name, - filename=f"datapublic.{extension}", - media_type="application/octet-stream", - ) - - -@router.post("/items/add", response_model=SuccessResponse) -async def item_add( - data: DataPublicDatasetCreate, - session: Session = Depends(get_session), -): - item = DataPublicDataset.model_validate(data) - - session.add(item) - session.commit() - session.refresh(item) - - return SuccessResponse(id=item.id, msg="Item added") - - -@router.get("/items/{item_id}", response_model=DataPublicDatasetRead) -async def item_get( - item_id: int, - session: Session = Depends(get_session), -) -> DataPublicDatasetRead: - statement = select(DataPublicDataset).where(DataPublicDataset.id == item_id) - data = session.exec(statement).first() - return data - - -@router.put("/items/{item_id}", response_model=DataPublicDatasetRead) -async def item_update( - item_id: int, - data: DataPublicDatasetUpdate, - session: Session = Depends(get_session), -) -> DataPublicDatasetRead: - statement = select(DataPublicDataset).where(DataPublicDataset.id == item_id) - data_db = session.exec(statement).first() - - for field, value in data.model_dump(exclude_unset=True).items(): - setattr(data_db, field, value) - - session.add(data_db) - session.commit() - session.refresh(data_db) - - return data_db - - -@router.delete("/items/{item_id}", response_model=SuccessResponse) -async def item_delete( - item_id: int, - session: Session = Depends(get_session), -): - statement = select(DataPublicDataset).where(DataPublicDataset.id == item_id) - data = session.exec(statement).first() - session.delete(data) - session.commit() - - return SuccessResponse(id=item_id, msg="Item deleted") diff --git a/aymurai/api/endpoints/routers/database/dump.py b/aymurai/api/endpoints/routers/database/dump.py new file mode 100644 index 0000000..cbeb003 --- /dev/null +++ b/aymurai/api/endpoints/routers/database/dump.py @@ -0,0 +1,34 @@ +import os + +from fastapi import HTTPException +from fastapi.responses import FileResponse +from fastapi.routing import APIRouter + +from aymurai.logger import get_logger +from aymurai.settings import settings + +logger = get_logger(__name__) + +router = APIRouter() + + +@router.get("/database/export") +async def database_dump() -> FileResponse: + # Only support SQLite dumps for now + db_uri = str(settings.SQLALCHEMY_DATABASE_URI) + if not db_uri.startswith("sqlite:///"): + raise HTTPException( + status_code=400, + detail="Database export only supported for SQLite", + ) + + db_file = db_uri.removeprefix("sqlite:///") + if not os.path.exists(db_file): + raise HTTPException(status_code=404, detail="Database file not found") + + # Return the raw SQLite file + return FileResponse( + path=db_file, + filename="aymurai-backend.db", + media_type="application/octet-stream", + ) diff --git a/aymurai/api/endpoints/routers/datapublic/__init__.py b/aymurai/api/endpoints/routers/datapublic/__init__.py index e69de29..a164ab3 100644 --- a/aymurai/api/endpoints/routers/datapublic/__init__.py +++ b/aymurai/api/endpoints/routers/datapublic/__init__.py @@ -0,0 +1,2 @@ +# ruff: noqa: F401 +from . import dataset, validation diff --git a/aymurai/api/endpoints/routers/datapublic/datapublic.py b/aymurai/api/endpoints/routers/datapublic/datapublic.py deleted file mode 100644 index b83ae0d..0000000 --- a/aymurai/api/endpoints/routers/datapublic/datapublic.py +++ /dev/null @@ -1,161 +0,0 @@ -import os -from threading import Lock - -import torch -from fastapi import Body, Depends, Query, HTTPException -from fastapi.routing import APIRouter -from pydantic import UUID5 -from sqlmodel import Session - -from aymurai.api.utils import load_pipeline -from aymurai.database.schema import ( - DataPublicParagraph, - DataPublicDocument, - DataPublicDocumentParagraph, -) -from aymurai.database.session import get_session -from aymurai.database.utils import text_to_uuid -from aymurai.logger import get_logger -from aymurai.meta.api_interfaces import ( - DataPublicDocumentAnnotations, - DocumentInformation, - TextRequest, -) -from aymurai.settings import settings -from aymurai.utils.misc import get_element - -logger = get_logger(__name__) - - -RESOURCES_BASEPATH = settings.RESOURCES_BASEPATH -torch.set_num_threads = 100 # FIXME: polemic ? -pipeline_lock = Lock() - - -router = APIRouter() - - -# MARK: Predict -@router.post("/predict/{document_id}", response_model=DocumentInformation) -async def predict_over_text( - document_id: UUID5, - text_request: TextRequest = Body({"text": "Buenos Aires, 17 de noviembre 2024"}), - use_cache: bool = Query( - True, description="Use cache to store or retrive predictions" - ), - session: Session = Depends(get_session), -) -> DocumentInformation: - logger.info("datapublic predict single") - - logger.info(f"Checking cache (use cache: {use_cache})") - text = text_request.text - paragraph_id = text_to_uuid(text) - - cached_prediction = session.get(DataPublicParagraph, paragraph_id) - if cached_prediction and use_cache: - logger.info(f"cache loaded from key: {paragraph_id}") - logger.debug(f"{cached_prediction}") - labels = cached_prediction.prediction - return DocumentInformation(document=cached_prediction.text, labels=labels or []) - - # load datapublic pipeline - logger.info("Running prediction") - item = [{"path": "empty", "data": {"doc.text": text_request.text}}] - pipeline = load_pipeline( - os.path.join(RESOURCES_BASEPATH, "pipelines", "production", "full-paragraph") - ) - - with pipeline_lock: - processed = pipeline.preprocess(item) - processed = pipeline.predict_single(processed[0]) - processed = pipeline.postprocess([processed]) - - text = get_element(processed[0], ["data", "doc.text"]) or "" - labels = get_element(processed[0], ["predictions", "entities"]) or [] - - if use_cache: - logger.info(f"saving in cache: {paragraph_id}") - - document = session.get(DataPublicDocument, document_id) - if not document: - document = DataPublicDocument(id=document_id) - - paragraph = DataPublicParagraph(id=paragraph_id, text=text, prediction=labels) - document_paragraphs = DataPublicDocumentParagraph( - paragraph_id=paragraph_id, - document_id=document_id, - ) - - session.add_all([document, paragraph, document_paragraphs]) - session.commit() - session.refresh(paragraph) - - # paragraph = datapublic_paragraph_create(paragraph, session=session) - - return DocumentInformation(document=text, labels=paragraph.prediction) - - -# MARK: Validate Paragraph -# @router.post("/validation", response_model=list[DocLabel] | None) -# async def datapublic_get_paragraph_validation( -# text_request: TextRequest = Body( -# {"text": "Acusado: Ramiro Marrón DNI 34.555.666."} -# ), -# session: Session = Depends(get_session), -# ) -> list[DocLabel] | None: -# """ -# Get the validation labels for a given paragraph text. -# """ - -# text = text_request.text -# paragraph_id = text_to_uuid(text) - -# paragraph = datapublic_paragraph_read(paragraph_id, session=session) -# if not paragraph: -# return None - -# return paragraph.validation - - -# MARK: GET Validation Document -@router.get("/validation/document/{document_id}") -async def datapublic_read_document_validation( - document_id: UUID5, - session: Session = Depends(get_session), -) -> DataPublicDocumentAnnotations | None: - logger.info(f"loading annotations => {document_id}") - - document = session.get(DataPublicDocument, document_id) - if not document: - raise HTTPException( - status_code=404, - detail=f"Document not found: {document_id}", - ) - - annotations = document.validation - if not annotations: - return None - - return DataPublicDocumentAnnotations(root=annotations) - - -# MARK: POST Validation Document -@router.post("/validation/document/{document_id}") -async def datapublic_save_document_validation( - document_id: UUID5, - annotations: DataPublicDocumentAnnotations = Body(..., example={}), - session: Session = Depends(get_session), -) -> None: - logger.info(f"processing annotations => {document_id}") - - document = session.get(DataPublicDocument, document_id) - if not document: - document = DataPublicDocument(id=document_id) - - document.validation = annotations.root - session.add(document) - session.commit() - session.refresh(document) - logger.info(f"document validation saved => {document_id}") - - return diff --git a/aymurai/api/endpoints/routers/datapublic/dataset.py b/aymurai/api/endpoints/routers/datapublic/dataset.py index 8916b54..db42ffe 100644 --- a/aymurai/api/endpoints/routers/datapublic/dataset.py +++ b/aymurai/api/endpoints/routers/datapublic/dataset.py @@ -1,109 +1,123 @@ -import os import json +import os import tempfile +from datetime import datetime +from enum import Enum import pandas as pd -from pydantic import UUID4, UUID5 -from sqlmodel import Session, select -from fastapi.routing import APIRouter +from fastapi import Depends from fastapi.responses import FileResponse +from fastapi.routing import APIRouter +from sqlmodel import Session, select from starlette.background import BackgroundTask -from fastapi import Depends, UploadFile, HTTPException -from aymurai.logger import get_logger -from aymurai.database.session import get_session -from aymurai.meta.api_interfaces import SuccessResponse from aymurai.api.endpoints.meta.database import ExportFormat from aymurai.database.schema import ( - DataPublicDataset, - DataPublicDatasetRead, - DataPublicDatasetCreate, + Datapublic, + DatapublicFormat, + ModelType, ) +from aymurai.database.session import get_session +from aymurai.logger import get_logger logger = get_logger(__name__) -router = APIRouter() - - -@router.post("", response_model=DataPublicDatasetRead) -async def datapublic_dataset_create( - data: DataPublicDatasetCreate, - session: Session = Depends(get_session), -) -> DataPublicDatasetRead: - data = DataPublicDataset(**data.model_dump()) +# add a Dataset enum to include "all" and map NONE to "unsorted" +class Dataset(str, Enum): + ALL = "all" + UNSORTED = "unsorted" - with session.begin(): - session.add(data) - session.refresh(data) +router = APIRouter() - return DataPublicDatasetRead(**data.model_dump()) +def get_records( + session: Session, + dataset: Dataset = Dataset.ALL, + start_date: datetime | None = None, + end_date: datetime | None = None, + offset: int = 0, + limit: int = 100, +) -> pd.DataFrame: + # build base query + statement = select(Datapublic) + + # filter by dataset/format + if dataset != Dataset.ALL: + if dataset == Dataset.UNSORTED: + fmt = DatapublicFormat.NONE + else: + fmt = DatapublicFormat(dataset.value) + statement = statement.where(Datapublic.validation_format == fmt) -@router.get("/{document_id}", response_model=DataPublicDatasetRead) -async def datapublic_dataset_read( - document_id: UUID4 | UUID5, - session: Session = Depends(get_session), -) -> DataPublicDatasetRead: - statement = select(DataPublicDataset).where(DataPublicDataset.id == document_id) - data = session.exec(statement).first() + # optional date filters + if start_date: + statement = statement.where(Datapublic.created_at >= start_date) + if end_date: + statement = statement.where(Datapublic.created_at <= end_date) - if not data: - raise HTTPException(status_code=404, detail="Document not found") + # pagination + statement = statement.offset(offset).limit(limit) + results = session.exec(statement).all() - return DataPublicDatasetRead(**data.model_dump()) + return pd.DataFrame([item.records for item in results]) -@router.post("/batch/import/json", response_model=SuccessResponse) -async def datapublic_dataset_batch_create( - json_file: UploadFile, +@router.get(f"/pipeline/{ModelType.DATAPUBLIC}/records") +async def get_datapublic_records( + dataset: Dataset = Dataset.ALL, + start_date: datetime | None = None, + end_date: datetime | None = None, + offset: int = 0, + limit: int = 100, session: Session = Depends(get_session), -) -> SuccessResponse: - try: - data = json.load(json_file.file) - - with session.begin(): - for item in data: - dataset = DataPublicDataset.model_validate(item) - session.add(dataset) +) -> list[dict]: + df = get_records( + session=session, + dataset=dataset, + start_date=start_date, + end_date=end_date, + offset=offset, + limit=limit, + ) - except json.JSONDecodeError as e: - raise HTTPException(status_code=400, detail=f"Invalid JSON format: {str(e)}") - except Exception as e: - raise HTTPException(status_code=400, detail=str(e)) + encoded = df.to_json(orient="records", date_format="iso", index=False) - return SuccessResponse(message="Data imported successfully") + return json.loads(encoded) -@router.get("/batch/export") -async def datapublic_dataset_batch_read( - format: ExportFormat = ExportFormat.CSV, +@router.get(f"/pipeline/{ModelType.DATAPUBLIC}/records/export") +async def database_export( + dataset: Dataset = Dataset.ALL, + start_date: datetime | None = None, + end_date: datetime | None = None, + offset: int = 0, + limit: int = 100, session: Session = Depends(get_session), ) -> FileResponse: - statement = select(DataPublicDataset) - dump = session.exec(statement).all() - - # re-encode data - data = [DataPublicDatasetRead(**row.model_dump()) for row in dump] - data = [json.loads(row.model_dump_json()) for row in data] + data = get_records( + session=session, + dataset=dataset, + start_date=start_date, + end_date=end_date, + offset=offset, + limit=limit, + ) with tempfile.NamedTemporaryFile(delete=False) as temp: if format == ExportFormat.CSV: - df = pd.DataFrame(data) - df.to_csv(temp.name, index=False) + data.to_csv(temp.name, index=False) extension = "csv" elif format == ExportFormat.JSON: - temp.write(json.dumps(data).encode("utf-8")) + data.to_json(temp.name, orient="records") extension = "json" else: - raise HTTPException(status_code=400, detail="Invalid export format") - - export_filename = temp.name + raise ValueError("Invalid export format") return FileResponse( - path=export_filename, + path=temp.name, filename=f"datapublic.{extension}", + background=BackgroundTask(os.remove, temp.name), media_type="application/octet-stream", - background=BackgroundTask(os.remove, export_filename), ) diff --git a/aymurai/api/endpoints/routers/datapublic/validation.py b/aymurai/api/endpoints/routers/datapublic/validation.py new file mode 100644 index 0000000..5690c56 --- /dev/null +++ b/aymurai/api/endpoints/routers/datapublic/validation.py @@ -0,0 +1,80 @@ +from fastapi import Body, Depends, HTTPException +from fastapi.responses import JSONResponse +from fastapi.routing import APIRouter +from pydantic import UUID4, UUID5 +from sqlmodel import Session, select +from starlette.status import HTTP_201_CREATED + +from aymurai.database.schema import ( + Datapublic, + DatapublicFormat, + DatapublicPublic, + ModelType, +) +from aymurai.database.session import get_session +from aymurai.logger import get_logger + +logger = get_logger(__name__) + + +router = APIRouter() + + +# MARK: GET Validation Document +@router.get(f"/pipeline/{ModelType.DATAPUBLIC}/document/{{document_id}}/validation") +async def datapublic_get_document_validation( + document_id: UUID4 | UUID5, + session: Session = Depends(get_session), +) -> DatapublicPublic: + statement = select(Datapublic).where(Datapublic.fk_document == document_id) + datapublic = session.exec(statement).first() + if not datapublic: + raise HTTPException( + status_code=404, + detail=f"datapublic validation not found for document: {document_id}", + ) + + logger.info(f"datapublic validation loaded for document: {document_id}") + + return DatapublicPublic.model_validation(datapublic) + + +# MARK: POST Validation Document +@router.post(f"/pipeline/{ModelType.DATAPUBLIC}/document/{{document_id}}/validation") +async def datapublic_save_document_validation( + document_id: UUID4 | UUID5, + records: dict = Body(..., example={}), + validation_format: DatapublicFormat = DatapublicFormat.NONE, + session: Session = Depends(get_session), +) -> JSONResponse: + logger.info(f"processing annotations => {document_id}") + + statement = select(Datapublic).where(Datapublic.fk_document == document_id) + datapublic = session.exec(statement).first() + if not datapublic: + datapublic = Datapublic( + fk_document=document_id, + records={}, + validation_format=validation_format, + ) + + match validation_format: + case DatapublicFormat.NONE: + logger.info(f"validation format: {validation_format}. skipping") + case _: + NotImplementedError( + f"validation format: {validation_format} not implemented" + ) + + datapublic.records = records + datapublic.validation_format = validation_format + + session.add(datapublic) + session.commit() + + logger.info(f"datapublic validation saved for document: {document_id}") + + return JSONResponse( + status_code=HTTP_201_CREATED, + content={"message": f"datapublic validation saved for document: {document_id}"}, + ) diff --git a/aymurai/api/endpoints/routers/document/__init__.py b/aymurai/api/endpoints/routers/document/__init__.py new file mode 100644 index 0000000..32d24e2 --- /dev/null +++ b/aymurai/api/endpoints/routers/document/__init__.py @@ -0,0 +1,2 @@ +# ruff: noqa: F401 +from . import convert, extract, predict diff --git a/aymurai/api/endpoints/routers/document/convert.py b/aymurai/api/endpoints/routers/document/convert.py new file mode 100644 index 0000000..ac274fa --- /dev/null +++ b/aymurai/api/endpoints/routers/document/convert.py @@ -0,0 +1,144 @@ +import os +import subprocess +import tempfile +from enum import Enum +from typing import Literal + +import pymupdf4llm +import pypandoc +from fastapi import HTTPException, Path, Query, UploadFile +from fastapi.responses import FileResponse +from fastapi.routing import APIRouter +from starlette import status +from starlette.background import BackgroundTask + +from aymurai.logger import get_logger +from aymurai.settings import settings + +logger = get_logger(__name__) +router = APIRouter() + + +class FileFormat(str, Enum): + pdf = "pdf" + docx = "docx" + odt = "odt" + + +def libreoffice_convert( + input: str, + format: Literal["pdf", "docx", "odt", "txt"], + output_dir=tempfile.gettempdir(), + extra_args: str = "", +) -> str: + cmd = f"{settings.LIBREOFFICE_BIN} --headless --convert-to {format} --outdir {output_dir} {input} {extra_args}" + subprocess.run(cmd, shell=True, check=True) + + filename = os.path.basename(input) + output = os.path.join(output_dir, filename.replace(input.split(".")[-1], format)) + + if not os.path.exists(output): + raise RuntimeError(f"LibreOffice conversion failed: {output}") + + return output + + +async def convert_libreoffice( + file: UploadFile, + output_format: Literal["pdf", "docx", "odt"], + extra_args: str = "", +) -> FileResponse: + _, suffix = os.path.splitext(file.filename) + + tmp_file = tempfile.NamedTemporaryFile(suffix=suffix, delete=False) + with open(tmp_file.name, "wb") as tmp: + data = file.file.read() + tmp.write(data) + + output = libreoffice_convert( + tmp_file.name, + format=output_format, + extra_args=extra_args, + ) + os.remove(tmp_file.name) + + return FileResponse( + output, + background=BackgroundTask(os.remove, output), + media_type="application/octet-stream", + filename=os.path.basename(file.filename).replace(suffix, f".{output_format}"), + ) + + +async def convert_pdf_pandoc( + file: UploadFile, + output_format: Literal["docx", "odt"], +) -> FileResponse: + _, suffix = os.path.splitext(file.filename) + if suffix != ".pdf": + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail="Expected a .pdf file", + ) + + with tempfile.NamedTemporaryFile(suffix=".pdf", delete=True) as tmp_file: + tmp_file.write(file.file.read()) + tmp_file.flush() + + text = pymupdf4llm.to_markdown( + tmp_file.name, + write_images=True, + embed_images=True, + image_size_limit=0, + ) + + output = tempfile.mktemp(suffix=f".{output_format}") + pypandoc.convert_text(text, output_format, format="md", outputfile=output) + + return FileResponse( + output, + background=BackgroundTask(os.remove, output), + media_type="application/octet-stream", + filename=os.path.basename(file.filename).replace(".pdf", f".{output_format}"), + ) + + +@router.post("/convert/{source}/{target}") +async def convert_file( + file: UploadFile, + source: FileFormat = Path(...), + target: FileFormat = Path(...), + backend: Literal["libreoffice", "pandoc"] = Query("libreoffice"), +) -> FileResponse: + _, suffix = os.path.splitext(file.filename) + if suffix.lower() != f".{source.value}": + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUES, + detail=f"Expected a .{source.value} file", + ) + if source == target: + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail="Source and target formats are the same.", + ) + + # Set extra_args for libreoffice conversions + extra_args = '--infilter="writer_pdf_import"' if source == FileFormat.pdf else "" + + match (source, backend): + case (FileFormat.pdf, "pandoc"): + return await convert_pdf_pandoc(file, output_format=target.value) + case (_, "libreoffice"): + return await convert_libreoffice( + file, + output_format=target.value, + extra_args=extra_args, + ) + case _: + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail=( + f"Conversion from {source.value} to {target.value} " + f"with backend {backend} is not supported." + ), + ) diff --git a/aymurai/api/endpoints/routers/document/extract.py b/aymurai/api/endpoints/routers/document/extract.py new file mode 100644 index 0000000..4147898 --- /dev/null +++ b/aymurai/api/endpoints/routers/document/extract.py @@ -0,0 +1,131 @@ +import concurrent.futures +import os +import re +import tempfile + +from fastapi import Depends, HTTPException, UploadFile +from fastapi.routing import APIRouter +from more_itertools import unique_justseen +from sqlmodel import Session +from starlette import status + +from aymurai.database.schema import Document, DocumentPublic, Paragraph +from aymurai.database.session import get_session +from aymurai.database.utils import data_to_uuid +from aymurai.logger import get_logger +from aymurai.text.extraction import extract_document +from aymurai.text.normalize import document_normalize + +logger = get_logger(__name__) + +router = APIRouter() + + +def extraction(path: str) -> str: + """ + Wrapper function to call the extract_document function. + This is necessary to ensure that the function can be pickled and run in a separate process. + """ + text = extract_document(path) + return document_normalize(text) if text else "" + + +def run_safe_text_extraction(path: str, timeout_s: float = 5) -> str: + """ + Runs the text extraction in a separate process to avoid blocking the main thread. + This is useful for long-running tasks or when the extraction might hang. + Args: + path (str): Path to the file to be processed. + timeout_s (float): Timeout in seconds for the extraction process. + Returns: + str: Extracted text from the document. + Raises: + TimeoutError: If the extraction process exceeds the specified timeout. + """ + + with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor: + future = executor.submit(extraction, path) + try: + return future.result(timeout=timeout_s) + except concurrent.futures.TimeoutError: + # Cancel/killing the subprocess + future.cancel() + raise + + +@router.post("/extract", response_model=DocumentPublic) +def plain_text_extractor( + file: UploadFile, + session: Session = Depends(get_session), + use_cache: bool = True, +) -> DocumentPublic: + logger.info(f"receiving => {file.filename}") + _, extension = os.path.splitext(file.filename) + extension = extension.lstrip(".").lower() + logger.info(f"detected extension: {extension} ({file.content_type})") + + data = file.file.read() + document_id = data_to_uuid(data) + + document = session.get(Document, document_id) + if document and use_cache: + logger.warning(f"Document already exists: {document_id}. skipping creation.") + return document + + # Use delete=False to avoid the file being deleted when the NamedTemporaryFile object is closed + # This is necessary on Windows, as the file is locked by the file object and cannot be deleted + with tempfile.NamedTemporaryFile(delete=False, suffix=f".{extension}") as tmp_file: + try: + tmp_filename = tmp_file.name + tmp_file.write(data) + tmp_file.flush() + tmp_file.close() + + logger.info(f"saved temp file on local storage => {tmp_filename}") + + item = {"path": tmp_filename} + + logger.info("processing data item") + logger.info(f"{item}") + + document = run_safe_text_extraction(tmp_filename, timeout_s=5) + + except concurrent.futures.TimeoutError: + logger.error(f"Timeout while extracting text from {file.filename}") + raise HTTPException( + status_code=status.HTTP_504_GATEWAY_TIMEOUT, + detail="Text extraction timed out", + ) + + except Exception as e: + logger.error(f"error while processing data item: {e}") + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail=str(e), + ) + + os.remove(tmp_filename) + logger.info(f"removed temp file from local storage => {tmp_filename}") + + paragraph_text = [text.strip() for text in document.split("\n") if text.strip()] + paragraph_text = [re.sub(r"\s{2,}", " ", text) for text in paragraph_text] + paragraph_text = list(unique_justseen(paragraph_text)) + + # Add paragraphs to the database + # validation MUST be at least an empty list, to remember user feedback + paragraphs = [ + Paragraph(text=paragraph, index=i) for i, paragraph in enumerate(paragraph_text) + ] + + paragraph_text = Document( + id=document_id, + data=data, + name=file.filename, + paragraphs=paragraphs, + ) + + session.add_all([paragraph_text] + paragraphs) + session.commit() + session.refresh(paragraph_text) + + return paragraph_text diff --git a/aymurai/api/endpoints/routers/document/predict.py b/aymurai/api/endpoints/routers/document/predict.py new file mode 100644 index 0000000..1bec869 --- /dev/null +++ b/aymurai/api/endpoints/routers/document/predict.py @@ -0,0 +1,50 @@ +from fastapi import Depends, HTTPException +from fastapi.routing import APIRouter +from more_itertools import flatten +from pydantic import UUID4, UUID5 +from sqlmodel import Session + +from aymurai.database.crud.prediction import ( + read_document_prediction_paragraphs, +) +from aymurai.database.meta.document import Document +from aymurai.database.schema import ( + ModelType, +) +from aymurai.database.session import get_session +from aymurai.logger import get_logger +from aymurai.meta.entities import DocLabel +from aymurai.settings import settings + +logger = get_logger(__name__) + + +RESOURCES_BASEPATH = settings.RESOURCES_BASEPATH + + +router = APIRouter() + + +# MARK: Get Document labels +@router.get("/pipeline/{pipeline_type}/document/{document_id}") +async def get_document_labels( + document_id: UUID4 | UUID5, + pipeline_type: ModelType, + session: Session = Depends(get_session), +) -> list[DocLabel]: + # ———————— Sanity check —————————————————————————————————————————————————————————— + document = session.get(Document, document_id) + if not document: + raise HTTPException( + status_code=404, + detail=f"Document not found: {document_id}", + ) + + annotations = read_document_prediction_paragraphs( + session=session, + document_id=document_id, + model_type=pipeline_type, + ) + + labels = [para.prediction.labels for para in annotations if para.prediction] + return list(flatten(labels)) diff --git a/aymurai/api/endpoints/routers/document/search.py b/aymurai/api/endpoints/routers/document/search.py new file mode 100644 index 0000000..dd1ef37 --- /dev/null +++ b/aymurai/api/endpoints/routers/document/search.py @@ -0,0 +1,154 @@ +import unicodedata +import uuid + +import numpy as np +import pandas as pd +import regex +from fastapi import Depends, HTTPException +from fastapi.routing import APIRouter +from more_itertools import flatten +from pydantic import UUID4, UUID5 +from sklearn.cluster import DBSCAN +from sklearn.feature_extraction.text import CountVectorizer +from sqlmodel import Session + +from aymurai.database.crud.prediction import read_document_prediction_paragraphs +from aymurai.database.schema import Document, ModelType +from aymurai.database.session import get_session +from aymurai.logger import get_logger +from aymurai.meta.entities import DocLabel + +logger = get_logger(__name__) + +router = APIRouter() + + +# Normalize helper +def normalize_text(text): + text = unicodedata.normalize("NFKD", text) + text = "".join(ch for ch in text if unicodedata.category(ch) != "Mn") + text = regex.sub(r"\s+", " ", text) + text = regex.sub(r"\p{P}", "", text) + return text.lower() + + +def cluster_entities(entities: list[DocLabel], eps: float = 0.01, min_samples: int = 2): + """ + Cluster entity texts in a group and return a DataFrame with columns: + text, norm_text, index, cluster. Uses binary vectorization and Jaccard-DBSCAN. + """ + + indexed_entities = [(i, entity) for i, entity in enumerate(entities)] + indexed_entities = sorted(indexed_entities, key=lambda x: x[1].attrs.aymurai_label) + + # Prepare raw and normalized texts + texts = [ent[1].text for ent in indexed_entities] + norm_texts = [normalize_text(t) for t in texts] + + # --------- Binary vectorization of words ---------------- + vectorizer = CountVectorizer(binary=True, token_pattern=r"\b\w+\b") + matrix = vectorizer.fit_transform( + norm_texts + ) # sparse matrix (n_samples x n_features) + + # --------- Jaccard distance matrix ---------------- + ints = (matrix @ matrix.T).toarray() + row_sums = matrix.sum(axis=1).A1 + union = row_sums[:, None] + row_sums[None, :] - ints + # Avoid division by zero and compute distances + with np.errstate(divide="ignore", invalid="ignore"): + X = 1 - (ints / union) + X[union == 0] = 1.0 + + # -------- DBSCAN ---------------------------------------------------- + db = DBSCAN(eps=eps, min_samples=min_samples, metric="precomputed") + labels = db.fit_predict(X) + + # Merge clusters by overlapping centroids + unique_lbls = sorted(set(labels) - {-1}) + if unique_lbls: + # build centroid binary vectors + centroid_vectors = [] + for lbl in unique_lbls: + mat = matrix[labels == lbl] + centroid = (mat.sum(axis=0) > 0).A1 # boolean mask + centroid_vectors.append(centroid) + + C = np.vstack(centroid_vectors).astype(bool) + sim = (C.astype(int) @ C.T.astype(int)) > 0 + mapping = { + lbl: unique_lbls[int(np.argmax(sim[idx]))] + for idx, lbl in enumerate(unique_lbls) + } + labels = [mapping.get(lbl, -1) for lbl in labels] + + # Build results DataFrame + df = ( + pd.DataFrame( + { + "id": [ent[1].id for ent in indexed_entities], + "index": [ent[0] for ent in indexed_entities], + "paragraph_id": [ent[1].fk_paragraph for ent in indexed_entities], + "label": [ent[1].attrs.aymurai_label for ent in indexed_entities], + "text": texts, + "norm_text": norm_texts, + "cluster": labels, + } + ) + .sort_values("cluster") + .reset_index(drop=True) + ) + df["id"] = df["id"].astype(uuid.UUID) + df["paragraph_id"] = df["paragraph_id"].astype(uuid.UUID) + df.set_index("id", inplace=True) + df.sort_values(by=["cluster", "index"], inplace=True) + + return df + + +@router.get("/pipeline/{pipeline_type}/document/{document_id}/search") +async def search( + pipeline_type: ModelType, + document_id: UUID4 | UUID5, + query: str | None = None, + label_id: UUID4 | UUID5 | None = None, + session: Session = Depends(get_session), +) -> list[DocLabel] | None: + # ———————— Sanity check ———————————————————————————————————————————————————————— + if (not query and not label_id) or (query and label_id): + raise HTTPException( + status_code=400, + detail="Either query or label_id must be provided", + ) + + document = session.get(Document, document_id) + if not document: + raise HTTPException(status_code=404, detail="Document not found") + + # ———————— Get annotations ———————————————————————————————————————————————————————— + annotations = read_document_prediction_paragraphs( + session=session, + document_id=document_id, + model_type=pipeline_type, + ) + + labels = [para.prediction.labels for para in annotations if para.prediction] + entities = list(flatten(labels)) + + clusters_df = cluster_entities(entities) + + if label_id: + label_cluster = clusters_df.loc[label_id, "cluster"] + cluster = clusters_df[clusters_df["cluster"] == label_cluster] + + elif query: + query = normalize_text(query) + cluster = cluster[cluster["norm_text"].str.contains(query, na=False)] + + if cluster.empty: + return None + + indices = cluster.index.tolist() + entities = [entities[i] for i in indices] + + return entities diff --git a/aymurai/api/endpoints/routers/misc/convert.py b/aymurai/api/endpoints/routers/misc/convert.py deleted file mode 100644 index 058534e..0000000 --- a/aymurai/api/endpoints/routers/misc/convert.py +++ /dev/null @@ -1,208 +0,0 @@ -import os -import tempfile -import subprocess -from threading import Lock -from typing import Literal - -import pypandoc -import pymupdf4llm -from fastapi import UploadFile -from fastapi.responses import FileResponse -from fastapi.routing import APIRouter -from starlette.background import BackgroundTask - -from aymurai.api.exceptions import UnsupportedFileType -from aymurai.logger import get_logger -from aymurai.settings import settings - -logger = get_logger(__name__) -pipeline_lock = Lock() - -router = APIRouter() - - -def libreoffice_convert( - input: str, - format: Literal["pdf", "docx", "odt", "txt"], - output_dir=tempfile.gettempdir(), - extra_args: str = "", -) -> str: - cmd = f"{settings.LIBREOFFICE_BIN} --headless --convert-to {format} --outdir {output_dir} {input} {extra_args}" - subprocess.run(cmd, shell=True, check=True) - - filename = os.path.basename(input) - output = os.path.join(output_dir, filename.replace(input.split(".")[-1], format)) - - if not os.path.exists(output): - raise RuntimeError(f"LibreOffice conversion failed: {output}") - - return output - - -async def convert_libreoffice( - file: UploadFile, - output_format: Literal["pdf", "docx", "odt"], - extra_args: str = "", -) -> FileResponse: - _, suffix = os.path.splitext(file.filename) - - tmp_file = tempfile.NamedTemporaryFile(suffix=suffix, delete=False) - with open(tmp_file.name, "wb") as tmp: - data = file.file.read() - tmp.write(data) - - output = libreoffice_convert( - tmp_file.name, - format=output_format, - extra_args=extra_args, - ) - os.remove(tmp_file.name) - - return FileResponse( - output, - background=BackgroundTask(os.remove, output), - media_type="application/octet-stream", - filename=os.path.basename(file.filename).replace(suffix, f".{output_format}"), - ) - - -async def convert_pdf_pandoc( - file: UploadFile, - output_format: Literal["docx", "odt"], -) -> FileResponse: - _, suffix = os.path.splitext(file.filename) - if suffix != ".pdf": - raise UnsupportedFileType(detail="Expected a .pdf file") - - with tempfile.NamedTemporaryFile(suffix=".pdf", delete=True) as tmp_file: - tmp_file.write(file.file.read()) - tmp_file.flush() - - text = pymupdf4llm.to_markdown( - tmp_file.name, - write_images=True, - embed_images=True, - image_size_limit=0, - ) - - output = tempfile.mktemp(suffix=f".{output_format}") - pypandoc.convert_text(text, output_format, format="md", outputfile=output) - - return FileResponse( - output, - background=BackgroundTask(os.remove, output), - media_type="application/octet-stream", - filename=os.path.basename(file.filename).replace(".pdf", f".{output_format}"), - ) - - -# MARK: PDF -> ODT -@router.post("/convert/pdf/odt") -async def convert_pdf_odt( - file: UploadFile, - backend: Literal["libreoffice", "pandoc"] = "libreoffice", -) -> FileResponse: - _, suffix = os.path.splitext(file.filename) - if suffix != ".pdf": - raise UnsupportedFileType(detail="Expected a .pdf file") - - if backend == "libreoffice": - return await convert_libreoffice( - file, - output_format="odt", - extra_args='--infilter="writer_pdf_import"', - ) - elif backend == "pandoc": - return await convert_pdf_pandoc(file, output_format="odt") - raise ValueError("Invalid backend") - - -# MARK: PDF -> DOCX -@router.post("/convert/pdf/docx") -async def convert_pdf_docx( - file: UploadFile, - backend: Literal["libreoffice", "pandoc"] = "libreoffice", -) -> FileResponse: - _, suffix = os.path.splitext(file.filename) - if suffix != ".pdf": - raise UnsupportedFileType(detail="Expected a .pdf file") - - if backend == "libreoffice": - return await convert_libreoffice( - file, - output_format="docx", - extra_args='--infilter="writer_pdf_import"', - ) - elif backend == "pandoc": - return await convert_pdf_pandoc(file, output_format="docx") - raise ValueError("Invalid backend") - - -# MARK: DOCX -> ODT -@router.post("/convert/docx/odt") -async def convert_docx_odt(file: UploadFile) -> FileResponse: - _, suffix = os.path.splitext(file.filename) - if suffix != ".docx": - raise UnsupportedFileType(detail="Expected a .docx file") - - return await convert_libreoffice(file, output_format="odt") - - -# MARK: DOCX -> PDF -@router.post("/convert/docx/pdf") -async def convert_docx_pdf(file: UploadFile) -> FileResponse: - _, suffix = os.path.splitext(file.filename) - if suffix != ".docx": - raise UnsupportedFileType(detail="Expected a .docx file") - - return await convert_libreoffice(file, output_format="pdf") - - -# MARK: ODT -> PDF -@router.post("/convert/odt/pdf") -async def convert_odt_pdf(file: UploadFile) -> FileResponse: - _, suffix = os.path.splitext(file.filename) - if suffix != ".odt": - raise UnsupportedFileType(detail="Expected a .odt file") - - return await convert_libreoffice(file, output_format="pdf") - - -# MARK: ODT -> DOCX -@router.post("/convert/odt/docx") -async def convert_odt_docx(file: UploadFile) -> FileResponse: - _, suffix = os.path.splitext(file.filename) - if suffix != ".odt": - raise UnsupportedFileType(detail="Expected a .odt file") - - return await convert_libreoffice(file, output_format="docx") - - -# # MARK: TXT -> DOCX -# @router.post("/convert/txt/docx") -# async def convert_txt_docx(file: UploadFile) -> FileResponse: -# _, suffix = os.path.splitext(file.filename) -# if suffix != ".txt": -# raise UnsupportedFileType(detail="Expected a .txt file") - -# return await convert_common_txt_docx_doc(file, output_format="docx") - - -# # MARK: TXT -> ODT -# @router.post("/convert/txt/odt") -# async def convert_txt_odt(file: UploadFile) -> FileResponse: -# _, suffix = os.path.splitext(file.filename) -# if suffix != ".txt": -# raise UnsupportedFileType(detail="Expected a .txt file") - -# return await convert_common_txt_docx_doc(file, output_format="odt") - - -# # MARK: TXT -> PDF -# @router.post("/convert/txt/pdf") -# async def convert_txt_pdf(file: UploadFile) -> FileResponse: -# _, suffix = os.path.splitext(file.filename) -# if suffix != ".txt": -# raise UnsupportedFileType(detail="Expected a .txt file") - -# return await convert_common_txt_docx_doc(file, output_format="odt") diff --git a/aymurai/api/endpoints/routers/misc/document_extract.py b/aymurai/api/endpoints/routers/misc/document_extract.py deleted file mode 100644 index c004106..0000000 --- a/aymurai/api/endpoints/routers/misc/document_extract.py +++ /dev/null @@ -1,68 +0,0 @@ -import os -import re -import tempfile -from threading import Lock - -from fastapi import Depends, UploadFile -from fastapi.routing import APIRouter -from more_itertools import unique_justseen - -from aymurai.api.utils import get_pipeline_doc_extract -from aymurai.database.utils import data_to_uuid -from aymurai.logger import get_logger -from aymurai.meta.api_interfaces import Document -from aymurai.pipeline import AymurAIPipeline -from aymurai.text.extraction import MIMETYPE_EXTENSION_MAPPER -from aymurai.utils.misc import get_element - -logger = get_logger(__name__) -pipeline_lock = Lock() - -router = APIRouter() - - -@router.post("/document-extract", response_model=Document) -def plain_text_extractor( - file: UploadFile, - pipeline: AymurAIPipeline = Depends(get_pipeline_doc_extract), -) -> Document: - logger.info(f"receiving => {file.filename}") - extension = MIMETYPE_EXTENSION_MAPPER.get(file.content_type) - logger.info(f"detected extension: {extension} ({file.content_type})") - - data = file.file.read() - - # Use delete=False to avoid the file being deleted when the NamedTemporaryFile object is closed - # This is necessary on Windows, as the file is locked by the file object and cannot be deleted - with tempfile.NamedTemporaryFile(delete=False, suffix=f".{extension}") as tmp_file: - try: - tmp_filename = tmp_file.name - tmp_file.write(data) - tmp_file.flush() - tmp_file.close() - - logger.info(f"saved temp file on local storage => {tmp_filename}") - - item = {"path": tmp_filename} - - logger.info("processing data item") - logger.info(f"{item}") - - with pipeline_lock: - processed = pipeline.preprocess([item]) - - except Exception as e: - logger.error(f"error while processing data item: {e}") - raise e - - os.remove(tmp_filename) - logger.info(f"removed temp file from local storage => {tmp_filename}") - - document_id = data_to_uuid(data) - doc_text: str = get_element(processed[0], ["data", "doc.text"], "") - - document = [text.strip() for text in doc_text.split("\n") if text.strip()] - document = [re.sub(r"\s{2,}", " ", text) for text in document] - document = list(unique_justseen(document)) - - return Document(document=document, document_id=document_id) diff --git a/aymurai/api/endpoints/routers/paragraph/__init__.py b/aymurai/api/endpoints/routers/paragraph/__init__.py new file mode 100644 index 0000000..43926e1 --- /dev/null +++ b/aymurai/api/endpoints/routers/paragraph/__init__.py @@ -0,0 +1,2 @@ +# ruff: noqa: F401 +from . import predict diff --git a/aymurai/api/endpoints/routers/paragraph/predict.py b/aymurai/api/endpoints/routers/paragraph/predict.py new file mode 100644 index 0000000..ea597c0 --- /dev/null +++ b/aymurai/api/endpoints/routers/paragraph/predict.py @@ -0,0 +1,196 @@ +import threading + +from fastapi import Depends, HTTPException, Query +from fastapi.routing import APIRouter +from pydantic import UUID4 +from sqlmodel import Session + +from aymurai.api.utils import load_pipeline +from aymurai.database.crud.model import read_latest_model_by_type +from aymurai.database.crud.prediction import read_prediction_by_text +from aymurai.database.meta.extra import ParagraphPredictionPublic +from aymurai.database.schema import ( + ModelType, + Paragraph, + Prediction, + PredictionCreate, + PredictionPublic, +) +from aymurai.database.session import get_session +from aymurai.logger import get_logger +from aymurai.meta.entities import DocLabel +from aymurai.settings import settings +from aymurai.utils.misc import get_element + +logger = get_logger(__name__) + + +RESOURCES_BASEPATH = settings.RESOURCES_BASEPATH + +prediction_lock = threading.Lock() + +router = APIRouter() + + +# MARK: Paragraph Predict +@router.get( + "/pipeline/{pipeline_type}/paragraph/{paragraph_id}/predict", + response_model=ParagraphPredictionPublic, +) +async def paragraph_predict( + paragraph_id: UUID4, + pipeline_type: ModelType, + use_cache: bool = Query( + True, description="Use cache to store or retrieve predictions" + ), + session: Session = Depends(get_session), +) -> ParagraphPredictionPublic: + """ + Endpoint to predict for a given paragraph of text. + + Args: + paragraph_id (UUID4): The ID of the paragraph to be predicted. + pipeline_type (ModelType): The type of model to be used for prediction. + use_cache (bool): Flag to determine whether to use cache for storing or retrieving predictions. + session (Session): Database session dependency. + + Returns: + ParagraphPredictionPublic: The predicted document information including the text and labels. + """ + model = read_latest_model_by_type(model_type=pipeline_type, session=session) + pipeline = load_pipeline(model.pipeline_path) + + paragraph = session.get(Paragraph, paragraph_id) + + # ———————— Sanity check —————————————————————————————————————————————————————————— + if not paragraph: + raise HTTPException( + status_code=404, + detail=f"Paragraph not found: {paragraph_id}", + ) + + logger.info(f"Running prediction: {model.name} ({model.version})") + + input_text = paragraph.text + # —————— Check cache ————————————————————————————————————————————————————————————— + logger.info(f"Checking cache (use cache: {use_cache})") + cached_pred = read_prediction_by_text( + text=input_text, model_id=model.id, session=session + ) + if cached_pred and use_cache: + logger.info(f"cache loaded from prediction: {cached_pred.id}") + logger.debug(f"{cached_pred}") + + # Explicitly pass required fields to ParagraphPredictionPublic + return ParagraphPredictionPublic( + id=paragraph.id, + text=paragraph.text, + hash=paragraph.hash, + index=paragraph.index, + created_at=paragraph.created_at, + updated_at=paragraph.updated_at, + prediction=PredictionPublic( + **cached_pred.model_dump(), + model=cached_pred.model.model_dump(), + ), + ) + + # ——————— Run prediction ————————————————————————————————————————————————————————— + logger.info("Running prediction") + item = [{"path": "empty", "data": {"doc.text": input_text}}] + + with prediction_lock: + processed = pipeline.preprocess(item) + processed = pipeline.predict_single(processed[0]) + processed = pipeline.postprocess([processed]) + + output_text = get_element(processed[0], ["data", "doc.text"]) or "" + output_labels = get_element(processed[0], ["predictions", "entities"]) or [] + + # ———————— Sanity check —————————————————————————————————————————————————————————— + if input_text != output_text: + logger.critical( + f"Input and output text do not match: {input_text} != {output_text}" + ) + if settings.ERROR_HANDLER == "raise": + raise ValueError("Input and output text do not match") + + # Run validation on the output labels + output_labels = [ + DocLabel(**label).model_dump(mode="json") for label in output_labels + ] + + # ——————— Save to cache —————————————————————————————————————————————————————————— + if pred := cached_pred: + logger.info(f"saving in cache: {pred.id}") + + logger.warning(f"Prediction already exists: {pred}. Updating.") + pred.input = output_text + pred.prediction = output_labels + else: + pred = PredictionCreate( + input=output_text, + prediction=output_labels, + validation=None, + fk_model=model.id, + fk_paragraph=paragraph.id, + ).compile() + + session.add(pred) + session.commit() + session.refresh(pred) + + return ParagraphPredictionPublic( + id=paragraph.id, + text=paragraph.text, + index=paragraph.index, + hash=paragraph.hash, + created_at=paragraph.created_at, + updated_at=paragraph.updated_at, + prediction=PredictionPublic( + **pred.model_dump(), + model=pred.model.model_dump(), + ), + ) + + +# MARK: Paragraph Validate +@router.patch("/pipeline/{pipeline_type}/paragraph/{paragraph_id}/validate") +async def save_paragraph_validation( + paragraph_id: UUID4, + pipeline_type: ModelType, + labels: list[DocLabel], + session: Session = Depends(get_session), +) -> None: + paragraph = session.get(Paragraph, paragraph_id) + + model = read_latest_model_by_type(model_type=pipeline_type, session=session) + + # ———————— Sanity check —————————————————————————————————————————————————————————— + # Check if the document exists + if not paragraph: + raise HTTPException( + status_code=404, + detail=f"Paragraph not found: {paragraph_id}", + ) + + # ——————— Updating validations ——————————————————————————————————————————————————— + prediction = read_prediction_by_text( + text=paragraph.text, model_id=model.id, session=session + ) + if prediction: + prediction.validation = labels + else: + logger.warning("Prediction not found.") + if settings.ERROR_HANDLER == "raise": + raise ValueError(f"Prediction not found for paragraph: `{paragraph.id}`") + prediction = Prediction( + input=paragraph.text, + fk_model=model.id, + validation=labels, + ) + + session.add(prediction) + session.commit() + + return diff --git a/aymurai/api/exceptions.py b/aymurai/api/exceptions.py deleted file mode 100644 index de6e5ed..0000000 --- a/aymurai/api/exceptions.py +++ /dev/null @@ -1,16 +0,0 @@ -from fastapi import HTTPException - - -class AymuraiAPIException(HTTPException): - status_code: int - title: str - - def __init__(self, status_code: int = None, detail: str = None): - self.status_code = status_code or self.status_code - self.detail = f"{self.title}: {detail}" if detail else self.title - super().__init__(status_code=self.status_code, detail=detail) - - -class UnsupportedFileType(AymuraiAPIException): - status_code = 400 - title = "Unsupported file type" diff --git a/aymurai/api/main.py b/aymurai/api/main.py index 28f2b34..800b208 100644 --- a/aymurai/api/main.py +++ b/aymurai/api/main.py @@ -6,24 +6,21 @@ from alembic import command from alembic.config import Config from fastapi import FastAPI, Request -from fastapi.responses import RedirectResponse from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import RedirectResponse from aymurai.api import core +from aymurai.api.startup.database import check_db_connection +from aymurai.database.crud.model import ModelType, register_model +from aymurai.database.session import get_session from aymurai.logger import get_logger -from aymurai.settings import settings from aymurai.pipeline import AymurAIPipeline -from aymurai.api.startup.database import check_db_connection - -try: - from aymurai.version import __version__ -except ImportError: - __version__ = "0.0.0" +from aymurai.settings import Settings, settings logger = get_logger(__name__) -torch.set_num_threads = 100 # FIXME: polemic ? +torch.set_num_threads(100) # FIXME: polemic ? RESOURCES_BASEPATH = settings.RESOURCES_BASEPATH @@ -31,21 +28,49 @@ @asynccontextmanager async def lifespan(app: FastAPI): logger.info("> Initializing service") + + # ------ Check DB connection and run migrations ---------------------------------- logger.info(f">> Checking DB connection: `{settings.SQLALCHEMY_DATABASE_URI}`") try: check_db_connection() logger.info(">> Running Alembic migrations") - alembic_cfg = Config(str(settings.ALEMBIC_INI_PATH)) + alembic_cfg = Config(str(settings.ALEMBIC_CONFIG)) command.upgrade(alembic_cfg, "head") except Exception as error: - logger.error("Error while starting up:", error) + logger.exception(f"Error while starting up: {error}") + + # ------ Register pipelines ------------------------------------------------------ + logger.info(">> Registering models") + session = next(get_session()) + PIPELINES_PATH = os.path.join(RESOURCES_BASEPATH, "pipelines", "production") + model = register_model( + model_name="flair-anonymizer", + model_type=ModelType.ANONYMIZATION, + app_version=settings.APP_VERSION, + pipeline_path=os.path.join(PIPELINES_PATH, "flair-anonymizer"), + session=session, + ) + logger.info( + f">>> Registering `{model.type}` model: {model.name} (version: {model.version})" + ) + + model = register_model( + model_name="datapublic", + model_type=ModelType.DATAPUBLIC, + app_version=settings.APP_VERSION, + pipeline_path=os.path.join(PIPELINES_PATH, "full-paragraph"), + session=session, + ) + logger.info( + f">>> Registering `{model.type}` model: {model.name} (version: {model.version})" + ) yield api = FastAPI( title="AymurAI API", - version=__version__, + version=settings.APP_VERSION, lifespan=lifespan, ) @@ -60,7 +85,7 @@ async def lifespan(app: FastAPI): ) -logger.info("Loading server ...") +logger.info(f"Loading server (Aymurai API - {settings.APP_VERSION})...") logger.info(f"CORS_ORIGINS: {settings.CORS_ORIGINS}") @@ -75,16 +100,7 @@ async def add_process_time_header(request: Request, call_next): @api.get("/", response_class=RedirectResponse, include_in_schema=False) async def index(): - return "/docs" - - -# @api.get("/docs", include_in_schema=False) -# async def custom_swagger_ui_html(): -# return get_swagger_ui_html( -# openapi_url=api.openapi_url, -# title=f"{api.title} - Swagger UI", -# swagger_css_url="https://cdn.jsdelivr.net/gh/danielperezrubio/swagger-dark-theme@main/assets/swagger-ui.min.css", -# ) + return RedirectResponse(url="/docs") ################################################################################ @@ -98,6 +114,20 @@ def healthcheck(): return {"status": "ok"} +# API settings +if settings.DEVELOPMENT_MODE: + logger.warning("Development mode enabled") + + @api.get( + "/server/environment", + status_code=200, + tags=["server"], + response_model=Settings, + ) + def environment(): + return settings.model_dump() + + # Api endpoints api.include_router(core.router) @@ -105,9 +135,6 @@ def healthcheck(): if __name__ == "__main__": # download the necessary data logger.info("Loading pipelines and exit.") - AymurAIPipeline.load( - os.path.join(RESOURCES_BASEPATH, "pipelines", "production", "doc-extraction") - ) AymurAIPipeline.load( os.path.join(RESOURCES_BASEPATH, "pipelines", "production", "flair-anonymizer") ) diff --git a/aymurai/api/startup/database.py b/aymurai/api/startup/database.py index 50ec7e3..78b3c63 100644 --- a/aymurai/api/startup/database.py +++ b/aymurai/api/startup/database.py @@ -1,11 +1,11 @@ -import os import logging +import os from sqlmodel import select -from tenacity import retry, after_log, before_log, wait_fixed, stop_after_attempt +from tenacity import after_log, before_log, retry, stop_after_attempt, wait_fixed -from aymurai.settings import settings from aymurai.database.session import get_session +from aymurai.settings import settings logger = logging.getLogger(__name__) diff --git a/aymurai/database/crud/anonymization/__init__.py b/aymurai/database/crud/anonymization/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/aymurai/database/crud/anonymization/document.py b/aymurai/database/crud/anonymization/document.py deleted file mode 100644 index dc56384..0000000 --- a/aymurai/database/crud/anonymization/document.py +++ /dev/null @@ -1,79 +0,0 @@ -import uuid - -from sqlmodel import Session, select - -from aymurai.logger import get_logger -from aymurai.database.schema import ( - AnonymizationDocument, - AnonymizationParagraph, - AnonymizationDocumentUpdate, -) - -logger = get_logger(__name__) - - -def anonymization_document_create( - id: uuid.UUID, - name: str, - paragraphs: list[AnonymizationParagraph], - session: Session, - override: bool = False, -) -> AnonymizationDocument: - document = AnonymizationDocument(id=id, name=name, paragraphs=paragraphs) - - exists = session.get(AnonymizationDocument, id) - if exists and override: - session.delete(exists) - if exists and not override: - return exists - - session.add(document) - session.commit() - session.refresh(document) - - return document - - -def anonymization_document_read( - document_id: uuid.UUID, - session: Session, -) -> AnonymizationDocument | None: - statement = select(AnonymizationDocument).where( - AnonymizationDocument.id == document_id - ) - data = session.exec(statement).first() - return data - - -def anonymization_document_update( - document_id: uuid.UUID, - document_in: AnonymizationDocumentUpdate, - session: Session, -) -> AnonymizationDocument: - statement = select(AnonymizationDocument).where( - AnonymizationDocument.id == document_id - ) - document = session.exec(statement).first() - - if not document: - raise ValueError(f"Document not found: {document_id}") - - for field, value in document_in.model_dump(exclude_unset=True).items(): - setattr(document, field, value) - - return anonymization_document_create(document, session) - - -def anonymization_document_delete(document_id: uuid.UUID, session: Session): - statement = select(AnonymizationDocument).where( - AnonymizationDocument.id == document_id - ) - document = session.exec(statement).first() - - if not document: - raise ValueError(f"Document not found: {document_id}") - - session.delete(document) - session.commit() - - return diff --git a/aymurai/database/crud/anonymization/paragraph.py b/aymurai/database/crud/anonymization/paragraph.py deleted file mode 100644 index 593ebbd..0000000 --- a/aymurai/database/crud/anonymization/paragraph.py +++ /dev/null @@ -1,101 +0,0 @@ -import uuid - -from sqlmodel import Session - -from aymurai.database.schema import ( - AnonymizationParagraph, - AnonymizationParagraphCreate, - AnonymizationParagraphUpdate, -) -from aymurai.database.utils import text_to_uuid -from aymurai.logger import get_logger - -logger = get_logger(__name__) - - -def anonymization_paragraph_create( - paragraph_in: AnonymizationParagraphCreate, - session: Session, - override: bool = False, -) -> AnonymizationParagraph: - new_paragraph = AnonymizationParagraph(**paragraph_in.model_dump()) - - if override: - existing = session.get(AnonymizationParagraph, new_paragraph.id) - - if existing: - session.delete(existing) - - session.add(new_paragraph) - session.commit() - session.refresh(new_paragraph) - return new_paragraph - - -def anonymization_paragraph_read( - paragraph_id: uuid.UUID, - session: Session, -) -> AnonymizationParagraph | None: - return session.get(AnonymizationParagraph, paragraph_id) - - -def anonymization_paragraph_update( - paragraph_id: uuid.UUID, - paragraph_in: AnonymizationParagraphUpdate, - session: Session, -) -> AnonymizationParagraph: - paragraph = session.get(AnonymizationParagraph, paragraph_id) - - if not paragraph: - raise ValueError(f"Paragraph not found: {paragraph_id}") - - for field, value in paragraph_in.model_dump(exclude_none=True).items(): - setattr(paragraph, field, value) - - session.add(paragraph) - session.commit() - session.refresh(paragraph) - return paragraph - - -def anonymization_paragraph_delete(paragraph_id: uuid.UUID, session: Session): - paragraph = session.get(AnonymizationParagraph, paragraph_id) - - if not paragraph: - raise ValueError(f"Paragraph not found: {paragraph_id}") - - session.delete(paragraph) - session.commit() - - return - - -# FIXME: This can be CLEARLY optimized -def anonymization_paragraph_batch_create_update( - paragraphs_in: list[AnonymizationParagraphCreate], session: Session -) -> list[AnonymizationParagraph]: - paragraphs = [] - - for p_in in paragraphs_in: - paragraph_id = text_to_uuid(p_in.text) - - paragraph = session.get(AnonymizationParagraph, paragraph_id) - if paragraph: - update = AnonymizationParagraphUpdate(**p_in.model_dump()) - - for field, value in update.model_dump(exclude_none=True).items(): - setattr(paragraph, field, value) - - else: - paragraph = AnonymizationParagraph(**p_in.model_dump()) - - session.add(paragraph) - session.commit() - session.refresh(paragraph) - - paragraphs.append(paragraph) - - # refresh models (Must be a list or for-loop) - [session.refresh(paragraph) for paragraph in paragraphs] - - return paragraphs diff --git a/aymurai/database/crud/datapublic/__init__.py b/aymurai/database/crud/datapublic/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/aymurai/database/crud/datapublic/document.py b/aymurai/database/crud/datapublic/document.py deleted file mode 100644 index 293a163..0000000 --- a/aymurai/database/crud/datapublic/document.py +++ /dev/null @@ -1,76 +0,0 @@ -import uuid - -from sqlmodel import Session, select - -from aymurai.logger import get_logger -from aymurai.database.schema import ( - DataPublicDocument, - DataPublicParagraph, - DataPublicDocumentUpdate, -) - -logger = get_logger(__name__) - - -def datapublic_document_create( - id: uuid.UUID, - name: str, - paragraphs: list[DataPublicParagraph], - session: Session, - override: bool = False, -) -> DataPublicDocument: - document = DataPublicDocument(id=id, name=name, paragraphs=paragraphs) - - if override: - statement = select(DataPublicDocument).where( - DataPublicDocument.id == document.id - ) - existing = session.exec(statement).first() - - if existing: - session.delete(existing) - - session.add(document) - session.commit() - session.refresh(document) - - return document - - -def datapublic_document_read( - document_id: uuid.UUID, - session: Session, -) -> DataPublicDocument | None: - statement = select(DataPublicDocument).where(DataPublicDocument.id == document_id) - data = session.exec(statement).first() - return data - - -def datapublic_document_update( - document_id: uuid.UUID, - document_in: DataPublicDocumentUpdate, - session: Session, -) -> DataPublicDocument: - statement = select(DataPublicDocument).where(DataPublicDocument.id == document_id) - document = session.exec(statement).first() - - if not document: - raise ValueError(f"Document not found: {document_id}") - - for field, value in document_in.model_dump(exclude_unset=True).items(): - setattr(document, field, value) - - return datapublic_document_create(document, session) - - -def datapublic_document_delete(document_id: uuid.UUID, session: Session): - statement = select(DataPublicDocument).where(DataPublicDocument.id == document_id) - document = session.exec(statement).first() - - if not document: - raise ValueError(f"Document not found: {document_id}") - - session.delete(document) - session.commit() - - return diff --git a/aymurai/database/crud/datapublic/paragraph.py b/aymurai/database/crud/datapublic/paragraph.py deleted file mode 100644 index 591334a..0000000 --- a/aymurai/database/crud/datapublic/paragraph.py +++ /dev/null @@ -1,112 +0,0 @@ -import uuid - -from sqlmodel import Session, select - -from aymurai.database.schema import ( - DataPublicParagraph, - DataPublicParagraphCreate, - DataPublicParagraphUpdate, -) -from aymurai.database.utils import text_to_uuid -from aymurai.logger import get_logger - -logger = get_logger(__name__) - - -def datapublic_paragraph_create( - paragraph_in: DataPublicParagraphCreate, - session: Session, - override: bool = False, -) -> DataPublicParagraph: - paragraph = DataPublicParagraph(**paragraph_in.model_dump()) - - if override: - statement = select(DataPublicParagraph).where( - DataPublicParagraph.id == paragraph.id - ) - existing = session.exec(statement).first() or paragraph - - if existing: - session.delete(existing) - - session.add(paragraph) - session.commit() - session.refresh(paragraph) - return paragraph - - -def datapublic_paragraph_read( - paragraph_id: uuid.UUID, - session: Session, -) -> DataPublicParagraph | None: - statement = select(DataPublicParagraph).where( - DataPublicParagraph.id == paragraph_id - ) - data = session.exec(statement).first() - return data - - -def datapublic_paragraph_update( - paragraph_id: uuid.UUID, - paragraph_in: DataPublicParagraphUpdate, - session: Session, -) -> DataPublicParagraph: - statement = select(DataPublicParagraph).where( - DataPublicParagraph.id == paragraph_id - ) - paragraph = session.exec(statement).first() - - if not paragraph: - raise ValueError(f"Paragraph not found: {paragraph_id}") - - for field, value in paragraph_in.model_dump(exclude_none=True).items(): - setattr(paragraph, field, value) - - return datapublic_paragraph_create(paragraph, session) - - -def datapublic_paragraph_delete(paragraph_id: uuid.UUID, session: Session): - statement = select(DataPublicParagraph).where( - DataPublicParagraph.id == paragraph_id - ) - paragraph = session.exec(statement).first() - - if not paragraph: - raise ValueError(f"Paragraph not found: {paragraph_id}") - - session.delete(paragraph) - session.commit() - - return - - -def datapublic_paragraph_batch_create_update( - paragraphs_in: list[DataPublicParagraphCreate], session: Session -) -> list[DataPublicParagraph]: - paragraphs = [] - - for paragraph_in in paragraphs_in: - paragraph_id = text_to_uuid(paragraph_in.text) - - statement = select(DataPublicParagraph).where( - DataPublicParagraph.id == paragraph_id - ) - paragraph = session.exec(statement).first() - if paragraph: - update = DataPublicParagraphUpdate(**paragraph_in.model_dump()) - - for field, value in update.model_dump(exclude_none=True).items(): - setattr(paragraph, field, value) - - else: - paragraph = DataPublicParagraph(**paragraph_in.model_dump()) - - paragraphs.append(paragraph) - - session.add(paragraph) - session.commit() - - # refresh models (Must be a list or for-loop) - [session.refresh(paragraph) for paragraph in paragraphs] - - return paragraphs diff --git a/aymurai/database/crud/document-bkp.py b/aymurai/database/crud/document-bkp.py new file mode 100644 index 0000000..cc97a1c --- /dev/null +++ b/aymurai/database/crud/document-bkp.py @@ -0,0 +1,71 @@ +import uuid + +from sqlmodel import Session, select + +from aymurai.logger import get_logger +from aymurai.database.schema import ( + Document, + Paragraph, + DocumentUpdate, +) + +logger = get_logger(__name__) + + +def document_create( + id: uuid.UUID, + name: str, + paragraphs: list[Paragraph], + session: Session, +) -> Document: + document = Document(id=id, name=name, paragraphs=paragraphs) + + exists = session.get(Document, id) + if exists: + logger.warning(f"Document already exists: {id}. skipping creation.") + return exists + + session.add(document) + session.commit() + session.refresh(document) + + return document + + +def document_read( + document_id: uuid.UUID, + session: Session, +) -> Document | None: + statement = select(Document).where(Document.id == document_id) + data = session.exec(statement).first() + return data + + +def document_update( + document_id: uuid.UUID, + document_in: DocumentUpdate, + session: Session, +) -> Document: + statement = select(Document).where(Document.id == document_id) + document = session.exec(statement).first() + + if not document: + raise ValueError(f"Document not found: {document_id}") + + for field, value in document_in.model_dump(exclude_unset=True).items(): + setattr(document, field, value) + + return document_create(document, session) + + +def document_delete(document_id: uuid.UUID, session: Session): + statement = select(Document).where(Document.id == document_id) + document = session.exec(statement).first() + + if not document: + raise ValueError(f"Document not found: {document_id}") + + session.delete(document) + session.commit() + + return diff --git a/aymurai/database/crud/document.py b/aymurai/database/crud/document.py new file mode 100644 index 0000000..cc97a1c --- /dev/null +++ b/aymurai/database/crud/document.py @@ -0,0 +1,71 @@ +import uuid + +from sqlmodel import Session, select + +from aymurai.logger import get_logger +from aymurai.database.schema import ( + Document, + Paragraph, + DocumentUpdate, +) + +logger = get_logger(__name__) + + +def document_create( + id: uuid.UUID, + name: str, + paragraphs: list[Paragraph], + session: Session, +) -> Document: + document = Document(id=id, name=name, paragraphs=paragraphs) + + exists = session.get(Document, id) + if exists: + logger.warning(f"Document already exists: {id}. skipping creation.") + return exists + + session.add(document) + session.commit() + session.refresh(document) + + return document + + +def document_read( + document_id: uuid.UUID, + session: Session, +) -> Document | None: + statement = select(Document).where(Document.id == document_id) + data = session.exec(statement).first() + return data + + +def document_update( + document_id: uuid.UUID, + document_in: DocumentUpdate, + session: Session, +) -> Document: + statement = select(Document).where(Document.id == document_id) + document = session.exec(statement).first() + + if not document: + raise ValueError(f"Document not found: {document_id}") + + for field, value in document_in.model_dump(exclude_unset=True).items(): + setattr(document, field, value) + + return document_create(document, session) + + +def document_delete(document_id: uuid.UUID, session: Session): + statement = select(Document).where(Document.id == document_id) + document = session.exec(statement).first() + + if not document: + raise ValueError(f"Document not found: {document_id}") + + session.delete(document) + session.commit() + + return diff --git a/aymurai/database/crud/model.py b/aymurai/database/crud/model.py new file mode 100644 index 0000000..8a40455 --- /dev/null +++ b/aymurai/database/crud/model.py @@ -0,0 +1,53 @@ +from sqlmodel import Session, desc, select + +from aymurai.database.schema import Model, ModelCreate, ModelPublic, ModelType +from aymurai.database.utils import text_to_uuid +from aymurai.logger import get_logger + +logger = get_logger(__name__) + + +def register_model( + model_name: str, + app_version: str, + model_type: ModelType, + pipeline_path: str, + session: Session, +) -> ModelPublic: + """ + Register a model in the database. + """ + model_id = text_to_uuid(f"{model_type}-{model_name}-{app_version}") + + # Check if the model already exists + model = session.get(Model, model_id) + if model: + logger.warning(f"Model already exists: {model_id}. skipping creation.") + return ModelPublic(**model.model_dump()) + + model = ModelCreate( + id=model_id, + name=model_name, + version=app_version, + type=model_type, + pipeline_path=pipeline_path, + ).compile() + + session.add(model) + session.commit() + session.refresh(model) + + return ModelPublic(**model.model_dump()) + + +def read_latest_model_by_type(model_type: ModelType, session: Session) -> ModelPublic: + """ + Read a model from the database by its type. + """ + stm = select(Model).where(Model.type == model_type).order_by(desc(Model.created_at)) + model = session.exec(stm).first() + + if not model: + raise ValueError(f"Model not found for type: {model_type}") + + return ModelPublic(**model.model_dump()) diff --git a/aymurai/database/crud/prediction.py b/aymurai/database/crud/prediction.py new file mode 100644 index 0000000..ac7b424 --- /dev/null +++ b/aymurai/database/crud/prediction.py @@ -0,0 +1,150 @@ +import uuid +from sqlmodel import Session, select, func +from sqlalchemy.orm import aliased + +from aymurai.database.schema import ( + Prediction, + Model, + Paragraph, + PredictionPublic, + ModelType, +) +from aymurai.database.meta.extra import ( + ParagraphPredictionPublic, +) +from aymurai.database.utils import text_to_hash + + +def read_validation( + text: str, + model_name: str, + session: Session, +) -> Prediction | None: + """ + Read the validation labels for a given input hash. + + Args: + input_hash (str): The hash of the input text. + model_name (str): The name of the model associated with the prediction. + session (Session): Database session dependency. + + Returns: + list[DocLabel] | None: A list of validation labels for the given input hash, or None if no validation exists. + """ + + input_hash = text_to_hash(text) + + statement = ( + select(Prediction) + .join(Model, Prediction.fk_model == Model.id) + .where( + Prediction.input_hash == input_hash, + Model.name == model_name, + Prediction.validation.isnot(None), + ) + .order_by(Prediction.updated_at.desc(), Prediction.created_at.desc()) + ) + pred = session.exec(statement).first() + + return pred or None + + +def read_prediction_by_text( + text: str, + model_id: uuid.UUID, + session: Session, +) -> Prediction | None: + """ + Read the validation labels for a given input hash. + + Args: + input_hash (str): The hash of the input text. + model_name (str): The name of the model associated with the prediction. + session (Session): Database session dependency. + + Returns: + list[DocLabel] | None: A list of validation labels for the given input hash, or None if no validation exists. + """ + + input_hash = text_to_hash(text) + + statement = ( + select(Prediction) + .where( + Prediction.input_hash == input_hash, + Prediction.fk_model == model_id, + # Prediction.prediction.isnot(None), + ) + .order_by(Prediction.updated_at.desc(), Prediction.created_at.desc()) + ) + pred = session.exec(statement).first() + + return pred or None + + +def get_paragraph_prediction_public( + paragraph_pred: tuple[Paragraph, PredictionPublic | None], +) -> ParagraphPredictionPublic: + paragraph, pred = paragraph_pred + + if pred is None: + return ParagraphPredictionPublic(**paragraph.model_dump(), prediction=None) + + obj = ParagraphPredictionPublic( + **paragraph.model_dump(), + prediction=PredictionPublic( + **pred.model_dump(), + model=pred.model.model_dump(), + ), + ) + + return obj + + +def read_document_prediction_paragraphs( + document_id: uuid.UUID, + model_type: ModelType, + session: Session, +) -> list[ParagraphPredictionPublic]: + """ + For each paragraph in `document_id` (ordered by Paragraph.index) + and for the given `model_type`, return the most‐recent Prediction or None. + """ + + # 1) build a row_number window over each paragraph + rn = ( + func.row_number() + .over( + partition_by=Prediction.fk_paragraph, + order_by=(Prediction.updated_at.desc(), Prediction.created_at.desc()), + ) + .label("rn") + ) + + # 2) subquery: join to Model by type & compute rn + pred_subq = ( + select(Prediction, rn) + .join(Model, Prediction.fk_model == Model.id) + .where(Model.type == model_type) + ).subquery() + + # alias the subquery as a full ORM-mapped Prediction + LatestPred = aliased(Prediction, pred_subq) + + # 3) outer-join Paragraph → the aliased Prediction, filter rn==1, order by paragraph.index + stmt = ( + select(Paragraph, LatestPred) + .join( + LatestPred, + Paragraph.id == LatestPred.fk_paragraph, + isouter=True, + ) + .where(Paragraph.fk_document == document_id) + .where((pred_subq.c.rn == 1) | (pred_subq.c.rn.is_(None))) + .order_by(Paragraph.index) + ) + + rows = session.exec(stmt).all() + # rows is list of (Paragraph, LatestPred or None) + + return [get_paragraph_prediction_public(row) for row in rows] diff --git a/aymurai/database/meta/anonymization/__init__.py b/aymurai/database/meta/anonymization/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/aymurai/database/meta/anonymization/document.py b/aymurai/database/meta/anonymization/document.py deleted file mode 100644 index d8d4b2d..0000000 --- a/aymurai/database/meta/anonymization/document.py +++ /dev/null @@ -1,45 +0,0 @@ -import uuid -from datetime import datetime -from typing import TYPE_CHECKING - -from pydantic import BaseModel -from sqlmodel import Field, SQLModel, Relationship -from sqlalchemy import Column, DateTime, func, text - -from aymurai.database.meta.anonymization.document_paragraph import ( - AnonymizationDocumentParagraph, -) - -if TYPE_CHECKING: - from aymurai.database.meta.anonymization.paragraph import AnonymizationParagraph - - -class AnonymizationDocument(SQLModel, table=True): - __tablename__ = "anonymization_document" - id: uuid.UUID | None = Field(None, primary_key=True) - created_at: datetime = Field( - sa_column_kwargs={"server_default": text("CURRENT_TIMESTAMP")} - ) - updated_at: datetime | None = Field( - sa_column=Column(DateTime(), onupdate=func.now()) - ) - - name: str = Field(nullable=False) - paragraphs: list["AnonymizationParagraph"] = Relationship( - back_populates="documents", link_model=AnonymizationDocumentParagraph - ) - - -class AnonymizationDocumentCreate(BaseModel): - name: str - # paragraphs: list["AnonymizationParagraph"] - - -class AnonymizationDocumentUpdate(BaseModel): - name: str | None = None - - -class AnonymizationDocumentRead(BaseModel): - id: uuid.UUID - name: str - # paragraphs: list[uuid.UUID] | None diff --git a/aymurai/database/meta/anonymization/document_paragraph.py b/aymurai/database/meta/anonymization/document_paragraph.py deleted file mode 100644 index c25eca1..0000000 --- a/aymurai/database/meta/anonymization/document_paragraph.py +++ /dev/null @@ -1,21 +0,0 @@ -import uuid - -from sqlmodel import Field, SQLModel - - -class AnonymizationDocumentParagraph(SQLModel, table=True): - __tablename__ = "anonymization_document_paragraph" - # NOTE: The id is required to allow repition of document_id and paragraph_id - # (the paragraph is repeated multiple times in the document) - id: uuid.UUID | None = Field(default_factory=uuid.uuid4, primary_key=True) - document_id: uuid.UUID | None = Field( - None, - foreign_key="anonymization_document.id", - primary_key=True, - ) - paragraph_id: uuid.UUID | None = Field( - None, - foreign_key="anonymization_paragraph.id", - primary_key=True, - ) - order: int | None = Field(nullable=True) diff --git a/aymurai/database/meta/anonymization/paragraph.py b/aymurai/database/meta/anonymization/paragraph.py deleted file mode 100644 index 9a6e29b..0000000 --- a/aymurai/database/meta/anonymization/paragraph.py +++ /dev/null @@ -1,67 +0,0 @@ -import uuid -from datetime import datetime - -from pydantic import BaseModel, model_validator -from typing_extensions import TYPE_CHECKING, Self -from sqlmodel import Field, SQLModel, Relationship -from sqlalchemy import JSON, Column, DateTime, func, text - -from aymurai.database.utils import text_to_uuid -from aymurai.meta.api_interfaces import DocLabel - -if TYPE_CHECKING: - from aymurai.database.meta.anonymization.document import AnonymizationDocument - -from aymurai.database.meta.anonymization.document_paragraph import ( - AnonymizationDocumentParagraph, -) - - -class AnonymizationParagraphBase(SQLModel): - id: uuid.UUID | None = Field(None, primary_key=True) - - text: str = Field(nullable=False) - prediction: list[DocLabel] | None = Field(None, sa_column=Column(JSON)) - validation: list[DocLabel] | None = Field(None, sa_column=Column(JSON)) - - @model_validator(mode="after") - def validate_text(self) -> Self: - self.id = text_to_uuid(self.text) - return self - - -class AnonymizationParagraph(AnonymizationParagraphBase, table=True): - __tablename__ = "anonymization_paragraph" - - # NOTE: SQLModel with table=True does not run validators - id: uuid.UUID | None = Field(None, primary_key=True) - created_at: datetime = Field( - sa_column_kwargs={"server_default": text("CURRENT_TIMESTAMP")} - ) - updated_at: datetime | None = Field( - sa_column=Column(DateTime(), onupdate=func.now()) - ) - documents: list["AnonymizationDocument"] = Relationship( - back_populates="paragraphs", - link_model=AnonymizationDocumentParagraph, - ) - - -class AnonymizationParagraphCreate(AnonymizationParagraphBase): - pass - - -class AnonymizationParagraphUpdate(BaseModel): - prediction: list[DocLabel] | None = None - validation: list[DocLabel] | None = None - - -class AnonymizationParagraphRead(BaseModel): - id: uuid.UUID - - text: str - prediction: list[DocLabel] | None - validation: list[DocLabel] | None - - created_at: datetime - updated_at: datetime | None diff --git a/aymurai/database/meta/datapublic.py b/aymurai/database/meta/datapublic.py new file mode 100644 index 0000000..4b01d60 --- /dev/null +++ b/aymurai/database/meta/datapublic.py @@ -0,0 +1,44 @@ +import enum +import uuid +from datetime import datetime + +from pydantic import BaseModel, ConfigDict +from sqlalchemy import JSON, Column, DateTime, func, text +from sqlmodel import Field, Relationship, SQLModel + +from aymurai.database.meta.document import Document + + +class DatapublicFormat(str, enum.Enum): + NONE = "none" + + +class Datapublic(SQLModel, table=True): + __tablename__ = "datapublic" + + id: uuid.UUID | None = Field(default_factory=uuid.uuid4, primary_key=True) + + created_at: datetime = Field( + sa_column_kwargs={"server_default": text("CURRENT_TIMESTAMP")} + ) + updated_at: datetime | None = Field( + sa_column=Column(DateTime(), onupdate=func.now()) + ) + + validation_format: DatapublicFormat = DatapublicFormat.NONE + + records: dict = Field(sa_column=Column(JSON)) + + fk_document: uuid.UUID | None = Field(foreign_key="document.id") + document: Document | None = Relationship(back_populates="datapublic") + + +class DatapublicPublic(BaseModel): + model_config = ConfigDict(from_attributes=True) + + id: uuid.UUID + fk_document: uuid.UUID + created_at: datetime + updated_at: datetime | None = None + validation_format: DatapublicFormat = DatapublicFormat.NONE + records: dict = {} diff --git a/aymurai/database/meta/datapublic/__init__.py b/aymurai/database/meta/datapublic/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/aymurai/database/meta/datapublic/categories.py b/aymurai/database/meta/datapublic/categories.py deleted file mode 100644 index 8a540b4..0000000 --- a/aymurai/database/meta/datapublic/categories.py +++ /dev/null @@ -1,58 +0,0 @@ -from enum import Enum - - -class Materia(str, Enum): - PENAL = "penal" - CONTRAVENCIONAL = "contravencional" - FALTAS = "faltas" - AMPARO = "amparo" - HABEAS_CORPUS = "habeas corpus" - EJECUCIONES_DE_MULTA = "ejecuciones de multa" - - -class Violencia(str, Enum): - SI = "si" - NO = "no" - SD = "s/d" - NO_CORRESPONDE = "no_corresponde" - - -class ModalidadViolencia(str, Enum): - DOMESTICA = "domestica" - INSTITUCIONAL = "institucional" - MEDIATICA = "mediatica" - LABORAL = "laboral" - CONTRA_LIBERTAD_REPRODUCTIVA = "contra la libertad reproductiva" - OBSTETRICA = "obstetrica" - ESPACIO_PUBLICO_PRIVADO = "en espacio publico o privado" - POLITICA_PUBLICA = "politica y publica" - DIGITAL = "digital" - - -class Genero(str, Enum): - VARON_CIS = "varon_cis" - MUJER_CIS = "mujer_cis" - MUJER_TRANS = "mujer_trans" - TRAVESTI = "travesti" - VARON_TRANS = "varon_trans" - NO_BINARIA = "no_binaria" - NO_CORRESPONDE = "no_corresponde" - NO_RESPONDE = "no_responde" - - -class FrecuenciaEpisodios(str, Enum): - ESPORADICO = "esporádico" - DIARIO = "diario" - HABITUAL = "habitual" - EVENTUAL = "eventual" - PRIMERA_AGRESION = "primera agresión" - - -class TipoResolucion(str, Enum): - INTERLOCUTORIA = "interlocutoria" - DEFINITIVA = "definitiva" - - -class OralEscrita(str, Enum): - ORAL = "oral" - ESCRITA = "escrita" diff --git a/aymurai/database/meta/datapublic/document.py b/aymurai/database/meta/datapublic/document.py deleted file mode 100644 index 5f0ac16..0000000 --- a/aymurai/database/meta/datapublic/document.py +++ /dev/null @@ -1,54 +0,0 @@ -import uuid -from datetime import datetime - -from typing import TYPE_CHECKING -from sqlmodel import Field, SQLModel, Relationship -from sqlalchemy import Column, DateTime, func, text, JSON -from aymurai.meta.api_interfaces import DataPublicDocumentAnnotations - -from aymurai.database.meta.datapublic.document_paragraph import ( - DataPublicDocumentParagraph, -) - -if TYPE_CHECKING: - from aymurai.database.meta.datapublic.paragraph import DataPublicParagraph - - -class DataPublicDocumentBase(SQLModel): - prediction: DataPublicDocumentAnnotations | None = Field( - None, sa_column=Column(JSON) - ) - validation: DataPublicDocumentAnnotations | None = Field( - None, sa_column=Column(JSON) - ) - - -class DataPublicDocument(DataPublicDocumentBase, table=True): - __tablename__ = "datapublic_document" - id: uuid.UUID | None = Field(None, primary_key=True) - created_at: datetime = Field( - sa_column_kwargs={"server_default": text("CURRENT_TIMESTAMP")} - ) - updated_at: datetime | None = Field( - sa_column=Column(DateTime(), onupdate=func.now()) - ) - - paragraphs: list["DataPublicParagraph"] = Relationship( - back_populates="documents", - link_model=DataPublicDocumentParagraph, - ) - - -class DataPublicDocumentCreate(DataPublicDocumentBase): - pass - - -class DataPublicDocumentUpdate(DataPublicDocumentBase): - prediction: DataPublicDocumentAnnotations | None = None - validation: DataPublicDocumentAnnotations | None = None - - -class DataPublicDocumentRead(DataPublicDocumentBase): - id: uuid.UUID - created_at: datetime - updated_at: datetime | None = None diff --git a/aymurai/database/meta/datapublic/document_paragraph.py b/aymurai/database/meta/datapublic/document_paragraph.py deleted file mode 100644 index b3d51b3..0000000 --- a/aymurai/database/meta/datapublic/document_paragraph.py +++ /dev/null @@ -1,21 +0,0 @@ -import uuid - -from sqlmodel import Field, SQLModel - - -class DataPublicDocumentParagraph(SQLModel, table=True): - __tablename__ = "datapublic_document_paragraph" - # NOTE: The id is required to allow repition of document_id and paragraph_id - # (the paragraph is repeated multiple times in the document) - id: uuid.UUID | None = Field(default_factory=uuid.uuid4, primary_key=True) - document_id: uuid.UUID | None = Field( - None, - foreign_key="datapublic_document.id", - primary_key=True, - ) - paragraph_id: uuid.UUID | None = Field( - None, - foreign_key="datapublic_paragraph.id", - primary_key=True, - ) - order: int | None = Field(nullable=True) diff --git a/aymurai/database/meta/datapublic/paragraph.py b/aymurai/database/meta/datapublic/paragraph.py deleted file mode 100644 index bd5edb2..0000000 --- a/aymurai/database/meta/datapublic/paragraph.py +++ /dev/null @@ -1,67 +0,0 @@ -import uuid -from datetime import datetime - -from pydantic import BaseModel, model_validator -from typing_extensions import TYPE_CHECKING, Self -from sqlmodel import Field, SQLModel, Relationship -from sqlalchemy import JSON, Column, DateTime, func, text - -from aymurai.database.utils import text_to_uuid -from aymurai.meta.api_interfaces import DocLabel - -if TYPE_CHECKING: - from aymurai.database.meta.datapublic.document import DataPublicDocument - -from aymurai.database.meta.datapublic.document_paragraph import ( - DataPublicDocumentParagraph, -) - - -class DataPublicParagraphBase(SQLModel): - id: uuid.UUID | None = Field(None, primary_key=True) - - text: str = Field(nullable=False) - prediction: list[DocLabel] | None = Field(None, sa_column=Column(JSON)) - validation: list[DocLabel] | None = Field(None, sa_column=Column(JSON)) - - @model_validator(mode="after") - def validate_text(self) -> Self: - self.id = text_to_uuid(self.text) - return self - - -class DataPublicParagraph(DataPublicParagraphBase, table=True): - __tablename__ = "datapublic_paragraph" - - # NOTE: SQLModel with table=True does not run validators - id: uuid.UUID | None = Field(None, primary_key=True) - created_at: datetime = Field( - sa_column_kwargs={"server_default": text("CURRENT_TIMESTAMP")} - ) - updated_at: datetime | None = Field( - sa_column=Column(DateTime(), onupdate=func.now()) - ) - documents: list["DataPublicDocument"] = Relationship( - back_populates="paragraphs", - link_model=DataPublicDocumentParagraph, - ) - - -class DataPublicParagraphCreate(DataPublicParagraphBase): - pass - - -class DataPublicParagraphUpdate(BaseModel): - prediction: list[DocLabel] | None = None - validation: list[DocLabel] | None = None - - -class DataPublicParagraphRead(BaseModel): - id: uuid.UUID - - text: str - prediction: list[DocLabel] | None - validation: list[DocLabel] | None - - created_at: datetime - updated_at: datetime | None diff --git a/aymurai/database/meta/document.py b/aymurai/database/meta/document.py new file mode 100644 index 0000000..ba6d578 --- /dev/null +++ b/aymurai/database/meta/document.py @@ -0,0 +1,45 @@ +import uuid +from datetime import datetime +from typing import TYPE_CHECKING + +from pydantic import BaseModel +from sqlalchemy import Column, DateTime, LargeBinary, func, text +from sqlalchemy.orm import deferred +from sqlmodel import Field, Relationship, SQLModel + +from aymurai.database.meta.paragraph import ParagraphPublic + +if TYPE_CHECKING: + from aymurai.database.meta.datapublic import Datapublic + from aymurai.database.meta.paragraph import Paragraph, ParagraphPublic + + +class Document(SQLModel, table=True): + __tablename__ = "document" + + id: uuid.UUID | None = Field(None, primary_key=True) + data: bytes = Field( + sa_column=deferred(Column(LargeBinary)), + description="binary data of the document", + ) + created_at: datetime = Field( + sa_column_kwargs={"server_default": text("CURRENT_TIMESTAMP")} + ) + updated_at: datetime | None = Field( + sa_column=Column(DateTime(), onupdate=func.now()) + ) + + name: str = Field(nullable=False) + + paragraphs: list["Paragraph"] = Relationship(back_populates="document") + datapublic: list["Datapublic"] = Relationship(back_populates="document") + + +class DocumentUpdate(BaseModel): + name: str | None = None + + +class DocumentPublic(BaseModel): + id: uuid.UUID + name: str + paragraphs: list[ParagraphPublic] | None = None diff --git a/aymurai/database/meta/extra.py b/aymurai/database/meta/extra.py new file mode 100644 index 0000000..a7da805 --- /dev/null +++ b/aymurai/database/meta/extra.py @@ -0,0 +1,9 @@ +from aymurai.database.schema import PredictionPublic, ParagraphPublic, DocumentPublic + + +class ParagraphPredictionPublic(ParagraphPublic): + prediction: PredictionPublic | None = None + + +class DocumentPredictionPublic(DocumentPublic): + paragraphs: list[ParagraphPredictionPublic] | None = None diff --git a/aymurai/database/meta/model.py b/aymurai/database/meta/model.py new file mode 100644 index 0000000..7869af1 --- /dev/null +++ b/aymurai/database/meta/model.py @@ -0,0 +1,52 @@ +import enum +import uuid +from datetime import datetime + +from pydantic import BaseModel +from sqlalchemy import text +from sqlmodel import Field, Relationship, SQLModel +from typing_extensions import TYPE_CHECKING + +if TYPE_CHECKING: + from aymurai.database.meta.prediction import Prediction + + +class ModelType(str, enum.Enum): + ANONYMIZATION = "anonymization" + DATAPUBLIC = "datapublic" + + +class ModelBase(SQLModel): + id: uuid.UUID | None = Field(default_factory=uuid.uuid4, primary_key=True) + + name: str = Field(nullable=False) + version: str = Field(nullable=False) + + type: ModelType = Field(nullable=False) + pipeline_path: str = Field(nullable=False) + + +class Model(ModelBase, table=True): + __tablename__ = "model" + + created_at: datetime = Field( + sa_column_kwargs={"server_default": text("CURRENT_TIMESTAMP")} + ) + + predictions: list["Prediction"] = Relationship(back_populates="model") + + +class ModelCreate(ModelBase): + def compile(self) -> Model: + return Model(**self.model_dump()) + + +class ModelPublic(BaseModel): + id: uuid.UUID + + type: ModelType + name: str + version: str + pipeline_path: str + + created_at: datetime diff --git a/aymurai/database/meta/paragraph.py b/aymurai/database/meta/paragraph.py new file mode 100644 index 0000000..8230d15 --- /dev/null +++ b/aymurai/database/meta/paragraph.py @@ -0,0 +1,68 @@ +import uuid +from datetime import datetime + +from pydantic import BaseModel, computed_field, model_validator +from sqlalchemy import Column, DateTime, func, text +from sqlmodel import Field, Relationship, SQLModel +from typing_extensions import TYPE_CHECKING, Self + +from aymurai.database.utils import text_to_uuid + +# from aymurai.database.schema import PredictionPublic + +if TYPE_CHECKING: + from aymurai.database.schema import Document, Prediction, PredictionPublic + + +class ParagraphBase(SQLModel): + id: uuid.UUID | None = Field(default_factory=uuid.uuid4, primary_key=True) + + text: str = Field(nullable=False) + + index: int = Field( + nullable=False, + description="Index of the paragraph in the document", + ) + + @model_validator(mode="after") + def validate_text(self) -> Self: + self.id = text_to_uuid(self.text) + return self + + @computed_field + @property + def hash(self) -> uuid.UUID: + """Compute the hash of the text""" + return text_to_uuid(self.text) + + +class Paragraph(ParagraphBase, table=True): + __tablename__ = "paragraph" + + # NOTE: SQLModel with table=True does not run validators + created_at: datetime = Field( + sa_column_kwargs={"server_default": text("CURRENT_TIMESTAMP")} + ) + updated_at: datetime | None = Field( + sa_column=Column(DateTime(), onupdate=func.now()) + ) + + fk_document: uuid.UUID = Field(None, foreign_key="document.id") + document: "Document" = Relationship(back_populates="paragraphs") + + predictions: list["Prediction"] = Relationship(back_populates="paragraph") + + +class ParagraphUpdate(BaseModel): + text: str | None = None + + +class ParagraphPublic(BaseModel): + id: uuid.UUID + + text: str + hash: uuid.UUID + index: int + + created_at: datetime + updated_at: datetime | None diff --git a/aymurai/database/meta/prediction.py b/aymurai/database/meta/prediction.py new file mode 100644 index 0000000..6fee404 --- /dev/null +++ b/aymurai/database/meta/prediction.py @@ -0,0 +1,81 @@ +import uuid +from datetime import datetime + +from pydantic import BaseModel, computed_field, model_validator +from sqlalchemy import JSON, Column, DateTime, func, text +from sqlmodel import Field, Relationship, SQLModel + +from aymurai.database.meta.model import ModelPublic + +# if TYPE_CHECKING: +from aymurai.database.schema import Model, Paragraph +from aymurai.database.utils import text_to_hash +from aymurai.meta.api_interfaces import DocLabel + + +class PredictionBase(SQLModel): + id: uuid.UUID | None = Field(default_factory=uuid.uuid4, primary_key=True) + + input: str = Field(nullable=False) + input_hash: str | None = None + + prediction: list[DocLabel] | None = Field(None, sa_column=Column(JSON)) + validation: list[DocLabel] | None = Field(None, sa_column=Column(JSON)) + + fk_model: uuid.UUID = Field(foreign_key="model.id") + fk_paragraph: uuid.UUID | None = Field(None, foreign_key="paragraph.id") + + @model_validator(mode="after") + def validate_input(self) -> "PredictionBase": + self.input_hash = text_to_hash(self.input) + + # set the paragraph id in doc labels + if self.validation: + for label in self.validation: + label.fk_paragraph = str(self.fk_paragraph) + if self.prediction: + for label in self.prediction: + label.fk_paragraph = str(self.fk_paragraph) + + return self + + +class Prediction(PredictionBase, table=True): + __tablename__ = "prediction" + + input_hash: str + + created_at: datetime = Field( + sa_column_kwargs={"server_default": text("CURRENT_TIMESTAMP")} + ) + updated_at: datetime | None = Field( + sa_column=Column(DateTime(), onupdate=func.now()) + ) + + model: "Model" = Relationship(back_populates="predictions") + paragraph: Paragraph | None = Relationship(back_populates="predictions") + + +class PredictionCreate(PredictionBase): + def compile(self) -> Prediction: + return Prediction(**self.model_dump()) + + +class PredictionPublic(BaseModel): + """Datatype for a prediction""" + + id: uuid.UUID + created_at: datetime + updated_at: datetime | None = None + + input: str + prediction: list[DocLabel] | None = Field(None, exclude=True) + validation: list[DocLabel] | None = Field(None, exclude=True) + + model: ModelPublic + + @computed_field + @property + def labels(self) -> list[DocLabel]: + """Return the labels of the prediction""" + return self.validation or self.prediction or [] diff --git a/aymurai/database/schema.py b/aymurai/database/schema.py index ef72bac..68048e8 100644 --- a/aymurai/database/schema.py +++ b/aymurai/database/schema.py @@ -1,31 +1,6 @@ # ruff: noqa: F401 - -from .meta.datapublic.paragraph import ( - DataPublicParagraph, - DataPublicParagraphRead, - DataPublicParagraphCreate, - DataPublicParagraphUpdate, -) -from .meta.datapublic.document import ( - DataPublicDocumentBase, - DataPublicDocument, - DataPublicDocumentRead, - DataPublicDocumentCreate, - DataPublicDocumentUpdate, -) -from .meta.anonymization.paragraph import ( - AnonymizationParagraph, - AnonymizationParagraphRead, - AnonymizationParagraphCreate, - AnonymizationParagraphUpdate, -) -from .meta.anonymization.document import ( - AnonymizationDocument, - AnonymizationDocumentRead, - AnonymizationDocumentCreate, - AnonymizationDocumentUpdate, - AnonymizationDocumentParagraph, -) -from .meta.datapublic.document_paragraph import ( - DataPublicDocumentParagraph, -) +from .meta.datapublic import Datapublic, DatapublicFormat, DatapublicPublic +from .meta.document import Document, DocumentPublic, DocumentUpdate +from .meta.model import Model, ModelCreate, ModelPublic, ModelType +from .meta.paragraph import Paragraph, ParagraphPublic, ParagraphUpdate +from .meta.prediction import Prediction, PredictionCreate, PredictionPublic diff --git a/aymurai/database/utils.py b/aymurai/database/utils.py index b692359..97e806b 100644 --- a/aymurai/database/utils.py +++ b/aymurai/database/utils.py @@ -1,13 +1,24 @@ import uuid from hashlib import blake2b +from functools import cache +from pydantic import UUID5 -def data_to_uuid(data: bytes) -> uuid.UUID: + +@cache +def data_to_uuid(data: bytes) -> UUID5: h = blake2b(digest_size=16) h.update(data) return uuid.uuid5(uuid.NAMESPACE_DNS, h.hexdigest()) -def text_to_uuid(text: str) -> uuid.UUID: +def text_to_uuid(text: str) -> UUID5: return data_to_uuid(text.encode("utf-8")) + + +def text_to_hash(text: str) -> str: + h = blake2b(digest_size=16) + h.update(text.encode("utf-8")) + + return h.hexdigest() diff --git a/aymurai/database/versions/13f78d08e925_create_database.py b/aymurai/database/versions/86bdf2a39c7e_create_database.py similarity index 57% rename from aymurai/database/versions/13f78d08e925_create_database.py rename to aymurai/database/versions/86bdf2a39c7e_create_database.py index b01416e..b351809 100644 --- a/aymurai/database/versions/13f78d08e925_create_database.py +++ b/aymurai/database/versions/86bdf2a39c7e_create_database.py @@ -1,8 +1,8 @@ """Create database -Revision ID: 13f78d08e925 +Revision ID: 86bdf2a39c7e Revises: -Create Date: 2025-05-01 00:15:10.748811 +Create Date: 2025-05-25 03:29:14.201556 """ from typing import Sequence, Union @@ -13,7 +13,7 @@ # revision identifiers, used by Alembic. -revision: str = "13f78d08e925" +revision: str = "86bdf2a39c7e" down_revision: Union[str, None] = None branch_labels: Union[str, Sequence[str], None] = None depends_on: Union[str, Sequence[str], None] = None @@ -22,8 +22,9 @@ def upgrade() -> None: # ### commands auto generated by Alembic - please adjust! ### op.create_table( - "anonymization_document", + "document", sa.Column("id", sa.Uuid(), nullable=False), + sa.Column("data", sa.LargeBinary(), nullable=False), sa.Column( "created_at", sa.DateTime(), @@ -35,24 +36,26 @@ def upgrade() -> None: sa.PrimaryKeyConstraint("id"), ) op.create_table( - "anonymization_paragraph", - sa.Column("text", sqlmodel.sql.sqltypes.AutoString(), nullable=False), - sa.Column("prediction", sa.JSON(), nullable=True), - sa.Column("validation", sa.JSON(), nullable=True), + "model", sa.Column("id", sa.Uuid(), nullable=False), + sa.Column("name", sqlmodel.sql.sqltypes.AutoString(), nullable=False), + sa.Column("version", sqlmodel.sql.sqltypes.AutoString(), nullable=False), + sa.Column( + "type", + sa.Enum("ANONYMIZATION", "DATAPUBLIC", name="modeltype"), + nullable=False, + ), + sa.Column("pipeline_path", sqlmodel.sql.sqltypes.AutoString(), nullable=False), sa.Column( "created_at", sa.DateTime(), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=False, ), - sa.Column("updated_at", sa.DateTime(), nullable=True), sa.PrimaryKeyConstraint("id"), ) op.create_table( - "datapublic_document", - sa.Column("prediction", sa.JSON(), nullable=True), - sa.Column("validation", sa.JSON(), nullable=True), + "datapublic", sa.Column("id", sa.Uuid(), nullable=False), sa.Column( "created_at", @@ -61,14 +64,24 @@ def upgrade() -> None: nullable=False, ), sa.Column("updated_at", sa.DateTime(), nullable=True), + sa.Column( + "validation_format", + sa.Enum("NONE", name="datapublicformat"), + nullable=False, + ), + sa.Column("records", sa.JSON(), nullable=True), + sa.Column("fk_document", sa.Uuid(), nullable=True), + sa.ForeignKeyConstraint( + ["fk_document"], + ["document.id"], + ), sa.PrimaryKeyConstraint("id"), ) op.create_table( - "datapublic_paragraph", - sa.Column("text", sqlmodel.sql.sqltypes.AutoString(), nullable=False), - sa.Column("prediction", sa.JSON(), nullable=True), - sa.Column("validation", sa.JSON(), nullable=True), + "paragraph", sa.Column("id", sa.Uuid(), nullable=False), + sa.Column("text", sqlmodel.sql.sqltypes.AutoString(), nullable=False), + sa.Column("index", sa.Integer(), nullable=False), sa.Column( "created_at", sa.DateTime(), @@ -76,49 +89,47 @@ def upgrade() -> None: nullable=False, ), sa.Column("updated_at", sa.DateTime(), nullable=True), - sa.PrimaryKeyConstraint("id"), - ) - op.create_table( - "anonymization_document_paragraph", - sa.Column("id", sa.Uuid(), nullable=False), - sa.Column("document_id", sa.Uuid(), nullable=False), - sa.Column("paragraph_id", sa.Uuid(), nullable=False), - sa.Column("order", sa.Integer(), nullable=True), + sa.Column("fk_document", sa.Uuid(), nullable=False), sa.ForeignKeyConstraint( - ["document_id"], - ["anonymization_document.id"], + ["fk_document"], + ["document.id"], ), - sa.ForeignKeyConstraint( - ["paragraph_id"], - ["anonymization_paragraph.id"], - ), - sa.PrimaryKeyConstraint("id", "document_id", "paragraph_id"), + sa.PrimaryKeyConstraint("id"), ) op.create_table( - "datapublic_document_paragraph", + "prediction", sa.Column("id", sa.Uuid(), nullable=False), - sa.Column("document_id", sa.Uuid(), nullable=False), - sa.Column("paragraph_id", sa.Uuid(), nullable=False), - sa.Column("order", sa.Integer(), nullable=True), + sa.Column("input", sqlmodel.sql.sqltypes.AutoString(), nullable=False), + sa.Column("prediction", sa.JSON(), nullable=True), + sa.Column("validation", sa.JSON(), nullable=True), + sa.Column("fk_model", sa.Uuid(), nullable=False), + sa.Column("fk_paragraph", sa.Uuid(), nullable=True), + sa.Column("input_hash", sqlmodel.sql.sqltypes.AutoString(), nullable=False), + sa.Column( + "created_at", + sa.DateTime(), + server_default=sa.text("(CURRENT_TIMESTAMP)"), + nullable=False, + ), + sa.Column("updated_at", sa.DateTime(), nullable=True), sa.ForeignKeyConstraint( - ["document_id"], - ["datapublic_document.id"], + ["fk_model"], + ["model.id"], ), sa.ForeignKeyConstraint( - ["paragraph_id"], - ["datapublic_paragraph.id"], + ["fk_paragraph"], + ["paragraph.id"], ), - sa.PrimaryKeyConstraint("id", "document_id", "paragraph_id"), + sa.PrimaryKeyConstraint("id"), ) # ### end Alembic commands ### def downgrade() -> None: # ### commands auto generated by Alembic - please adjust! ### - op.drop_table("datapublic_document_paragraph") - op.drop_table("anonymization_document_paragraph") - op.drop_table("datapublic_paragraph") - op.drop_table("datapublic_document") - op.drop_table("anonymization_paragraph") - op.drop_table("anonymization_document") + op.drop_table("prediction") + op.drop_table("paragraph") + op.drop_table("datapublic") + op.drop_table("model") + op.drop_table("document") # ### end Alembic commands ### diff --git a/aymurai/meta/api_interfaces.py b/aymurai/meta/api_interfaces.py index 1ff5144..49089e7 100644 --- a/aymurai/meta/api_interfaces.py +++ b/aymurai/meta/api_interfaces.py @@ -2,7 +2,7 @@ from pydantic import UUID5, BaseModel, Field, RootModel -from aymurai.meta.entities import EntityAttributes +from aymurai.meta.entities import DocLabel class SuccessResponse(BaseModel): @@ -19,27 +19,11 @@ class TextRequest(BaseModel): ) -class DocLabel(BaseModel): - """Datatype for a document label""" - - text: str = Field( - description="raw text of entity", - # alias=AliasChoices(["text", "document"]), - ) - start_char: int = Field( - description="start character of the span in relation of the full text" - ) - end_char: int = Field( - description="last character of the span in relation of the full text" - ) - attrs: EntityAttributes - - class DocumentInformation(BaseModel): """Datatype for a document information with all labels""" document: str = Field(description="processed text") - labels: list[DocLabel] = Field(default_factory=list) + labels: list[DocLabel] | None = None class DocumentAnnotations(BaseModel): @@ -48,6 +32,11 @@ class DocumentAnnotations(BaseModel): data: list[DocumentInformation] +# class Document(BaseModel): +# document: list[str] +# document_id: UUID5 +# header: list[str] | None = None +# footer: list[str] | None = None class DataPublicDocumentAnnotations(RootModel): """Datatype for document annotations""" diff --git a/aymurai/meta/entities.py b/aymurai/meta/entities.py index d16c0b6..28e2fd6 100644 --- a/aymurai/meta/entities.py +++ b/aymurai/meta/entities.py @@ -1,4 +1,6 @@ -from pydantic import BaseModel, Field +import uuid + +from pydantic import BaseModel, Field, model_validator class EntityAttributes(BaseModel): @@ -38,3 +40,29 @@ class Entity(BaseModel): context_pre: str = "" context_post: str = "" attrs: EntityAttributes | None = None + + +class DocLabel(BaseModel): + """Datatype for a document label""" + + id: str | None = None + fk_paragraph: str | None = None + + text: str = Field( + description="raw text of entity", + # alias=AliasChoices(["text", "document"]), + ) + start_char: int = Field( + description="start character of the span in relation of the full text" + ) + end_char: int = Field( + description="last character of the span in relation of the full text" + ) + attrs: EntityAttributes + + @model_validator(mode="after") + def _(self) -> "DocLabel": + """Validate the DocLabel attributes.""" + if not self.id: + self.id = str(uuid.uuid5(uuid.NAMESPACE_DNS, self.attrs.model_dump_json())) + return self diff --git a/aymurai/meta/xml_document.py b/aymurai/meta/xml_document.py new file mode 100644 index 0000000..250256c --- /dev/null +++ b/aymurai/meta/xml_document.py @@ -0,0 +1,30 @@ +from pydantic import BaseModel + +from aymurai.database.meta.extra import ParagraphPredictionPublic + + +class XMLTextFragment(BaseModel): + text: str + normalized_text: str + start: int + end: int + fragment_index: int + paragraph_index: int + + +class ParagraphMetadata(BaseModel): + start: int + end: int + fragments: list[XMLTextFragment] + xml_file: str + + +class XMLParagraph(BaseModel): + plain_text: str + metadata: ParagraphMetadata + hash: str | int | None = None + __pred_indices: list[int] = [] + + +class XMLParagraphWithParagraphPrediction(XMLParagraph): + paragraph_prediction: ParagraphPredictionPublic diff --git a/aymurai/models/decision/binregex.py b/aymurai/models/decision/binregex.py index a3b77a0..46a7c80 100644 --- a/aymurai/models/decision/binregex.py +++ b/aymurai/models/decision/binregex.py @@ -7,7 +7,8 @@ from unidecode import unidecode from aymurai.logger import get_logger -from aymurai.meta.api_interfaces import DocLabel, EntityAttributes +from aymurai.meta.api_interfaces import DocLabel +from aymurai.meta.entities import EntityAttributes from aymurai.meta.pipeline_interfaces import TrainModule from aymurai.meta.types import DataBlock, DataItem from aymurai.models.decision.conv1d import Conv1dTextClassifier diff --git a/aymurai/settings.py b/aymurai/settings.py index 9fdd533..ebbe886 100644 --- a/aymurai/settings.py +++ b/aymurai/settings.py @@ -1,29 +1,45 @@ import os from pathlib import Path +from typing import Literal from dotenv import load_dotenv +from pydantic import ConfigDict, FilePath, field_validator from pydantic_settings import BaseSettings -from pydantic import FilePath, ConfigDict, field_validator import aymurai +from aymurai.logger import get_logger + +try: + from aymurai.version import __version__ +except ImportError: + __version__ = "0.0.0" PARENT = Path(aymurai.__file__).parent +logger = get_logger(__name__) + def load_env(): + logger.info("Loading environment variables from .env files") load_dotenv(".env") # Load the stage-specific .env file (if it exists) stage = os.getenv("STAGE") if stage: + logger.info(f"Loading environment variables for stage: {stage}") env_file = f".env.{stage}" if os.path.exists(env_file): + logger.info(f"Loading environment variables from {env_file}") load_dotenv(env_file) class Settings(BaseSettings): model_config = ConfigDict(case_sensitive=True) + APP_VERSION: str = __version__ + + ERROR_HANDLER: Literal["ignore", "raise"] = "ignore" + CORS_ORIGINS: list[str] | str = ",".join( [ "http://localhost", @@ -51,7 +67,7 @@ def assemble_cors_origins(cls, v) -> list[str]: RESOURCES_BASEPATH: str = "/resources" # Alembic Config for running migrations - ALEMBIC_INI_PATH: FilePath = PARENT / "alembic.ini" + ALEMBIC_CONFIG: FilePath = PARENT / "alembic.ini" ENV: str | None = None @@ -61,6 +77,9 @@ def assemble_cors_origins(cls, v) -> list[str]: LIBREOFFICE_BIN: str = "libreoffice" + # ----- Miscellaneous settings ----- + DEVELOPMENT_MODE: bool = False + load_env() settings = Settings() diff --git a/aymurai/api/endpoints/routers/misc/__init__.py b/aymurai/text/anonymization/__init__.py similarity index 100% rename from aymurai/api/endpoints/routers/misc/__init__.py rename to aymurai/text/anonymization/__init__.py diff --git a/aymurai/text/anonymization/alignment.py b/aymurai/text/anonymization/alignment.py new file mode 100644 index 0000000..ff1cdb5 --- /dev/null +++ b/aymurai/text/anonymization/alignment.py @@ -0,0 +1,309 @@ +import os +import re +import xml.sax.saxutils +from copy import deepcopy +from unicodedata import normalize + +import jiwer +import numpy as np +import pandas as pd +from more_itertools import flatten + +from aymurai.database.meta.extra import ParagraphPredictionPublic +from aymurai.database.schema import PredictionPublic +from aymurai.meta.entities import DocLabel, EntityAttributes +from aymurai.meta.xml_document import ( + ParagraphMetadata, + XMLParagraph, + XMLParagraphWithParagraphPrediction, + XMLTextFragment, +) +from aymurai.models.flair.utils import FlairTextNormalize +from aymurai.utils.alignment.core import align_text, tokenize + +REGEX_PARAGRAPH = r"((?.*?)(\/w:p\b)" +REGEX_FRAGMENT = r"(?(?P.*?)(<.*?\/w:t)" + + +def merge_consecutive_labels(text: str, labels: list[DocLabel]) -> list[DocLabel]: + """ + Merge consecutive labels in the paragraph prediction. + Args: + text (str): The original text. + labels (list[DocLabel]): A list of labels to merge. + Returns: + list[DocLabel]: A list of merged DocLabel objects. + """ + if not labels: + return [] + + labels = sorted(labels, key=lambda x: x.start_char) + merged = [] + current = labels[0] + + for label in labels[1:]: + # Get label info + cur_label = current.attrs.aymurai_label + next_label = label.attrs.aymurai_label + cur_end = current.attrs.aymurai_alt_end_char or current.end_char + next_start = label.attrs.aymurai_alt_start_char or label.start_char + # If same label and adjacent or overlapping + if cur_label == next_label and (next_start - cur_end) <= 1: + # Extend current label + current = DocLabel( + text=text[current.start_char : (label.end_char + 1)], + start_char=current.start_char, + end_char=label.end_char, + attrs=EntityAttributes( + aymurai_label=cur_label, + aymurai_alt_text=text[current.start_char : (label.end_char + 1)], + aymurai_alt_start_char=current.start_char, + aymurai_alt_end_char=label.end_char, + ), + ) + else: + merged.append(current) + current = label + merged.append(current) + return merged + + +def replace_labels_in_text(text: str, prediction: PredictionPublic | None) -> str: + """ + Replaces labels in the text with anonymized tokens. + + Args: + text (str): The text to be anonymized. + prediction (PredictionPublic): The prediction object containing labels. + + Returns: + str: The text with replaced labels. + """ + + if not prediction: + return text + + # merge consecutive labels + # merged_labels = merge_consecutive_labels(text, prediction.labels) + merged_labels = prediction.labels + + # Initialize the offset + offset = 0 + + # Replace labels in the text + for label in merged_labels: + # Adjust start and end character indices of the label + start_char = label.start_char + offset + end_char = label.end_char + offset + len_text_to_replace = end_char - start_char + + # Replace the text with the anonymized token + aymurai_label = xml.sax.saxutils.escape(f" <{label.attrs.aymurai_label}>") + len_aymurai_label = len(aymurai_label) + + text = text[:start_char] + aymurai_label + text[end_char:] + + # Update the offset + offset += len_aymurai_label - len_text_to_replace + + return re.sub(r" +", " ", text).strip() + + +def erase_duplicates_justseen(series: pd.Series) -> pd.Series: + """ + Replaces consecutive duplicate values in a pandas Series with an empty string, keeping only the first occurrence. + """ + return series.where(series.ne(series.shift(), fill_value=None), "") + + +def gen_alignment_table(paragraph: XMLParagraphWithParagraphPrediction) -> pd.DataFrame: + original_text = " ".join([f.text for f in paragraph.metadata.fragments]) + anonymized_text = replace_labels_in_text( + text=paragraph.plain_text, + prediction=paragraph.paragraph_prediction.prediction, + ) + + aligned = align_text( + " " + original_text + " ", + " " + anonymized_text + " ", + ) + aligned["target"] = erase_duplicates_justseen(aligned["target"]) + + xml_file = paragraph.metadata.xml_file + + tokens = [] + for i, fragment in enumerate(paragraph.metadata.fragments): + text = fragment.text + tokenized_text = tokenize(text) + paragraph_index = fragment.paragraph_index + + # Use re.finditer to locate each instance of tokens in the text + # \S+ matches any non-whitespace sequence + token_matches = list(re.finditer(r"\S+", text)) + + # Loop over tokenized text and token_matches in parallel + for j, (token, match) in enumerate(zip(tokenized_text, token_matches)): + start = paragraph.metadata.start + fragment.start + match.start() + end = start + len(token) + + # tokens.append((xml_file, paragraph_index, i, j, token, start, end)) + tokens.append( + { + "xml_file": xml_file, + "paragraph_index": paragraph_index, + "fragment_index": i, + "token_index": j, + "token": token, + "start_char": start, + "end_char": end, + } + ) + + tokens_df = pd.DataFrame(tokens) + + tokens_df = pd.concat( + [ + tokens_df, + aligned["target"].iloc[1:-1].reset_index(drop=True), + ], + axis=1, + ) + + tokens_df["target"].fillna("", inplace=True) + + return tokens_df + + +def index_paragraphs(file: str) -> list[XMLParagraph]: + """ + Indexes the paragraphs of an XML file. + + Args: + file (str): The path to the XML file to be indexed. + + Returns: + list[Paragraph]: A list of Paragraph instances representing the indexed paragraphs. + """ + # Read the XML file + with open(file) as f: + xml = f.read() + + paragraphs = [] + paragraph_index = 0 + + # Find all paragraphs in the XML file + for match in re.finditer(REGEX_PARAGRAPH, xml): + paragraph = match.group("paragraph") + paragraph_start = match.start("paragraph") + paragraph_end = match.end("paragraph") + fragments: list[XMLTextFragment] = [] + fragment_index = 0 + + # Find all text fragments in the paragraph + for fragment in re.finditer(REGEX_FRAGMENT, paragraph): + text = fragment.group("text") + start = fragment.start("text") + end = fragment.end("text") + + fragments.append( + XMLTextFragment( + text=text, + normalized_text=FlairTextNormalize.normalize_text(text), + start=start, + end=end, + fragment_index=fragment_index, + paragraph_index=paragraph_index, + ) + ) + fragment_index += 1 + + # Join all fragments as plain text + plain_text = "".join([fragment.normalized_text for fragment in fragments]) + + paragraphs.append( + XMLParagraph( + plain_text=plain_text, + metadata=ParagraphMetadata( + start=paragraph_start, + end=paragraph_end, + fragments=fragments, + xml_file=os.path.basename(file), + ), + ) + ) + paragraph_index += 1 + + return paragraphs + + +def match_paragraphs_with_predictions( + src_paragraphs: list[XMLParagraph], + pred_paragraphs: list[ParagraphPredictionPublic], +) -> list[XMLParagraphWithParagraphPrediction]: + """ + Matches each paragraph with its corresponding predictions. + + Args: + paragraphs (list[dict]): A list of dictionaries representing the paragraphs. + predictions (list[ParagraphPredictionPublic]): A list of ParagraphPredictionPublic + instances representing the predictions. + + Returns: + list[dict]: A list of dictionaries representing + the matched paragraphs with predictions. + """ + + src_paragraphs = deepcopy(src_paragraphs) + + # Hash prediction documents + def text_hash(text: str) -> int: + return hash(normalize("NFKC", text.strip())) + + # Compute hashes for prediction paragraphs + pred_hashes = [text_hash(pred.text) for pred in pred_paragraphs] + idx2hash = {i: h for i, h in enumerate(pred_hashes)} + + # Compute hashes for source paragraphs + for p in src_paragraphs: + p.hash = text_hash(p.plain_text) + + # Map paragraph hashes to prediction indices + hash2idx = { + p.hash: [i for i, h in idx2hash.items() if h == p.hash] for p in src_paragraphs + } + + # Assign prediction indices to each paragraph + for p in src_paragraphs: + p.__pred_indices = hash2idx[p.hash] + + # Find indices of predictions that have not been matched to any paragraph + matched_indices = set(flatten(hash2idx.values())) + all_indices = set(idx2hash.keys()) + missing_indices = list(all_indices - matched_indices) + + if missing_indices: + # Assign prediction indices to each paragraph by lowest CER + target_texts = np.array([pred.text for pred in pred_paragraphs])[ + missing_indices + ] + + missing_paragraphs = [p for p in src_paragraphs if not p.__pred_indices] + + for missing_paragraph in missing_paragraphs: + source_text = missing_paragraph.plain_text + cer = np.array( + [jiwer.cer(source_text, target_text) for target_text in target_texts] + ) + min_cer_idx = np.argmin(cer) + missing_paragraph.__pred_indices = [missing_indices[min_cer_idx]] + + # Assign document text and labels + new_paragraphs = [ + XMLParagraphWithParagraphPrediction( + **p.model_dump(), + paragraph_prediction=pred_paragraphs[p.__pred_indices[0]], + ) + for p in src_paragraphs + ] + + return new_paragraphs diff --git a/aymurai/text/anonymization/core.py b/aymurai/text/anonymization/core.py new file mode 100644 index 0000000..ccb3e7a --- /dev/null +++ b/aymurai/text/anonymization/core.py @@ -0,0 +1,70 @@ +import os +import tempfile +from glob import glob + +from more_itertools import flatten + +from aymurai.database.meta.extra import ParagraphPredictionPublic +from aymurai.logger import get_logger +from aymurai.text.anonymization.alignment import ( + index_paragraphs, + match_paragraphs_with_predictions, +) +from aymurai.text.anonymization.xml_docx import ( + create_docx, + replace_text_in_xml, + unzip_document, +) + +logger = get_logger(__file__) + + +def anonymize_docx( + path: str, + paragraph_preds: list[ParagraphPredictionPublic], + output_dir: str = ".", +) -> None: + """ + Performs the anonymization process on a document. + + Args: + path (str): The path to the document to be anonymized. + paragraph_preds (list[ParagraphPredictionPublic]): The list of predictions for the document. + output_dir (str, optional): The directory to save the anonymized document. + Defaults to ".". + + Raises: + ValueError: If the document has an extension other than `.docx`. + """ + if not os.path.splitext(path)[-1] == ".docx": + raise ValueError("Only `.docx` extension is allowed.") + + if not os.path.exists(path): + raise ValueError(f"File not found: {path}") + + # Unzip document into a temporary directory + with tempfile.TemporaryDirectory() as tempdir: + unzip_document(path, tempdir) + + # Parse XML files + xml_files = glob(f"{tempdir}/**/*.xml", recursive=True) + source_paragraphs = (index_paragraphs(file) for file in xml_files) + source_paragraphs = list(flatten(source_paragraphs)) + + # Filter out empty paragraphs + source_paragraphs = [p for p in source_paragraphs if p.plain_text.strip()] + + # Matching + paragraphs = match_paragraphs_with_predictions( + source_paragraphs, paragraph_preds + ) + + # Edit XML files + replace_text_in_xml(paragraphs, tempdir) + + # Recreate anonymized document + os.makedirs(output_dir, exist_ok=True) + create_docx( + tempdir, + f"{output_dir}/{os.path.basename(path)}", + ) diff --git a/aymurai/text/anonymization/xml_docx.py b/aymurai/text/anonymization/xml_docx.py new file mode 100644 index 0000000..18c7871 --- /dev/null +++ b/aymurai/text/anonymization/xml_docx.py @@ -0,0 +1,160 @@ +import os +import re +import zipfile + +import pandas as pd +from lxml import etree + +from aymurai.meta.xml_document import XMLParagraphWithParagraphPrediction +from aymurai.text.anonymization.alignment import gen_alignment_table + + +def unzip_document(doc_path: str, output_dir: str) -> None: + """ + Unzips the document file to the specified output directory. + + Args: + doc_path (str): The path to the document file. + output_dir (str): The directory where the contents of the document + file will be extracted. + """ + # Ensure the output directory exists + os.makedirs(output_dir, exist_ok=True) + + # Open the doc file as a zip file + with zipfile.ZipFile(doc_path, "r") as doc_zip: + # Extract all the contents to the output directory + doc_zip.extractall(output_dir) + # logger.info(f"unzipped {doc_path} to {output_dir}") + + +def normalize_document(xml_content: str) -> str: + """ + Normalizes the XML document by removing extra spaces, preserving line breaks, + and removing hyperlinks while preserving text content. + + Args: + xml_content (str): The XML content to be normalized. + + Returns: + str: The normalized XML content. + """ + # Parse the XML content + parser = etree.XMLParser(ns_clean=True) + root = etree.fromstring(xml_content.encode("utf-8"), parser) + + # Extract namespaces + namespaces = {k: v for k, v in root.nsmap.items() if k} + + # Remove hyperlinks but preserve the text content + for hyperlink in root.xpath("//w:hyperlink", namespaces=namespaces): + parent = hyperlink.getparent() + index = list(parent).index(hyperlink) + + # Move all text-containing children (e.g., w:r elements) outside the hyperlink tag + for child in hyperlink: + parent.insert(index, child) + index += 1 + parent.remove(hyperlink) # Remove the element itself + + # Process each paragraph + for wp in root.xpath("//w:p", namespaces=namespaces): + first = True + + # Find all w:r elements containing w:t elements + for wr in wp.xpath(".//w:r", namespaces=namespaces): + wt = wr.find(".//w:t", namespaces) + if wt is not None and wt.text: + # Normalize spaces within the text, preserving new line breaks + wt.text = re.sub(r"[^\S\r\n]+", " ", wt.text) + + # Add a leading space if not the first fragment in the paragraph + if not first: + wt.text = " " + wt.text.lstrip() + else: + wt.text = wt.text.lstrip() + first = False + + # Remove trailing spaces from all fragments + wt.text = wt.text.rstrip() + + # Set the xml:space attribute to preserve + wt.set( + "{http://www.w3.org/XML/1998/namespace}space", + "preserve", + ) + + # Check if the text is empty after normalization + if not wt.text or wt.text.strip() == "": + # Remove the w:r element from its parent + wr.getparent().remove(wr) + + # Write back the XML content to a string + xml_str = etree.tostring(root, encoding="unicode", pretty_print=True) + + return xml_str + + +def replace_text_in_xml( + paragraphs: list[XMLParagraphWithParagraphPrediction], base_dir: str +) -> None: + tokens = pd.concat( + [gen_alignment_table(paragraph) for paragraph in paragraphs], + ignore_index=True, + ) + + fragments = ( + tokens.groupby(["xml_file", "paragraph_index", "fragment_index"]) + .agg({"target": " ".join, "start_char": "min", "end_char": "max"}) + .reset_index() + ) + + for xml_file, group in fragments.groupby("xml_file"): + group = group.sort_values("end_char", ascending=False) + + with open(f"{base_dir}/word/{xml_file}", "r+") as file: + content = file.read() + + for _, r in group.iterrows(): + start_char = r["start_char"] + end_char = r["end_char"] + + target = r["target"] + target = re.sub(r"[^\S\r\n]+", " ", target) + + content = content[:start_char] + target + content[end_char:] + + # MUST be at the end to dont screw up the indexes + content = normalize_document(content) + + file.seek(0) + file.write(content) + file.truncate() + + +def add_files_to_zip(zip_file: zipfile.ZipFile, directory: str) -> None: + """ + Adds all files in the specified directory to a zip file. + + Args: + zip_file (zipfile.ZipFile): The zip file to add the files to. + directory (str): The directory containing the files to be added. + """ + for root, _, files in os.walk(directory): + for file in files: + file_path = os.path.join(root, file) + zip_file.write(file_path, os.path.relpath(file_path, directory)) + + +def create_docx(xml_directory, output_file) -> None: + """ + Creates a new DOCX file by adding XML components from the specified directory. + + Args: + xml_directory (str): The directory containing the XML components. + output_file (str): The path to the output DOCX file. + """ + # Create a new zip file + with zipfile.ZipFile(output_file, "w") as docx: + # Add XML components + add_files_to_zip(docx, xml_directory) diff --git a/aymurai/text/extraction.py b/aymurai/text/extraction.py index 0e00cd1..2ee5aaa 100644 --- a/aymurai/text/extraction.py +++ b/aymurai/text/extraction.py @@ -1,13 +1,12 @@ -import os import logging -import zipfile +import os import statistics import unicodedata +import zipfile from pathlib import Path from typing import Any from zipfile import BadZipFile -import magic import numpy as np import pymupdf import textract @@ -80,11 +79,6 @@ def __call__(self, item: dict) -> dict: return item -def get_extension(path: str) -> str: - mimetype = magic.from_file(path, mime=True) - return MIMETYPE_EXTENSION_MAPPER.get(mimetype, mimetype) - - def _load_xml_from_odt(path: str, xmlfile: str = "styles.xml") -> str: """ Load xml file inside an odt. @@ -239,7 +233,8 @@ def extract_document( if errors not in ERRORS: raise ValueError(f"errors argument must be in {ERRORS}") - ext = get_extension(filename) + _, ext = os.path.splitext(filename) + ext = ext.lstrip(".").lower() kwargs["extension"] = kwargs.get("extension", ext) kwargs["output_encoding"] = kwargs.get("output_encoding", "utf-8") diff --git a/aymurai/utils/pickle_data.py b/aymurai/utils/pickle_data.py index 4d5ceeb..d1d8caf 100644 --- a/aymurai/utils/pickle_data.py +++ b/aymurai/utils/pickle_data.py @@ -9,6 +9,6 @@ def save_pickle(object_: Any, output_path: str): def load_pickle(input_path: str) -> Any: - with (open(input_path, "rb")) as f: + with open(input_path, "rb") as f: object_ = pickle.load(f) return object_ diff --git a/docker-compose.yml b/docker-compose.yml index 3ce7c53..d6ac06a 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -7,9 +7,6 @@ services: context: . dockerfile: ./docker/api/Dockerfile target: aymurai-api - env_file: - - .env - - .env.common volumes: - ./resources/cache:/resources/cache diff --git a/docker/api/Dockerfile b/docker/api/Dockerfile index 6db18e4..05ab765 100644 --- a/docker/api/Dockerfile +++ b/docker/api/Dockerfile @@ -1,3 +1,6 @@ +############################################################################### +# Stage - aymurai builder +############################################################################### FROM python:3.10-slim AS builder # install git @@ -18,6 +21,8 @@ RUN --mount=type=cache,target=/root/.cache/uv \ COPY aymurai /app/aymurai # Sync the package +ARG SETUPTOOLS_SCM_PRETEND_VERSION +ENV SETUPTOOLS_SCM_PRETEND_VERSION=${SETUPTOOLS_SCM_PRETEND_VERSION} RUN --mount=type=bind,source=.git,target=.git \ --mount=type=cache,target=/root/.cache/uv \ --mount=type=bind,source=uv.lock,target=uv.lock \ @@ -25,8 +30,9 @@ RUN --mount=type=bind,source=.git,target=.git \ uv sync --frozen --no-editable +############################################################################### # Stage - aymurai-api -# ------------------- +############################################################################### FROM python:3.10-slim AS aymurai-api ENV LANG=C.UTF-8 LC_ALL=C.UTF-8 @@ -43,11 +49,9 @@ ENV LANG=en_US.UTF-8 LC_ALL=en_US.UTF-8 # install libreoffice RUN apt update \ && apt-get install -y --no-install-recommends \ - # antiword \ libreoffice-writer \ libreoffice-common \ default-jre \ - libmagic1 \ && apt-get autoremove -y \ && apt-get clean \ && rm -rf /tmp/* /var/tmp/* \ @@ -56,23 +60,20 @@ RUN apt update \ COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/ COPY --from=builder --chown=app:app /app/.venv /app/.venv -COPY resources/api/static /resources/api/static COPY resources/pipelines /resources/pipelines WORKDIR /app CMD uv run fastapi run --port 8899 .venv/lib/python3.10/site-packages/aymurai/api/main.py - - - +############################################################################### +# Stage - aymurai-api-full +############################################################################### FROM aymurai-api AS aymurai-api-full ENV TF_CPP_MIN_LOG_LEVEL=3 ENV TFHUB_CACHE_DIR=resources/cache/tfhub_modules ENV RESOURCES_BASEPATH=resources -# copy api resources -COPY ./resources/api resources/api # copy pipelines & run app startup to download models COPY ./resources/pipelines/production resources/pipelines/production diff --git a/notebooks/experiments/anonymization/05-entity-grouping/entitty-groups.ipynb b/notebooks/experiments/anonymization/05-entity-grouping/entitty-groups.ipynb new file mode 100644 index 0000000..d33a9a6 --- /dev/null +++ b/notebooks/experiments/anonymization/05-entity-grouping/entitty-groups.ipynb @@ -0,0 +1,358 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext rich" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import os\n", + "\n", + "import requests\n", + "from tqdm import tqdm\n", + "\n", + "API_URL = \"http://localhost:8999\" # Url for debugger. change it to your own" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sample document" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "doc_path = \"/resources/data/sample/document-01.docx\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## /document-extract endpoint output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Function to extract document using the API\n", + "def extract_document(file_path: str) -> dict:\n", + " # Open the file in binary mode and send the POST request\n", + " with open(file_path, \"rb\") as file:\n", + " files = {\"file\": file}\n", + " response = requests.post(url=f\"{API_URL}/document/extract\", files=files)\n", + " response.raise_for_status()\n", + " return response.json()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# /document-extract endpoint output\n", + "extracted_document = extract_document(doc_path)\n", + "extracted_document" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(extracted_document[\"paragraphs\"])\n", + "\n", + "document_id = extracted_document[\"id\"]\n", + "print(\"document_id:\", document_id)\n", + "print(\"number of paragraphs:\", len(extracted_document[\"paragraphs\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inference" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import uuid\n", + "\n", + "\n", + "# Function to make inference using the API\n", + "def get_predictions(paragraph_id: uuid.UUID) -> dict:\n", + " response = requests.get(\n", + " url=f\"{API_URL}/pipeline/anonymization/paragraph/{paragraph_id}/predict\",\n", + " params={\"use_cache\": True},\n", + " )\n", + " response.raise_for_status()\n", + " return response.json()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = [\n", + " get_predictions(paragraph[\"id\"])\n", + " for paragraph in tqdm(extracted_document[\"paragraphs\"])\n", + "]\n", + "predictions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "predictions[5]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from aymurai.database.crud.prediction import read_document_prediction_paragraphs\n", + "from aymurai.database.meta.extra import ParagraphPredictionPublic\n", + "from aymurai.database.schema import ModelType\n", + "from aymurai.database.session import get_session\n", + "\n", + "session = next(get_session())\n", + "\n", + "annotations = read_document_prediction_paragraphs(\n", + " session=session,\n", + " document_id=uuid.UUID(document_id),\n", + " model_type=ModelType.ANONYMIZATION,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "annotations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "document_id\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from itertools import groupby\n", + "\n", + "from more_itertools import flatten\n", + "\n", + "\n", + "def get_entities(prediction):\n", + " return prediction[\"labels\"]\n", + "\n", + "\n", + "labels = [para.prediction.labels for para in annotations if para.prediction]\n", + "entities = list(flatten(labels))\n", + "\n", + "indexed_entities = [(i, entity) for i, entity in enumerate(entities)]\n", + "indexed_entities = sorted(indexed_entities, key=lambda x: x[1].attrs.aymurai_label)\n", + "\n", + "\n", + "groups = {\n", + " label: list(group)\n", + " for label, group in groupby(\n", + " indexed_entities, key=lambda x: x[1].attrs.aymurai_label\n", + " )\n", + "}\n", + "groups" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "groups.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "entities" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import unicodedata\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import regex\n", + "from sklearn.cluster import DBSCAN\n", + "from sklearn.feature_extraction.text import CountVectorizer\n", + "\n", + "from aymurai.meta.entities import DocLabel\n", + "\n", + "\n", + "# Normalize helper\n", + "def normalize_text(text):\n", + " text = unicodedata.normalize(\"NFKD\", text)\n", + " text = \"\".join(ch for ch in text if unicodedata.category(ch) != \"Mn\")\n", + " text = regex.sub(r\"\\s+\", \" \", text)\n", + " text = regex.sub(r\"\\p{P}\", \"\", text)\n", + " return text.lower()\n", + "\n", + "\n", + "def cluster_entities(entities: list[DocLabel], eps: float = 0.01, min_samples: int = 2):\n", + " \"\"\"\n", + " Cluster entity texts in a group and return a DataFrame with columns:\n", + " text, norm_text, index, cluster. Uses binary vectorization and Jaccard-DBSCAN.\n", + " \"\"\"\n", + "\n", + " indexed_entities = [(i, entity) for i, entity in enumerate(entities)]\n", + " indexed_entities = sorted(indexed_entities, key=lambda x: x[1].attrs.aymurai_label)\n", + "\n", + " # Prepare raw and normalized texts\n", + " texts = [ent[1].text for ent in indexed_entities]\n", + " norm_texts = [normalize_text(t) for t in texts]\n", + "\n", + " # --------- Binary vectorization of words ----------------\n", + " vectorizer = CountVectorizer(binary=True, token_pattern=r\"\\b\\w+\\b\")\n", + " matrix = vectorizer.fit_transform(\n", + " norm_texts\n", + " ) # sparse matrix (n_samples x n_features)\n", + "\n", + " # --------- Jaccard distance matrix ----------------\n", + " ints = (matrix @ matrix.T).toarray()\n", + " row_sums = matrix.sum(axis=1).A1\n", + " union = row_sums[:, None] + row_sums[None, :] - ints\n", + " # Avoid division by zero and compute distances\n", + " with np.errstate(divide=\"ignore\", invalid=\"ignore\"):\n", + " X = 1 - (ints / union)\n", + " X[union == 0] = 1.0\n", + "\n", + " # -------- DBSCAN ----------------------------------------------------\n", + " db = DBSCAN(eps=eps, min_samples=min_samples, metric=\"precomputed\")\n", + " labels = db.fit_predict(X)\n", + "\n", + " # Merge clusters by overlapping centroids\n", + " unique_lbls = sorted(set(labels) - {-1})\n", + " if unique_lbls:\n", + " # build centroid binary vectors\n", + " centroid_vectors = []\n", + " for lbl in unique_lbls:\n", + " mat = matrix[labels == lbl]\n", + " centroid = (mat.sum(axis=0) > 0).A1 # boolean mask\n", + " centroid_vectors.append(centroid)\n", + "\n", + " C = np.vstack(centroid_vectors).astype(bool)\n", + " sim = (C.astype(int) @ C.T.astype(int)) > 0\n", + " mapping = {\n", + " lbl: unique_lbls[int(np.argmax(sim[idx]))]\n", + " for idx, lbl in enumerate(unique_lbls)\n", + " }\n", + " labels = [mapping.get(lbl, -1) for lbl in labels]\n", + "\n", + " # Build results DataFrame\n", + " df = (\n", + " pd.DataFrame({\n", + " \"id\": [ent[1].id for ent in indexed_entities],\n", + " \"index\": [ent[0] for ent in indexed_entities],\n", + " \"paragraph_id\": [ent[1].fk_paragraph for ent in indexed_entities],\n", + " \"label\": [ent[1].attrs.aymurai_label for ent in indexed_entities],\n", + " \"text\": texts,\n", + " \"norm_text\": norm_texts,\n", + " \"cluster\": labels,\n", + " })\n", + " .sort_values(\"cluster\")\n", + " .reset_index(drop=True)\n", + " )\n", + " df[\"id\"] = df[\"id\"].apply(uuid.UUID)\n", + " df[\"paragraph_id\"] = df[\"paragraph_id\"].apply(uuid.UUID)\n", + " df.set_index(\"id\", inplace=True)\n", + "\n", + " return df\n", + "\n", + "\n", + "# Example: cluster the PER group\n", + "results = cluster_entities(entities)\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.groupby(\"cluster\").apply(lambda x: x[\"norm_text\"].tolist()).to_dict()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/experiments/pdf-support/04-anonymize-pdf.ipynb b/notebooks/experiments/pdf-support/04-anonymize-pdf.ipynb index 2bb1a47..83fe365 100644 --- a/notebooks/experiments/pdf-support/04-anonymize-pdf.ipynb +++ b/notebooks/experiments/pdf-support/04-anonymize-pdf.ipynb @@ -15,20 +15,25 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", "import json\n", + "import os\n", "\n", "import requests\n", + "from rich.console import Console\n", "from tqdm import tqdm\n", "\n", + "console = Console(width=100)\n", + "\n", "API_URL = \"http://localhost:8999\" # Url for debugger. change it to your own" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "## Sample document" + "print(f\"API URL: {API_URL}\")" ] }, { @@ -37,7 +42,7 @@ "metadata": {}, "outputs": [], "source": [ - "doc_path = \"/resources/data/sample/document-01.pdf\"" + "doc_path = \"/resources/data/sample/document-03.docx\"" ] }, { @@ -58,7 +63,7 @@ " # Open the file in binary mode and send the POST request\n", " with open(file_path, \"rb\") as file:\n", " files = {\"file\": file}\n", - " response = requests.post(url=f\"{API_URL}/document-extract\", files=files)\n", + " response = requests.post(url=f\"{API_URL}/document/extract\", files=files)\n", " response.raise_for_status()\n", " return response.json()" ] @@ -71,7 +76,7 @@ "source": [ "# /document-extract endpoint output\n", "extracted_document = extract_document(doc_path)\n", - "extracted_document" + "console.print(extracted_document)" ] }, { @@ -80,7 +85,7 @@ "metadata": {}, "outputs": [], "source": [ - "len(extracted_document[\"document\"])" + "len(extracted_document[\"paragraphs\"])" ] }, { @@ -96,9 +101,15 @@ "metadata": {}, "outputs": [], "source": [ + "import uuid\n", + "\n", + "\n", "# Function to make inference using the API\n", - "def get_predictions(sample: str) -> dict:\n", - " response = requests.post(url=f\"{API_URL}/anonymizer/predict\", json={\"text\": sample})\n", + "def get_predictions(paragraph_id: uuid.UUID) -> dict:\n", + " response = requests.get(\n", + " url=f\"{API_URL}/pipeline/anonymization/paragraph/{paragraph_id}/predict\",\n", + " params={\"use_cache\": True},\n", + " )\n", " response.raise_for_status()\n", " return response.json()" ] @@ -110,7 +121,8 @@ "outputs": [], "source": [ "predictions = [\n", - " get_predictions(paragraph) for paragraph in tqdm(extracted_document[\"document\"])\n", + " get_predictions(paragraph[\"id\"])\n", + " for paragraph in tqdm(extracted_document[\"paragraphs\"])\n", "]\n", "predictions" ] @@ -121,32 +133,14 @@ "metadata": {}, "outputs": [], "source": [ - "json_prediction = json.dumps({\"data\": predictions})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with open(doc_path, \"rb\") as file:\n", - " files = {\"file\": file}\n", + "document_id = extracted_document[\"id\"]\n", + "\n", + "response = requests.get(\n", + " url=f\"{API_URL}/pipeline/anonymization/document/{document_id}/compile\",\n", + ")\n", + "response.raise_for_status()\n", + "\n", "\n", - " response = requests.post(\n", - " url=f\"{API_URL}/anonymizer/anonymize-document\",\n", - " data={\"annotations\": json_prediction},\n", - " files=files,\n", - " )\n", - " response.raise_for_status()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ "output_dir = \"output\"\n", "os.makedirs(output_dir, exist_ok=True)\n", "\n", diff --git a/notebooks/experiments/pdf-support/05-datapublic.ipynb b/notebooks/experiments/pdf-support/05-datapublic.ipynb index 214d515..3177272 100644 --- a/notebooks/experiments/pdf-support/05-datapublic.ipynb +++ b/notebooks/experiments/pdf-support/05-datapublic.ipynb @@ -55,7 +55,7 @@ " # Open the file in binary mode and send the POST request\n", " with open(file_path, \"rb\") as file:\n", " files = {\"file\": file}\n", - " response = requests.post(url=f\"{API_URL}/document-extract\", files=files)\n", + " response = requests.post(url=f\"{API_URL}/document/extract\", files=files)\n", " response.raise_for_status()\n", " return response.json()" ] diff --git a/pyproject.toml b/pyproject.toml index 4485021..819a110 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -62,7 +62,6 @@ dependencies = [ # "scikit-learn==1.5.2", "jiwer==3.0.5", "datasets>=3.2.0", - "python-magic==0.4.27", "unidecode==1.3.8", "sentencepiece==0.2.0", # "flair @ git+https://github.com/flairNLP/flair@v0.13.1", @@ -113,6 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