diff --git a/.gitignore b/.gitignore index 1267874..d92c080 100644 --- a/.gitignore +++ b/.gitignore @@ -161,3 +161,6 @@ cython_debug/ # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ poetry.lock + +# VS Code +.vscode/ \ No newline at end of file diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..c8cfe39 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.10 diff --git a/Dockerfile b/Dockerfile index 229b209..94439a3 100644 --- a/Dockerfile +++ b/Dockerfile @@ -46,7 +46,7 @@ RUN CHROMEDRIVER_VERSION=$(curl -sS chromedriver.storage.googleapis.com/LATEST_R # COPY --from=builder /usr/local/bin/chromedriver /usr/local/bin/chromedriver # Install PyTorch and related packages -RUN pip3 install --no-cache-dir torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 +RUN pip3 install --no-cache-dir torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124 # Set up working directory and copy application code COPY . /app @@ -55,7 +55,7 @@ WORKDIR /app # Install Python package (assuming it has a setup.py) RUN pip3 install --no-cache-dir -e . -RUN pip install transformers==4.41.2 +RUN pip install transformers==4.50.3 # Set environment variables ENV CHROME_BIN=/usr/bin/google-chrome \ diff --git a/README.md b/README.md index 19b7445..e97e9ed 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ > [!IMPORTANT] > ->OmniParse is a platform that ingests and parses any unstructured data into structured, actionable data optimized for GenAI (LLM) applications. Whether you are working with documents, tables, images, videos, audio files, or web pages, OmniParse prepares your data to be clean, structured, and ready for AI applications such as RAG, fine-tuning, and more +> OmniParse is a platform that ingests and parses any unstructured data into structured, actionable data optimized for GenAI (LLM) applications. Whether you are working with documents, tables, images, videos, audio files, or web pages, OmniParse prepares your data to be clean, structured, and ready for AI applications such as RAG, fine-tuning, and more ## Try it out [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/adithya-s-k/omniparse/blob/main/examples/OmniParse_GoogleColab.ipynb) @@ -50,13 +50,15 @@ conda activate omniparse-venv Install Dependencies: ```bash -poetry install +uv sync # or pip install -e . # or pip install -r pyproject.toml ``` +If using `uv`, activate the venv created by `uv sync`. + ### 🛳️ Docker To use OmniParse with Docker, execute the following commands: @@ -91,7 +93,7 @@ Run the Server: python server.py --host 0.0.0.0 --port 8000 --documents --media --web ``` -- `--documents`: Load in all the models that help you parse and ingest documents (Surya OCR series of models and Florence-2). +- `--documents`: Load in all the models that help you parse and ingest documents (Docling models and Florence-2). - `--media`: Load in Whisper model to transcribe audio and video files. - `--web`: Set up selenium crawler. @@ -102,7 +104,7 @@ If you want to download the models before starting the server python download.py --documents --media --web ``` -- `--documents`: Load in all the models that help you parse and ingest documents (Surya OCR series of models and Florence-2). +- `--documents`: Load in all the models that help you parse and ingest documents (Docling models and Florence-2). - `--media`: Load in Whisper model to transcribe audio and video files. - `--web`: Set up selenium crawler. @@ -283,7 +285,6 @@ Arguments: 🛠️ One magic API: just feed in your file prompt what you want, and we will take care of the rest 🔧 Dynamic model selection and support for external APIs 📄 Batch processing for handling multiple files at once -📦 New open-source model to replace Surya OCR and Marker **Final goal**: replace all the different models currently being used with a single MultiModel Model to parse any type of data and get the data you need. @@ -294,7 +295,7 @@ There is a need for a GPU with 8~10 GB minimum VRAM as we are using deep learnin Document Parsing Limitations \ -- [Marker](https://github.com/VikParuchuri/marker) which is the underlying PDF parser will not convert 100% of equations to LaTeX because it has to detect and then convert them. +- [Docling](https://github.com/docling-project/docling) is the underlying PDF parser and its limitations will apply. - It is good at parsing english but might struggle for languages such as Chinese - Tables are not always formatted 100% correctly; text can be in the wrong column. - Whitespace and indentations are not always respected. @@ -304,19 +305,13 @@ Document Parsing Limitations ## License OmniParse is licensed under the GPL-3.0 license. See `LICENSE` for more information. -The project uses Marker under the hood, which has a commercial license that needs to be followed. Here are the details: - -### Commercial Usage -Marker and Surya OCR Models are designed to be as widely accessible as possible while still funding development and training costs. Research and personal usage are always allowed, but there are some restrictions on commercial usage. -The weights for the models are licensed under cc-by-nc-sa-4.0. However, this restriction is waived for any organization with less than $5M USD in gross revenue in the most recent 12-month period AND less than $5M in lifetime VC/angel funding raised. To remove the GPL license requirements (dual-license) and/or use the weights commercially over the revenue limit, check out the options provided. -Please refer to [Marker](https://github.com/VikParuchuri/marker) for more Information about the License of the Model weights ## Acknowledgements -This project builds upon the remarkable [Marker](https://github.com/VikParuchuri/marker) project created by [Vik Paruchuri](https://twitter.com/VikParuchuri). We express our gratitude for the inspiration and foundation provided by this project. Special thanks to [Surya-OCR](https://github.com/VikParuchuri/surya) and [Texify](https://github.com/VikParuchuri/texify) for the OCR models extensively used in this project, and to [Crawl4AI](https://github.com/unclecode/crawl4ai) for their contributions. +This project builds upon the remarkable [Docling](https://github.com/docling-project/docling) project. We express our gratitude for the inspiration and foundation provided by this project. Special thanks to [Crawl4AI](https://github.com/unclecode/crawl4ai) for their contributions. Models being used: -- Surya OCR, Detect, Layout, Order, and Texify +- Docling IBM models - Florence-2 base - Whisper Small diff --git a/docs/README.md b/docs/README.md index c5f892b..0a77b07 100644 --- a/docs/README.md +++ b/docs/README.md @@ -105,7 +105,7 @@ Run the Server: python server.py --host 0.0.0.0 --port 8000 --documents --media --web ``` -* `--documents`: Load in all the models that help you parse and ingest documents (Surya OCR series of models and Florence-2). +* `--documents`: Load in all the models that help you parse and ingest documents (Docling models and Florence-2). * `--media`: Load in Whisper model to transcribe audio and video files. * `--web`: Set up selenium crawler. @@ -287,7 +287,6 @@ Arguments: 🛠️ One magic API: just feed in your file prompt what you want, and we will take care of the rest\ 🔧 Dynamic model selection and support for external APIs\ 📄 Batch processing for handling multiple files at once\ -📦 New open-source model to replace Surya OCR and Marker **Final goal**: replace all the different models currently being used with a single MultiModel Model to parse any type of data and get the data you need. @@ -297,13 +296,13 @@ OmniParse is licensed under the GPL-3.0 license. See `LICENSE` for more informat ### Acknowledgements -This project builds upon the remarkable [Marker](https://github.com/VikParuchuri/marker) project created by [Vik Paruchuri](https://twitter.com/VikParuchuri). We express our gratitude for the inspiration and foundation provided by this project. Special thanks to [Surya-OCR](https://github.com/VikParuchuri/surya) and [Texify](https://github.com/VikParuchuri/texify) for the OCR models extensively used in this project, and to [Crawl4AI](https://github.com/unclecode/crawl4ai) for their contributions. +This project builds upon the remarkable [Docling](https://github.com/docling-project/docling) project. We express our gratitude for the inspiration and foundation provided by this project. Special thanks to [Crawl4AI](https://github.com/unclecode/crawl4ai) for their contributions. Models being used: -* Surya OCR, Detect, Layout, Order, and Texify -* Florence-2 base -* Whisper Small +- Docling IBM models +- Florence-2 base +- Whisper Small Thank you to the authors for their contributions to these models. diff --git a/docs/deployment.md b/docs/deployment.md index 9255fcb..5e881f1 100644 --- a/docs/deployment.md +++ b/docs/deployment.md @@ -28,16 +28,4 @@ docker run --gpus all -p 8000:8000 omniparse # else docker run -p 8000:8000 omniparse -``` - -## ✈️ Skypilot(coming soon) - -SkyPilot is a framework for running LLMs, AI, and batch jobs on any cloud, offering maximum cost savings, highest GPU availability, and managed execution. To deploy Marker API using Skypilot on any cloud provider, execute the following command: - -```bash -pip install skypilot-nightly[all] - -# setup skypilot with the cloud provider our your - -sky launch skypilot.yaml -``` +``` \ No newline at end of file diff --git a/docs/installation.md b/docs/installation.md index 045c3dd..b3dcd96 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -32,6 +32,6 @@ Run the Server: python server.py --host 0.0.0.0 --port 8000 --documents --media --web ``` -* `--documents`: Load in all the models that help you parse and ingest documents (Surya OCR series of models and Florence-2). +* `--documents`: Load in all the models that help you parse and ingest documents (Docling models and Florence-2). * `--media`: Load in Whisper model to transcribe audio and video files. * `--web`: Set up selenium crawler. diff --git a/examples/OmniParse_GoogleColab.ipynb b/examples/OmniParse_GoogleColab.ipynb index f187d1e..91cdd93 100644 --- a/examples/OmniParse_GoogleColab.ipynb +++ b/examples/OmniParse_GoogleColab.ipynb @@ -32,7 +32,6 @@ "🛠️ One magic API: just feed in your file prompt what you want, and we will take care of the rest \n", "🔧 Dynamic model selection and support for external APIs \n", "📄 Batch processing for handling multiple files at once \n", - "🦙 New open-source model to replace Surya OCR and Marker \n", "\n", "**Final goal** - replace all the different models currently being used with a single MultiModel Model to parse any type of data and get the data you need\n", "\n", @@ -44,6 +43,15 @@ "| [![Original PDF](https://github.com/adithya-s-k/marker-api/raw/master/data/images/original\\_pdf.png)](https://github.com/adithya-s-k/marker-api/blob/master/data/images/original\\_pdf.png) | [![OmniParse-API](https://github.com/adithya-s-k/marker-api/raw/master/data/images/marker\\_api.png)](https://github.com/adithya-s-k/marker-api/blob/master/data/images/marker\\_api.png) | [![PyPDF](https://github.com/adithya-s-k/marker-api/raw/master/data/images/pypdf.png)](https://github.com/adithya-s-k/marker-api/blob/master/data/images/pypdf.png) |" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install uv" + ] + }, { "cell_type": "code", "execution_count": null, @@ -73,7 +81,7 @@ "## Install dependencies\n", "## if you get a restart session warning you can ignore it\n", "\n", - "%pip install -e ." + "!uv -q sync" ] }, { @@ -82,7 +90,9 @@ "metadata": {}, "outputs": [], "source": [ - "%pip install transformers==4.41.2" + "## Matplotlib backend error might occur, so we install matplotlib-inline\n", + "\n", + "!uv add matplotlib-inline" ] }, { @@ -169,7 +179,6 @@ "import threading\n", "import time\n", "import socket\n", - "import urllib.request\n", "\n", "def iframe_thread(port):\n", " while True:\n", @@ -186,12 +195,11 @@ " l = line.decode()\n", " if \"trycloudflare.com \" in l:\n", " print(\"This is the URL to access OmniPrase:\", l[l.find(\"http\"):], end='')\n", - " #print(l, end='')\n", "\n", "\n", "threading.Thread(target=iframe_thread, daemon=True, args=(8000,)).start()\n", "\n", - "!python server.py --host 127.0.0.1 --port 8000 --documents --media --web" + "!uv run server.py --host 127.0.0.1 --port 8000 --documents --media --web" ] }, { @@ -239,7 +247,7 @@ "\n", "threading.Thread(target=iframe_thread, daemon=True, args=(8000,)).start()\n", "\n", - "!python server.py --host 127.0.0.1 --port 8000 --documents --media --web" + "!uv run server.py --host 127.0.0.1 --port 8000 --documents --media --web" ] } ], diff --git a/omniparse/__init__.py b/omniparse/__init__.py index 3d1d919..c2296fe 100644 --- a/omniparse/__init__.py +++ b/omniparse/__init__.py @@ -1,20 +1,17 @@ """ Title: OmniPrase Author: Adithya S Kolavi -Date: 2024-07-02 +Date: 2025-05-23 -This code includes portions of code from the marker repository by VikParuchuri. -Original repository: https://github.com/VikParuchuri/marker +This code includes portions of code from the Docling repository. +Original repository: https://github.com/docling-project/docling -Original Author: VikParuchuri -Original Date: 2024-01-15 - -License: GNU General Public License (GPL) Version 3 -URL: https://github.com/VikParuchuri/marker/blob/master/LICENSE +License: MIT +URL: https://github.com/docling-project/docling/blob/main/LICENSE Description: -This section of the code was adapted from the marker repository to load all the OCR, layout and reading order detection models. -All credits for the original implementation go to VikParuchuri. +This section of the code was adapted from the Docling repository to enhance text pdf/word/ppt parsing. +All credits for the original implementation go to Docling. """ import torch @@ -24,12 +21,17 @@ import whisper from omniparse.utils import print_omniparse_text_art from omniparse.web.web_crawler import WebCrawler -from marker.models import load_all_models -# from omniparse.documents.models import load_all_models +from docling.utils.model_downloader import download_models +from docling.document_converter import ( + DocumentConverter, + PdfFormatOption, +) +from docling.datamodel.base_models import InputFormat +from docling.datamodel.pipeline_options import PdfPipelineOptions class SharedState(BaseModel): - model_list: Any = None + docling_converter: Any = None vision_model: Any = None vision_processor: Any = None whisper_model: Any = None @@ -38,6 +40,12 @@ class SharedState(BaseModel): shared_state = SharedState() +IMAGE_RESOLUTION_SCALE = 2.0 +pipeline_options = PdfPipelineOptions() +pipeline_options.images_scale = IMAGE_RESOLUTION_SCALE +pipeline_options.generate_page_images = True +pipeline_options.generate_picture_images = True + def load_omnimodel(load_documents: bool, load_media: bool, load_web: bool): global shared_state @@ -45,11 +53,16 @@ def load_omnimodel(load_documents: bool, load_media: bool, load_web: bool): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if load_documents: print("[LOG] ✅ Loading OCR Model") - shared_state.model_list = load_all_models() + download_models() + shared_state.docling_converter = DocumentConverter( + format_options={ + InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options) + } + ) print("[LOG] ✅ Loading Vision Model") # if device == "cuda": shared_state.vision_model = AutoModelForCausalLM.from_pretrained( - "microsoft/Florence-2-base", trust_remote_code=True + "microsoft/Florence-2-base", torch_dtype=torch.float32, trust_remote_code=True ).to(device) shared_state.vision_processor = AutoProcessor.from_pretrained( "microsoft/Florence-2-base", trust_remote_code=True diff --git a/omniparse/demo.py b/omniparse/demo.py index cf88ec8..548af2f 100644 --- a/omniparse/demo.py +++ b/omniparse/demo.py @@ -133,7 +133,7 @@ python server.py --host 0.0.0.0 --port 8000 --documents --media --web ```   -- `--documents`: Load in all the models that help you parse and ingest documents (Surya OCR series of models and Florence-2). +- `--documents`: Load in all the models that help you parse and ingest documents (Docling models and Florence-2). - `--media`: Load in Whisper model to transcribe audio and video files. - `--web`: Set up selenium crawler. diff --git a/omniparse/documents/__init__.py b/omniparse/documents/__init__.py index 6eec55b..e69de29 100644 --- a/omniparse/documents/__init__.py +++ b/omniparse/documents/__init__.py @@ -1,177 +0,0 @@ -""" -Title: OmniPrase -Author: Adithya S Kolavi -Date: 2024-07-02 - -This code includes portions of code from the marker repository by VikParuchuri. -Original repository: https://github.com/VikParuchuri/marker - -Original Author: VikParuchuri -Original Date: 2024-01-15 - -License: GNU General Public License (GPL) Version 3 -URL: https://github.com/VikParuchuri/marker/blob/master/LICENSE - -Description: -This section of the code was adapted from the marker repository to enhance text pdf/word/ppt parsing. -All credits for the original implementation go to VikParuchuri. -""" - -import os -import tempfile -import subprocess - -# from omniparse.documents.parse import parse_single_pdf -from marker.convert import convert_single_pdf -from omniparse.utils import encode_images -from omniparse.models import responseDocument - - -# Function to handle PDF parsing -def parse_pdf(input_data, model_state) -> responseDocument: - try: - if isinstance(input_data, bytes): - with tempfile.NamedTemporaryFile( - delete=False, suffix=".pdf" - ) as temp_pdf_file: - temp_pdf_file.write(input_data) - temp_pdf_path = temp_pdf_file.name - - input_path = temp_pdf_path - cleanup_tempfile = True - - elif isinstance(input_data, str) and input_data.endswith(".pdf"): - input_path = input_data - cleanup_tempfile = False - - else: - raise ValueError( - "Invalid input data format. Expected bytes or PDF file path." - ) - - full_text, images, out_meta = convert_single_pdf( - input_path, model_state.model_list - ) - - parse_pdf_result = responseDocument(text=full_text, metadata=out_meta) - encode_images(images, parse_pdf_result) - - if cleanup_tempfile: - os.remove(input_path) - - return parse_pdf_result - - except Exception as e: - raise RuntimeError(f"Error parsing PPT: {str(e)}") - - -# Function to handle PPT and DOC parsing -def parse_ppt(input_data, model_state) -> responseDocument: - try: - if isinstance(input_data, bytes): - print("Recieved ppt file") - with tempfile.NamedTemporaryFile(delete=False) as tmp_file: - tmp_file.write(input_data) - tmp_file.flush() - input_path = tmp_file.name - - elif isinstance(input_data, str) and ( - input_data.endswith(".ppt") - or input_data.endswith(".pptx") - or input_data.endswith(".doc") - or input_data.endswith(".docx") - ): - input_path = input_data - - else: - raise ValueError( - "Invalid input data format. Expected bytes or PPT/DOC file path." - ) - - if input_path.endswith((".ppt", ".pptx", ".doc", ".docx")): - output_dir = tempfile.mkdtemp() - command = [ - "libreoffice", - "--headless", - "--convert-to", - "pdf", - "--outdir", - output_dir, - input_path, - ] - subprocess.run(command, check=True) - output_pdf_path = os.path.join( - output_dir, os.path.splitext(os.path.basename(input_path))[0] + ".pdf" - ) - input_path = output_pdf_path - - full_text, images, out_meta = convert_single_pdf( - input_path, model_state.model_list - ) - images = encode_images(images) - - parse_ppt_result = responseDocument(text=full_text, metadata=out_meta) - encode_images(images, parse_ppt_result) - - if input_data != input_path: - os.remove(input_path) - - return parse_ppt_result - - except Exception as e: - raise RuntimeError(f"Error parsing PPT: {str(e)}") - - -def parse_doc(input_data, model_state) -> responseDocument: - try: - if isinstance(input_data, bytes): - with tempfile.NamedTemporaryFile(delete=False) as tmp_file: - tmp_file.write(input_data) - tmp_file.flush() - input_path = tmp_file.name - - elif isinstance(input_data, str) and ( - input_data.endswith(".ppt") - or input_data.endswith(".pptx") - or input_data.endswith(".doc") - or input_data.endswith(".docx") - ): - input_path = input_data - - else: - raise ValueError( - "Invalid input data format. Expected bytes or PPT/DOC file path." - ) - - if input_path.endswith((".ppt", ".pptx", ".doc", ".docx")): - output_dir = tempfile.mkdtemp() - command = [ - "libreoffice", - "--headless", - "--convert-to", - "pdf", - "--outdir", - output_dir, - input_path, - ] - subprocess.run(command, check=True) - output_pdf_path = os.path.join( - output_dir, os.path.splitext(os.path.basename(input_path))[0] + ".pdf" - ) - input_path = output_pdf_path - - full_text, images, out_meta = convert_single_pdf( - input_path, model_state.model_list - ) - images = encode_images(images) - - parse_doc_result = responseDocument(text=full_text, metadata=out_meta) - encode_images(images, parse_doc_result) - - if input_data != input_path: - os.remove(input_path) - - return parse_doc_result - - except Exception as e: - raise RuntimeError(f"Error parsing PPT: {str(e)}") diff --git a/omniparse/documents/router.py b/omniparse/documents/router.py index 202de53..9a01760 100644 --- a/omniparse/documents/router.py +++ b/omniparse/documents/router.py @@ -1,36 +1,32 @@ """ Title: OmniPrase Author: Adithya S Kolavi -Date: 2024-07-02 +Date: 2025-05-23 -This code includes portions of code from the marker repository by VikParuchuri. -Original repository: https://github.com/VikParuchuri/marker +This code includes portions of code from the Docling repository. +Original repository: https://github.com/docling-project/docling -Original Author: VikParuchuri -Original Date: 2024-01-15 - -License: GNU General Public License (GPL) Version 3 -URL: https://github.com/VikParuchuri/marker/blob/master/LICENSE +License: MIT +URL: https://github.com/docling-project/docling/blob/main/LICENSE Description: -This section of the code was adapted from the marker repository to enhance text pdf/word/ppt parsing. -All credits for the original implementation go to VikParuchuri. +This section of the code was adapted from the Docling repository to enhance text pdf/word/ppt parsing. +All credits for the original implementation go to Docling. """ import os import tempfile import subprocess +from io import BytesIO +from pathlib import Path -# from omniparse.documents.parse import parse_single_pdf from fastapi import APIRouter, File, UploadFile, HTTPException from fastapi.responses import JSONResponse from omniparse import get_shared_state -# from omniparse.documents import parse_pdf , parse_ppt , parse_doc -# from omniparse.documents import parse_pdf -from marker.convert import convert_single_pdf from omniparse.utils import encode_images from omniparse.models import responseDocument +from docling.datamodel.base_models import DocumentStream document_router = APIRouter() model_state = get_shared_state() @@ -41,13 +37,20 @@ async def parse_pdf_endpoint(file: UploadFile = File(...)): try: file_bytes = await file.read() - full_text, images, out_meta = convert_single_pdf( - file_bytes, model_state.model_list - ) + source = DocumentStream(name=file.filename, stream=BytesIO(file_bytes)) + filetype = os.path.splitext(file.filename)[1].lstrip('.').upper() + out_meta = {"filename": file.filename, "filetype": filetype} + + docling_result = model_state.docling_converter.convert(source) + full_text = docling_result.document.export_to_markdown() + + out_meta["block_stats"] = { + "images": len(docling_result.document.pictures), + "tables": len(docling_result.document.tables), + } result = responseDocument(text=full_text, metadata=out_meta) - encode_images(images, result) - # result : responseDocument = convert_single_pdf(file_bytes , model_state.model_list) + encode_images(file.filename, docling_result.document, result) return JSONResponse(content=result.model_dump()) @@ -82,14 +85,24 @@ async def parse_ppt_endpoint(file: UploadFile = File(...)): with open(output_pdf_path, "rb") as pdf_file: pdf_bytes = pdf_file.read() - full_text, images, out_meta = convert_single_pdf(pdf_bytes, model_state.model_list) + source = DocumentStream(name=file.filename, stream=BytesIO(pdf_bytes)) + filetype = os.path.splitext(file.filename)[1].lstrip('.').upper() + out_meta = {"filename": file.filename, "filetype": filetype} + + docling_result = model_state.docling_converter.convert(source) + full_text = docling_result.document.export_to_markdown() + + out_meta["block_stats"] = { + "images": len(docling_result.document.pictures), + "tables": len(docling_result.document.tables), + } os.remove(input_path) os.remove(output_pdf_path) os.rmdir(output_dir) result = responseDocument(text=full_text, metadata=out_meta) - encode_images(images, result) + encode_images(file.filename, docling_result.document, result) return JSONResponse(content=result.model_dump()) @@ -120,10 +133,20 @@ async def parse_doc_endpoint(file: UploadFile = File(...)): with open(output_pdf_path, "rb") as pdf_file: pdf_bytes = pdf_file.read() - full_text, images, out_meta = convert_single_pdf(pdf_bytes, model_state.model_list) + source = DocumentStream(name=file.filename, stream=BytesIO(pdf_bytes)) + filetype = os.path.splitext(file.filename)[1].lstrip('.').upper() + out_meta = {"filename": file.filename, "filetype": filetype} + + docling_result = model_state.docling_converter.convert(source) + full_text = docling_result.document.export_to_markdown() + + out_meta["block_stats"] = { + "images": len(docling_result.document.pictures), + "tables": len(docling_result.document.tables), + } result = responseDocument(text=full_text, metadata=out_meta) - encode_images(images, result) + encode_images(file.filename, docling_result.document, result) return JSONResponse(content=result.model_dump()) @@ -163,36 +186,22 @@ async def parse_any_endpoint(file: UploadFile = File(...)): ) input_path = output_pdf_path - # Common parsing logic - full_text, images, out_meta = convert_single_pdf(input_path, model_state.model_list) + docling_result = model_state.docling_converter.convert(Path(input_path)) + full_text = docling_result.document.export_to_markdown() + + filetype = os.path.splitext(file.filename)[1].lstrip('.').upper() + out_meta = { + "filename": file.filename, + "filetype": filetype, + "block_stats": { + "images": len(docling_result.document.pictures), + "tables": len(docling_result.document.tables), + }, + } os.remove(input_path) result = responseDocument(text=full_text, metadata=out_meta) - encode_images(images, result) + encode_images(file.filename, docling_result.document, result) return JSONResponse(content=result.model_dump()) - - -# @document_router.post("/docs") -# async def parse_docs_endpoint(file: UploadFile = File(...)): -# try: - -# file_bytes = await file.read() -# result = parse_doc(file_bytes , model_state) - -# return JSONResponse(content=result) - -# except Exception as e: -# raise HTTPException(status_code=500, detail=str(e)) - -# @document_router.post("/ppt") -# async def parse_ppt_endpoint(file: UploadFile = File(...)): -# try: -# file_bytes = await file.read() -# result = parse_ppt(file_bytes , model_state) - -# return JSONResponse(content=result) - -# except Exception as e: -# raise HTTPException(status_code=500, detail=str(e)) diff --git a/omniparse/image/__init__.py b/omniparse/image/__init__.py index 4249443..5af7e8d 100644 --- a/omniparse/image/__init__.py +++ b/omniparse/image/__init__.py @@ -1,20 +1,17 @@ """ Title: OmniPrase Author: Adithya S Kolavi -Date: 2024-07-02 - -This code includes portions of code from the marker repository by VikParuchuri. -Original repository: https://github.com/VikParuchuri/marker +Date: 2025-05-23 -Original Author: VikParuchuri -Original Date: 2024-01-15 +This code includes portions of code from the Docling repository. +Original repository: https://github.com/docling-project/docling -License: GNU General Public License (GPL) Version 3 -URL: https://github.com/VikParuchuri/marker/blob/master/LICENSE +License: MIT +URL: https://github.com/docling-project/docling/blob/main/LICENSE Description: -This section of the code was adapted from the marker repository to enhance text image parsing. -All credits for the original implementation go to VikParuchuri. +This section of the code was adapted from the Docling repository to enhance text pdf/word/ppt parsing. +All credits for the original implementation go to Docling. """ """ @@ -38,15 +35,14 @@ import tempfile import img2pdf from PIL import Image +from pathlib import Path -# from omniparse.document.parse import parse_single_image -from marker.convert import convert_single_pdf from omniparse.image.process import process_image_task from omniparse.utils import encode_images from omniparse.models import responseDocument -def parse_image(input_data, model_state) -> dict: +def parse_image(image_name, input_data, model_state) -> dict: temp_files = [] try: @@ -87,13 +83,20 @@ def parse_image(input_data, model_state) -> dict: temp_pdf_path = temp_pdf_file.name temp_files.append(temp_pdf_path) - # Parse the PDF file - full_text, images, out_meta = convert_single_pdf( - temp_pdf_path, model_state.model_list - ) + docling_result = model_state.docling_converter.convert(Path(temp_pdf_path)) + full_text = docling_result.document.export_to_markdown() + + out_meta = { + "filename": image_name, + "filetype": image.format.lower() if image.format else "image", + "block_stats": { + "images": len(docling_result.document.pictures), + "tables": len(docling_result.document.tables), + }, + } parse_image_result = responseDocument(text=full_text, metadata=out_meta) - encode_images(images, parse_image_result) + encode_images(image_name, docling_result.document, parse_image_result) return parse_image_result diff --git a/omniparse/image/router.py b/omniparse/image/router.py index c318cf3..1c7a38b 100644 --- a/omniparse/image/router.py +++ b/omniparse/image/router.py @@ -12,7 +12,7 @@ async def parse_image_endpoint(file: UploadFile = File(...)): try: file_bytes = await file.read() - result: responseDocument = parse_image(file_bytes, model_state) + result: responseDocument = parse_image(file.filename, file_bytes, model_state) return JSONResponse(content=result.model_dump()) except Exception as e: diff --git a/omniparse/media/__init__.py b/omniparse/media/__init__.py index c92b47f..5f9777f 100644 --- a/omniparse/media/__init__.py +++ b/omniparse/media/__init__.py @@ -19,9 +19,7 @@ import os import tempfile -from fastapi import UploadFile -from fastapi.responses import JSONResponse -from moviepy.editor import VideoFileClip +from moviepy import VideoFileClip from omniparse.models import responseDocument from omniparse.media.utils import WHISPER_DEFAULT_SETTINGS from omniparse.media.utils import transcribe # Assuming transcribe function is imported diff --git a/omniparse/utils.py b/omniparse/utils.py index ad4baa3..530552d 100644 --- a/omniparse/utils.py +++ b/omniparse/utils.py @@ -1,24 +1,24 @@ import base64 +import tempfile import os from art import text2art from omniparse.models import responseDocument -def encode_images(images, inputDocument: responseDocument): - for i, (filename, image) in enumerate(images.items()): - # print(f"Processing image {filename}") - # Save image as PNG - image.save(filename, "PNG") - # Read the saved image file as bytes - with open(filename, "rb") as f: - image_bytes = f.read() - # Convert image to base64 - image_base64 = base64.b64encode(image_bytes).decode("utf-8") +def encode_images(document_name, document, inputDocument: responseDocument): + file_name = os.path.splitext(document_name)[0] + for idx, image in enumerate(document.pictures): + with tempfile.NamedTemporaryFile(delete=True, suffix=".png") as tmp_file: + filename = tmp_file.name + image.get_image(document).save(filename, "PNG") + with open(filename, "rb") as f: + image_bytes = f.read() - inputDocument.add_image(image_name=filename, image_data=image_base64) - - # Remove the temporary image file - os.remove(filename) + # Convert image to base64 + image_base64 = base64.b64encode(image_bytes).decode("utf-8") + inputDocument.add_image( + image_name=f"{file_name}_image_{idx}", image_data=image_base64 + ) def print_omniparse_text_art(suffix=None): diff --git a/pyproject.toml b/pyproject.toml index 9f9eed0..c48972c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,57 +1,46 @@ -[tool.poetry] +[project] name = "omniparse" -version = "0.0.1" +version = "0.0.2" description = "API to convert Documents | Media | Webpage into Structured Markdown for LLM usecases" -authors = ["Adithya S K "] -license = "Apache" +authors = [ + {name = "Adithya S K", email = "adithyaskolavi@gmail.com"} +] +license = {file = "LICENSE"} readme = "README.md" keywords = ["pdf", "markdown", "ocr", "parse"] -include = [ - "server.py", -] -[tool.poetry.dependencies] -python = "^3.10" -scikit-learn = "^1.3.2" -Pillow = "^10.1.0" -pydantic = "^2.4.2" -pydantic-settings = "^2.0.3" -transformers = "^4.41.2" -numpy = "^1.26.1" -python-dotenv = "^1.0.0" -torch = "^2.2.2" # Issue with torch 2.3.0 and vision models - https://github.com/pytorch/pytorch/issues/121834 -tqdm = "^4.66.1" -tabulate = "^0.9.0" -ftfy = "^6.1.1" -texify = "^0.1.8" -rapidfuzz = "^3.8.1" -surya-ocr = "^0.4.3" -filetype = "^1.2.0" -regex = "^2024.4.28" -pdftext = "^0.3.10" -grpcio = "^1.63.0" -fastapi = "^0.111.0" -uvicorn = "^0.29.0" -pypdfium2 = 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