diff --git a/README.md b/README.md index af32af27d999e00124397ff24287b52fabec98f5..496544618a87fded2058fcff5ace398c26d60934 100644 --- a/README.md +++ b/README.md @@ -17,4 +17,6 @@ datasets: "Riksarkivet/placeholder_line_segmentation", "Riksarkivet/placeholder_htr", ] +license: mit +short_description: Transform research papers and mathematical concepts into stu --- diff --git a/app/assets/__init__.py b/app/assets/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/app/assets/examples.py b/app/assets/examples.py deleted file mode 100644 index 26cb6e0be4ddfa7c2698c0956c3fcaa5933876ce..0000000000000000000000000000000000000000 --- a/app/assets/examples.py +++ /dev/null @@ -1,27 +0,0 @@ -import io - -import datasets -from PIL import Image - - -class DemoImages: - def __init__(self, url="Riksarkivet/test_images_demo", cache_dir=".app/assets/images/.cache_images"): - if not hasattr(self, "images_datasets"): - self.images_datasets = datasets.load_dataset(url, cache_dir=cache_dir, split="train") - self.example_df = self.images_datasets.to_pandas() - self.examples_list = self.convert_bytes_to_images() - - def convert_bytes_to_images(self): - examples_list = [] - for index, row in self.example_df.iterrows(): - image_bytes = row["image"]["bytes"] - image = Image.open(io.BytesIO(image_bytes)) - - path_to_image = f"./app/assets/images/image_{index}.jpg" - image.save(path_to_image) - - description = row["text"] - - examples_list.append([description, "Nested segmentation", path_to_image]) - - return examples_list diff --git a/app/assets/images/.gitkeep b/app/assets/images/.gitkeep deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/app/assets/templates/1_simple.yaml b/app/assets/templates/1_simple.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1f9914386c8e54db698558835d9b8a337555c13e --- /dev/null +++ b/app/assets/templates/1_simple.yaml @@ -0,0 +1,16 @@ +steps: +- step: Segmentation + settings: + model: yolo + model_settings: + model: Riksarkivet/yolov9-lines-within-regions-1 +- step: TextRecognition + settings: + model: TrOCR + model_settings: + model: Riksarkivet/trocr-base-handwritten-hist-swe-2 +- step: OrderLines +- step: Export + settings: + format: txt + dest: outputs \ No newline at end of file diff --git a/app/assets/templates/2_nested.yaml b/app/assets/templates/2_nested.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bc04739f004a7711212a04b6aba73940c8e3b0c2 --- /dev/null +++ b/app/assets/templates/2_nested.yaml @@ -0,0 +1,22 @@ +steps: +- step: Segmentation + settings: + model: yolo + model_settings: + model: Riksarkivet/yolov9-regions-1 +- step: Segmentation + settings: + model: yolo + model_settings: + model: Riksarkivet/yolov9-lines-within-regions-1 +- step: TextRecognition + settings: + model: TrOCR + model_settings: + model: Riksarkivet/trocr-base-handwritten-hist-swe-2 +- step: OrderLines +- step: Export + settings: + format: txt + dest: text-outputs + diff --git a/app/assets/templates/c_nested_labels.yaml b/app/assets/templates/c_nested_labels.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e91f469cd8f61bffe678cfae895f522aac86ef98 --- /dev/null +++ b/app/assets/templates/c_nested_labels.yaml @@ -0,0 +1,41 @@ +steps: +- step: Segmentation + settings: + model: yolo + model_settings: + model: Riksarkivet/yolov9-regions-1 + generation_settings: + batch_size: 2 +- step: Segmentation + settings: + model: yolo + model_settings: + model: Riksarkivet/yolov9-lines-within-regions-1 + generation_settings: + batch_size: 2 +- step: TextRecognition + settings: + model: WordLevelTrocr + model_settings: + model: Riksarkivet/trocr-base-handwritten-hist-swe-2 + generation_settings: + batch_size: 4 + num_beams: 1 +- step: ReadingOrderMarginalia + settings: + two_page: auto +- step: Export + settings: + dest: outputs/alto + format: alto +- step: Export + settings: + dest: outputs/page + format: page +labels: + level_labels: + - region + - line + - word + sep: _ + template: "{label}{number}" \ No newline at end of file diff --git a/app/assets/templates/c_nested_reading_order.yaml b/app/assets/templates/c_nested_reading_order.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6176f389760cc65a6daffc9364c6dabd024f493a --- /dev/null +++ b/app/assets/templates/c_nested_reading_order.yaml @@ -0,0 +1,24 @@ +steps: +- step: Segmentation + settings: + model: yolo + model_settings: + model: Riksarkivet/yolov9-regions-1 +- step: Segmentation + settings: + model: yolo + model_settings: + model: Riksarkivet/yolov9-lines-within-regions-1 +- step: TextRecognition + settings: + model: TrOCR + model_settings: + model: Riksarkivet/trocr-base-handwritten-hist-swe-2 +- step: ReadingOrderMarginalia + settings: + two_page: always +- step: Export + settings: + format: txt + dest: text-outputs + diff --git a/app/assets/templates/c_nested_with_filter.yaml b/app/assets/templates/c_nested_with_filter.yaml new file mode 100644 index 0000000000000000000000000000000000000000..86308d2b1a155781c737d9b6509eb42970c58863 --- /dev/null +++ b/app/assets/templates/c_nested_with_filter.yaml @@ -0,0 +1,28 @@ +steps: +- step: Segmentation + settings: + model: yolo + model_settings: + model: Riksarkivet/yolov9-regions-1 +- step: Segmentation + settings: + model: yolo + model_settings: + model: Riksarkivet/yolov9-lines-within-regions-1 +- step: TextRecognition + settings: + model: TrOCR + model_settings: + model: Riksarkivet/trocr-base-handwritten-hist-swe-2 +- step: OrderLines +- step: Export + settings: + format: txt + dest: raw-outputs +- step: RemoveLowTextConfidenceLines + settings: + threshold: 0.95 +- step: Export + settings: + format: txt + dest: cleaned-outputs diff --git a/app/assets/templates/c_simple_gensettings.yaml b/app/assets/templates/c_simple_gensettings.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0af8c02aeeb966b697fa9af3cc58ae2b1435f9bd --- /dev/null +++ b/app/assets/templates/c_simple_gensettings.yaml @@ -0,0 +1,21 @@ +steps: +- step: Segmentation + settings: + model: yolo + model_settings: + model: Riksarkivet/yolov9-lines-within-regions-1 + generation_settings: + batch_size: 2 +- step: TextRecognition + settings: + model: TrOCR + model_settings: + model: Riksarkivet/trocr-base-handwritten-hist-swe-2 + generation_settings: + batch_size: 4 + num_beams: 1 +- step: OrderLines +- step: Export + settings: + format: txt + dest: outputs \ No newline at end of file diff --git a/app/assets/templates/c_simple_multi_output.yaml b/app/assets/templates/c_simple_multi_output.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7a37a5a0b0d5619bbe1a1c3d64cca297a0c4f0f1 --- /dev/null +++ b/app/assets/templates/c_simple_multi_output.yaml @@ -0,0 +1,24 @@ +steps: +- step: Segmentation + settings: + model: yolo + model_settings: + model: Riksarkivet/yolov9-lines-within-regions-1 +- step: TextRecognition + settings: + model: TrOCR + model_settings: + model: Riksarkivet/trocr-base-handwritten-hist-swe-2 +- step: OrderLines +- step: Export + settings: + format: txt + dest: text-outputs +- step: Export + settings: + format: page + dest: page-outputs +- step: Export + settings: + format: alto + dest: alto-outputs \ No newline at end of file diff --git a/app/backend.py b/app/backend.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/app/content/ENG/changelog_roadmap/changelog.md b/app/content/ENG/changelog_roadmap/changelog.md deleted file mode 100644 index 25ff1d03257c4bc5355eb9f58d648e643e8f119c..0000000000000000000000000000000000000000 --- a/app/content/ENG/changelog_roadmap/changelog.md +++ /dev/null @@ -1,14 +0,0 @@ -## Changelog - -All notable changes to HTRFLOW will be documented here. - -### [0.1.0] - 2023-11-08 - -#### Added - -- Support for TROCR -> Latin and Eng model -- New feature! Compare different runs with GT, see tab **Fast track** > **Compare** - -#### Fixed - -- Fixed bug for Docker and running app locally, [issue](https://github.com/Riksarkivet/HTRFLOW/issues/2) diff --git a/app/content/ENG/changelog_roadmap/old_changelog.md b/app/content/ENG/changelog_roadmap/old_changelog.md deleted file mode 100644 index 66a5efb4ffc380d23d751886d0ae96d47bd1ad5c..0000000000000000000000000000000000000000 --- a/app/content/ENG/changelog_roadmap/old_changelog.md +++ /dev/null @@ -1,39 +0,0 @@ -## Changelog - -All notable changes to HTRFLOW will be documented here. - -### [0.0.2] - 2023-11-01 - -#### Added - -- Better documentation for API, see **Overview** > **Duplicating for own use & API** -- Better documentation for restrictions of app, see **Overview** > **HTRFLOW** - -#### Fixed - -- Fixed bug for API, [issue](https://github.com/Riksarkivet/HTRFLOW/issues/2) - -#### Changed - -- Changed named for **FAQ & Discussion** to **FAQ & Contact** - ---- - -### [0.0.1] - 2023-10-23 - -#### Added - -- Added a new feature to **Stepwise** > **Explore results** > New Text diff and CER component - -#### Fixed - -- Fixed naming conventions of tabs in app so they are more coherent with the code. - -#### Changed - -- Changed the layout in both Fast track and Stepwise to improve the UX - - - Examples are viewed in the middle of the layout - - "Advanced settings" are initial hidden - -- Removed **help** tab for now (documentation of Fast track and Stepwise will come in a later release) diff --git a/app/content/ENG/changelog_roadmap/roadmap.md b/app/content/ENG/changelog_roadmap/roadmap.md deleted file mode 100644 index ae72432fde4b08456b599757bd1cbe64fcf6383e..0000000000000000000000000000000000000000 --- a/app/content/ENG/changelog_roadmap/roadmap.md +++ /dev/null @@ -1,27 +0,0 @@ -## Roadmap - -Our roadmap is where you can learn about what features we're working on. Have any questions or comments about items on the roadmap? See **Overview** > **FAQ & Discussion** for feedback or collaboration. - -### Working on - -- Release Training and Eval data on HuggingFace - -- Add support for TrOCR models as Text recognition model: - - - Train a TrOCR model specialized on Swedish historical handwritten text. - -- Optimize the backend of the application: - - Package the code - - Add support for batch inference - - Start a collaborative open source project - -### Backlog - -- Initial document classifier - -- Add support for Different segmentation strategies: - - - Table detection - - Different text layouts with multiple columns - -- Add more endpoints for rest api and add a more extensive documentation diff --git a/app/content/ENG/contributions/contributions.md b/app/content/ENG/contributions/contributions.md deleted file mode 100644 index bcaa71cec148b6096486831237efcc06321f334f..0000000000000000000000000000000000000000 --- a/app/content/ENG/contributions/contributions.md +++ /dev/null @@ -1,33 +0,0 @@ -## HTRFLOW – Contributions - -The AI models used in HTRFLOW is the result of a collaborative effort, involving the National Archives in both Sweden and Finland, in partnership with the Stockholm City Archives, Jämtlands läns fornskriftsällskap, citizen science volunteers and researchers from Stockholm and Uppsala Universities. - -Several datasets have been created by participants through Citizen Science using the Handwritten Text Recognition (HTR) software, Transkribus, provided by [READ-COOP SCE](https://readcoop.eu/) . - -### Archives used to train models for HTRFLOW - -[Svea hovrätt (Renskrivna protokoll), 1713–1735](https://sok.riksarkivet.se/arkiv/H2hpDbNn14scxjzdWqAaJ1) - -[Bergmästaren i Nora m fl bergslag (Hammartingsprotokoll), 1698–1765](https://sok.riksarkivet.se/arkiv/M5Fe2TT9rH6cxG02H087k3) - -[Trolldomskommissionen, mainly 1670s](https://sok.riksarkivet.se/trolldomskommissionen) - -[Bergskollegium, 1718–1758](https://sok.riksarkivet.se/arkiv/SMFky31ekQ80Qsk0UCZZE2) - -[Jämtlands domsaga, 1647–1688](https://sok.riksarkivet.se/arkiv/2l4NYFT8rH6cxG02H087k3) - -[Stockholms domkapitel, 1728–1759](https://sok.riksarkivet.se/arkiv/etg1tyeEaIPMBzKbUKTjw1) - -[Politikollegiet, 1729–1759](https://sok.riksarkivet.se/arkiv/1lQnXIDiKaYxRLBlK1dGF3) - -[Göteborgs poliskammare före 1900 (Detektiva polisens rapportböcker), 1868–1901](https://sok.riksarkivet.se/arkiv/oLTOi9yxweZJUG018W43t3) - -[Renovated Court Records, the National Archives of Finland, 1800s](https://tuomiokirjat.kansallisarkisto.fi/) - -### Ongoing research collaborations - -[Transcription node Sweden – machine interpretation and citizen research combined](https://riksarkivet.se/forskning), Swedish National Archives and University of Gothenburg, funded by the Swedish National Heritage Board. - -[Mapping the geographies of early modern mining knowledge. A digital history of the study tours of the Swedish Bureau of Mines, 1691–1826](https://www.idehist.uu.se/forskning/projekt/den-tidigmoderna-bergsvetenskapens-geografier), Uppsala University and Stockholm University, funded by the Swedish Research Council. - -The Swedish National Archives' research and development on HTR is part of the Swedish national infrastructure Huminfra. [Click here](https://riksarkivet.se/huminfra) for more information. diff --git a/app/content/ENG/contributions/huminfra_image.md b/app/content/ENG/contributions/huminfra_image.md deleted file mode 100644 index ee517d8832cb05cafc38d637c2c6b7b557bbb4ad..0000000000000000000000000000000000000000 --- a/app/content/ENG/contributions/huminfra_image.md +++ /dev/null @@ -1,3 +0,0 @@ - - - diff --git a/app/content/ENG/contributions/riksarkivet_image.md b/app/content/ENG/contributions/riksarkivet_image.md deleted file mode 100644 index 164392c652b20d71842346a85d7a08581ef68e54..0000000000000000000000000000000000000000 --- a/app/content/ENG/contributions/riksarkivet_image.md +++ /dev/null @@ -1,3 +0,0 @@ - - - diff --git a/app/content/ENG/duplicate_api/api1.md b/app/content/ENG/duplicate_api/api1.md deleted file mode 100644 index 3ddd3e280ca20da62f3dbc5e06019700e5f05506..0000000000000000000000000000000000000000 --- a/app/content/ENG/duplicate_api/api1.md +++ /dev/null @@ -1,11 +0,0 @@ -## Usage of Client API - -If you prefer to run **Fast track** programmatically, we offer an API for that purpose. - -- [REST API](https://riksarkivet-htr-demo.hf.space/?view=api) -- Docuemtnation for gradio client with [python](https://www.gradio.app/guides/getting-started-with-the-python-client) -- Docuemtnation for gradio client with [javascript](https://www.gradio.app/guides/getting-started-with-the-js-client) - -**Note**: More extensive APIs and documentation can be added in the future upon request. - -See example below for usage of API in python: diff --git a/app/content/ENG/duplicate_api/api2.md b/app/content/ENG/duplicate_api/api2.md deleted file mode 100644 index c6d5882a5baf20097130fd64222327e9bc2f9845..0000000000000000000000000000000000000000 --- a/app/content/ENG/duplicate_api/api2.md +++ /dev/null @@ -1,3 +0,0 @@ -### Output from the api - -The output from the api is currently in the format of Page XML, which can be imported into this [viewer](https://huggingface.co/spaces/Riksarkivet/Viewer_demo). diff --git a/app/content/ENG/duplicate_api/api_code1.md b/app/content/ENG/duplicate_api/api_code1.md deleted file mode 100644 index 347bd71681cb6466b70c7c7547bdfc44a8e38d0c..0000000000000000000000000000000000000000 --- a/app/content/ENG/duplicate_api/api_code1.md +++ /dev/null @@ -1,11 +0,0 @@ -from gradio_client import Client # pip install gradio_client - -# Change url to your client (localhost: http://127.0.0.1:7860/) - -client = Client("https://huggingface.co/spaces/Riksarkivet/htr_demo") -job = client.submit( -"https://your.image.url.or.pah.jpg", -api_name="/run_htr_pipeline", -) - -print(job.result()) diff --git a/app/content/ENG/duplicate_api/api_code2.md b/app/content/ENG/duplicate_api/api_code2.md deleted file mode 100644 index 4de17d6784413aa343484e91de1048351258611d..0000000000000000000000000000000000000000 --- a/app/content/ENG/duplicate_api/api_code2.md +++ /dev/null @@ -1,26 +0,0 @@ -Loaded as API: http://127.0.0.1:7860/ ✔ - - - - - Swedish National Archives - 2023-08-21, 13:28:06 - - - - - - - - År 1865. - - - - - -...................................... - - - - -# Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings... diff --git a/app/content/ENG/duplicate_api/duplicate.md b/app/content/ENG/duplicate_api/duplicate.md deleted file mode 100644 index 9a4b3ebad9701936d1fc445c49593016c8005d14..0000000000000000000000000000000000000000 --- a/app/content/ENG/duplicate_api/duplicate.md +++ /dev/null @@ -1,26 +0,0 @@ -## Duplicating for own use - -Please be aware of certain limitations when using the application: - -- This application is primarily designed for demonstration purposes and is not intended for scaling up HTR. -- Currently, the Swedish National Archives has constraints on sharing hardware, leading to a queue system for high demand. -- The demo is hosted on Hugging Face domains, and they may rate-limit you if there's an excessive number of requests in a short timeframe, especially when using the API. - -For those requiring heavy usage, you can conveniently duplicate the application. - -- Duplicate [application](https://huggingface.co/spaces/Riksarkivet/htr_demo?duplicate=true). - -By doing so, you'll create your own private app, which allows for unlimited requests without any restrictions. The image below shows the minimum hardware you need to use if you don't have access to hardware youself: - -
- -HTR_tool -
Figure - Choose a hardware that has atleast a GPU
-
- -For individuals with access to dedicated hardware, additional options are available. You have the flexibility to run this application on your own machine utilizing Docker, or by cloning the repository directly. Doing so allows you to leverage your hardware's capabilities to their fullest extent. - -- [Clone with Docker](https://huggingface.co/spaces/Riksarkivet/htr_demo?docker=true) -- [Clone Repo](https://huggingface.co/spaces/Riksarkivet/htr_demo/settings?clone=true) - -**Note**: To take advantage of CUDA for accelerated inferences, an Nvidia graphics card is required. This setup significantly enhances the performance, ensuring a smoother and faster operation. diff --git a/app/content/ENG/faq_discussion/discussion.md b/app/content/ENG/faq_discussion/discussion.md deleted file mode 100644 index 6b5224ce4239686dd8eb5d48535bf457b62aad55..0000000000000000000000000000000000000000 --- a/app/content/ENG/faq_discussion/discussion.md +++ /dev/null @@ -1,7 +0,0 @@ -## Contact us - -If you have any questions, or suggestions for features or improvements, please don’t hesitate to contact us. - -- Open a discussion on [HuggingFace](https://huggingface.co/spaces/Riksarkivet/htr_demo/discussions). - -- Send an email to: forskning@riksarkivet.se diff --git a/app/content/ENG/faq_discussion/faq.md b/app/content/ENG/faq_discussion/faq.md deleted file mode 100644 index d7d344e343b3aa530dfdd123aa7b39d3e3991287..0000000000000000000000000000000000000000 --- a/app/content/ENG/faq_discussion/faq.md +++ /dev/null @@ -1,13 +0,0 @@ -## Frequently Asked Questions - -**Q**: Is my data secure? Can I upload my own images? -**A**: Absolutely. Uploaded files are not saved or stored. - -**Q**: Why am I always in a queue? -**A**: This is due to hardware constraints and rate limits imposed by Hugging Face. For alternative ways to use the app, refer to the tab > **Documentation** under > **Duplication for Own Use & API**. - -**Q**: Why is Fast track so slow? -**A**: The current speed is due to hardware limitations and the present state of the code. However, we plan to update the application in future releases, which will significantly improve the performance of the application. - -**Q**: Is it possible to run Fast track or the API on image batches? -**A**: Not currently, but we plan to implement this feature in the future. diff --git a/app/content/ENG/htrflow/htrflow_col1.md b/app/content/ENG/htrflow/htrflow_col1.md deleted file mode 100644 index b604c7095046efa21ed06bc3decea593b1b58070..0000000000000000000000000000000000000000 --- a/app/content/ENG/htrflow/htrflow_col1.md +++ /dev/null @@ -1,18 +0,0 @@ -## Introduction - -The Swedish National Archives introduces a demonstrational end-to-end HTR (Handwritten Text Recognition) pipeline. The pipeline consists of two instance segmentation models, one trained for segmenting text-regions within running-text document images, and another trained for segmenting text-lines within these regions. The text-lines are then transcribed by a text-recognition model trained on a vast set of swedish handwriting ranging from the 17th to the 19th century. - -## Usage - -It needs to be emphasized that this application is intended mainly for demo-purposes. Its aim is to showcase our pipeline for transcribing historical, running-text documents, not to put the pipeline into large-scale production. -**Note**: In the future we’ll optimize the code to suit a production scenario with multi-GPU, batch-inference, but this is still a work in progress.
- -For an insight into the upcoming features we are working on: - -- Navigate to the > **Overview** > **Changelog & Roadmap**. - -## Limitations - -The demo, hosted on Huggingface and assigned a T4 GPU, can only handle two users submissions at a time. If you experience long wait times or unresponsiveness, this is the reason. In the future, we plan to host this solution ourselves, with a better server for an improved user experience, optimized code, and multiple model options. Exciting developments are on the horizon! - -It's also important to note that the models work on running text, not text in table format. diff --git a/app/content/ENG/htrflow/htrflow_col2.md b/app/content/ENG/htrflow/htrflow_col2.md deleted file mode 100644 index 99607f6020e27e6880c1ad8e5763b78d6f29f5c7..0000000000000000000000000000000000000000 --- a/app/content/ENG/htrflow/htrflow_col2.md +++ /dev/null @@ -1,23 +0,0 @@ -## Source Code - -Please fork and leave a star on Github if you like it! The code for this project can be found here: - -- [Github](https://github.com/Riksarkivet/HTRFLOW) - -**Note**: We will in the future package all of the code for mass HTR (batch inference on multi-GPU setup), but the code is still work in progress. - -## Models - -The models used in this demo are very much a work in progress, and as more data, and new architectures, becomes available, they will be retrained and reevaluated. For more information about the models, please refer to their model-cards on Huggingface. - -- [Riksarkivet/rtmdet_regions](https://huggingface.co/Riksarkivet/rtmdet_regions) -- [Riksarkivet/rtmdet_lines](https://huggingface.co/Riksarkivet/rtmdet_lines) -- [Riksarkivet/satrn_htr](https://huggingface.co/https://huggingface.co/Riksarkivet/satrn_htr) - -## Datasets - -Train and testsets created by the Swedish National Archives will be released here: - -- [Riksarkivet/placeholder_region_segmentation](https://huggingface.co/datasets/Riksarkivet/placeholder_region_segmentation) -- [Riksarkivet/placeholder_line_segmentation](https://huggingface.co/datasets/Riksarkivet/placeholder_line_segmentation) -- [Riksarkivet/placeholder_htr](https://huggingface.co/datasets/Riksarkivet/placeholder_htr) diff --git a/app/content/ENG/htrflow/htrflow_row1.md b/app/content/ENG/htrflow/htrflow_row1.md deleted file mode 100644 index 2c843d67260070c26f313a933b62efce4af2ed57..0000000000000000000000000000000000000000 --- a/app/content/ENG/htrflow/htrflow_row1.md +++ /dev/null @@ -1,3 +0,0 @@ -## The Pipeline in Overview - -The steps in the pipeline can be seen below as follows: diff --git a/app/content/ENG/htrflow/htrflow_tab1.md b/app/content/ENG/htrflow/htrflow_tab1.md deleted file mode 100644 index 495ec9925b467de9fa59fae157b35ba1a8172d60..0000000000000000000000000000000000000000 --- a/app/content/ENG/htrflow/htrflow_tab1.md +++ /dev/null @@ -1,7 +0,0 @@ -### Binarization - -The reason for binarizing the images before processing them is that we want the models to generalize as well as possible. By training on only binarized images and by binarizing images before running them through the pipeline, we take the target domain closer to the training domain, and reduce negative effects of background variation, background noise etc., on the final results. The pipeline implements a simple adaptive thresholding algorithm for binarization. - -
-HTR_tool -
diff --git a/app/content/ENG/htrflow/htrflow_tab2.md b/app/content/ENG/htrflow/htrflow_tab2.md deleted file mode 100644 index dbbfb3e5b19a32ab9cab2c48693945ebd11ced1a..0000000000000000000000000000000000000000 --- a/app/content/ENG/htrflow/htrflow_tab2.md +++ /dev/null @@ -1,7 +0,0 @@ -### Text-region segmentation - -To facilitate the text-line segmentation process, it is advantageous to segment the image into text-regions beforehand. This initial step offers several benefits, including reducing variations in line spacing, eliminating blank areas on the page, establishing a clear reading order, and distinguishing marginalia from the main text. The segmentation model utilized in this process predicts both bounding boxes and masks. Although the model has the capability to predict both, only the masks are utilized for the segmentation tasks of lines and regions. An essential post-processing step involves checking for regions that are contained within other regions. During this step, only the containing region is retained, while the contained region is discarded. This ensures that the final segmented text-regions are accurate and devoid of overlapping or redundant areas. This ensures that there’s no duplicate text-regions sent to the text-recognition model. - -
-HTR_tool -
diff --git a/app/content/ENG/htrflow/htrflow_tab3.md b/app/content/ENG/htrflow/htrflow_tab3.md deleted file mode 100644 index 78bcdb9c2e4b17779a65a93b303b1f616f03b128..0000000000000000000000000000000000000000 --- a/app/content/ENG/htrflow/htrflow_tab3.md +++ /dev/null @@ -1,7 +0,0 @@ -### Text-line segmentation - -This is also an instance segmentation model, trained on extracting text-lines from the cropped text-regions. The same post-processing as in the text-region segmentation step, is done in the text-line segmentation step. - -
-HTR_tool -
diff --git a/app/content/ENG/htrflow/htrflow_tab4.md b/app/content/ENG/htrflow/htrflow_tab4.md deleted file mode 100644 index 89923a5e4c23b25546cc1dd23d813f9a1870acd6..0000000000000000000000000000000000000000 --- a/app/content/ENG/htrflow/htrflow_tab4.md +++ /dev/null @@ -1,7 +0,0 @@ -### Text Recognition - -The text-recognition model was trained on approximately one million handwritten text-line images ranging from the 17th to the 19th century. See the model card for detailed evaluation results, and results from some fine-tuning experiments. - -
-HTR_tool -
diff --git a/app/content/NOR/htrflow/htrflow_col1.md b/app/content/NOR/htrflow/htrflow_col1.md deleted file mode 100644 index ac45b8f4042e9c4f8f855aee6a3f8fe786272539..0000000000000000000000000000000000000000 --- a/app/content/NOR/htrflow/htrflow_col1.md +++ /dev/null @@ -1,18 +0,0 @@ -### Introduktion - -Riksarkivet presenterar en demonstrationspipeline för HTR (Handwritten Text Recognition). Pipelinen består av två instanssegmenteringsmodeller: en tränad för att segmentera textregioner i bilder av löpande-textdokument och en annan tränad för att segmentera textrader inom dessa regioner. Textraderna transkriberas därefter av en textigenkänningsmodell som är tränad på ett stort dataset med svensk handskrift från 1600- till 1800-talet. - -### Användning - -Det är viktigt att betona att denna applikation främst är avsedd för demonstrationsändamål. Målet är att visa upp vår pipeline för att transkribera historiska dokument med löpande text, inte att använda pipelinen i storskalig produktion. -**Obs**: I framtiden kommer vi att optimera koden för att passa ett produktionsscenario med multi-GPU och batch-inferens, men detta arbete pågår fortfarande.
- -För en inblick i de kommande funktionerna vi arbetar med: - -- Navigera till > **Översikt** > **Ändringslogg och roadmap**. - -### Begränsningar - -Demon, som är värd på Huggingface och tilldelad en T4 GPU, kan bara hantera två användarinlämningar åt gången. Om du upplever långa väntetider eller att applikationen inte svarar, är detta anledningen. I framtiden planerar vi att själva vara värdar för denna lösning, med en bättre server för en förbättrad användarupplevelse, optimerad kod och flera modellalternativ. Spännande utveckling är på gång! - -Det är också viktigt att notera att modellerna fungerar på löpande text och inte text i tabellformat. diff --git a/app/content/NOR/htrflow/htrflow_col2.md b/app/content/NOR/htrflow/htrflow_col2.md deleted file mode 100644 index 99607f6020e27e6880c1ad8e5763b78d6f29f5c7..0000000000000000000000000000000000000000 --- a/app/content/NOR/htrflow/htrflow_col2.md +++ /dev/null @@ -1,23 +0,0 @@ -## Source Code - -Please fork and leave a star on Github if you like it! The code for this project can be found here: - -- [Github](https://github.com/Riksarkivet/HTRFLOW) - -**Note**: We will in the future package all of the code for mass HTR (batch inference on multi-GPU setup), but the code is still work in progress. - -## Models - -The models used in this demo are very much a work in progress, and as more data, and new architectures, becomes available, they will be retrained and reevaluated. For more information about the models, please refer to their model-cards on Huggingface. - -- [Riksarkivet/rtmdet_regions](https://huggingface.co/Riksarkivet/rtmdet_regions) -- [Riksarkivet/rtmdet_lines](https://huggingface.co/Riksarkivet/rtmdet_lines) -- [Riksarkivet/satrn_htr](https://huggingface.co/https://huggingface.co/Riksarkivet/satrn_htr) - -## Datasets - -Train and testsets created by the Swedish National Archives will be released here: - -- [Riksarkivet/placeholder_region_segmentation](https://huggingface.co/datasets/Riksarkivet/placeholder_region_segmentation) -- [Riksarkivet/placeholder_line_segmentation](https://huggingface.co/datasets/Riksarkivet/placeholder_line_segmentation) -- [Riksarkivet/placeholder_htr](https://huggingface.co/datasets/Riksarkivet/placeholder_htr) diff --git a/app/content/NOR/htrflow/htrflow_row1.md b/app/content/NOR/htrflow/htrflow_row1.md deleted file mode 100644 index 2c843d67260070c26f313a933b62efce4af2ed57..0000000000000000000000000000000000000000 --- a/app/content/NOR/htrflow/htrflow_row1.md +++ /dev/null @@ -1,3 +0,0 @@ -## The Pipeline in Overview - -The steps in the pipeline can be seen below as follows: diff --git a/app/content/NOR/htrflow/htrflow_tab1.md b/app/content/NOR/htrflow/htrflow_tab1.md deleted file mode 100644 index 495ec9925b467de9fa59fae157b35ba1a8172d60..0000000000000000000000000000000000000000 --- a/app/content/NOR/htrflow/htrflow_tab1.md +++ /dev/null @@ -1,7 +0,0 @@ -### Binarization - -The reason for binarizing the images before processing them is that we want the models to generalize as well as possible. By training on only binarized images and by binarizing images before running them through the pipeline, we take the target domain closer to the training domain, and reduce negative effects of background variation, background noise etc., on the final results. The pipeline implements a simple adaptive thresholding algorithm for binarization. - -
-HTR_tool -
diff --git a/app/content/NOR/htrflow/htrflow_tab2.md b/app/content/NOR/htrflow/htrflow_tab2.md deleted file mode 100644 index dbbfb3e5b19a32ab9cab2c48693945ebd11ced1a..0000000000000000000000000000000000000000 --- a/app/content/NOR/htrflow/htrflow_tab2.md +++ /dev/null @@ -1,7 +0,0 @@ -### Text-region segmentation - -To facilitate the text-line segmentation process, it is advantageous to segment the image into text-regions beforehand. This initial step offers several benefits, including reducing variations in line spacing, eliminating blank areas on the page, establishing a clear reading order, and distinguishing marginalia from the main text. The segmentation model utilized in this process predicts both bounding boxes and masks. Although the model has the capability to predict both, only the masks are utilized for the segmentation tasks of lines and regions. An essential post-processing step involves checking for regions that are contained within other regions. During this step, only the containing region is retained, while the contained region is discarded. This ensures that the final segmented text-regions are accurate and devoid of overlapping or redundant areas. This ensures that there’s no duplicate text-regions sent to the text-recognition model. - -
-HTR_tool -
diff --git a/app/content/NOR/htrflow/htrflow_tab3.md b/app/content/NOR/htrflow/htrflow_tab3.md deleted file mode 100644 index 78bcdb9c2e4b17779a65a93b303b1f616f03b128..0000000000000000000000000000000000000000 --- a/app/content/NOR/htrflow/htrflow_tab3.md +++ /dev/null @@ -1,7 +0,0 @@ -### Text-line segmentation - -This is also an instance segmentation model, trained on extracting text-lines from the cropped text-regions. The same post-processing as in the text-region segmentation step, is done in the text-line segmentation step. - -
-HTR_tool -
diff --git a/app/content/NOR/htrflow/htrflow_tab4.md b/app/content/NOR/htrflow/htrflow_tab4.md deleted file mode 100644 index 89923a5e4c23b25546cc1dd23d813f9a1870acd6..0000000000000000000000000000000000000000 --- a/app/content/NOR/htrflow/htrflow_tab4.md +++ /dev/null @@ -1,7 +0,0 @@ -### Text Recognition - -The text-recognition model was trained on approximately one million handwritten text-line images ranging from the 17th to the 19th century. See the model card for detailed evaluation results, and results from some fine-tuning experiments. - -
-HTR_tool -
diff --git a/app/content/SWE/htrflow/htrflow_col1.md b/app/content/SWE/htrflow/htrflow_col1.md deleted file mode 100644 index ac45b8f4042e9c4f8f855aee6a3f8fe786272539..0000000000000000000000000000000000000000 --- a/app/content/SWE/htrflow/htrflow_col1.md +++ /dev/null @@ -1,18 +0,0 @@ -### Introduktion - -Riksarkivet presenterar en demonstrationspipeline för HTR (Handwritten Text Recognition). Pipelinen består av två instanssegmenteringsmodeller: en tränad för att segmentera textregioner i bilder av löpande-textdokument och en annan tränad för att segmentera textrader inom dessa regioner. Textraderna transkriberas därefter av en textigenkänningsmodell som är tränad på ett stort dataset med svensk handskrift från 1600- till 1800-talet. - -### Användning - -Det är viktigt att betona att denna applikation främst är avsedd för demonstrationsändamål. Målet är att visa upp vår pipeline för att transkribera historiska dokument med löpande text, inte att använda pipelinen i storskalig produktion. -**Obs**: I framtiden kommer vi att optimera koden för att passa ett produktionsscenario med multi-GPU och batch-inferens, men detta arbete pågår fortfarande.
- -För en inblick i de kommande funktionerna vi arbetar med: - -- Navigera till > **Översikt** > **Ändringslogg och roadmap**. - -### Begränsningar - -Demon, som är värd på Huggingface och tilldelad en T4 GPU, kan bara hantera två användarinlämningar åt gången. Om du upplever långa väntetider eller att applikationen inte svarar, är detta anledningen. I framtiden planerar vi att själva vara värdar för denna lösning, med en bättre server för en förbättrad användarupplevelse, optimerad kod och flera modellalternativ. Spännande utveckling är på gång! - -Det är också viktigt att notera att modellerna fungerar på löpande text och inte text i tabellformat. diff --git a/app/content/SWE/htrflow/htrflow_col2.md b/app/content/SWE/htrflow/htrflow_col2.md deleted file mode 100644 index 99607f6020e27e6880c1ad8e5763b78d6f29f5c7..0000000000000000000000000000000000000000 --- a/app/content/SWE/htrflow/htrflow_col2.md +++ /dev/null @@ -1,23 +0,0 @@ -## Source Code - -Please fork and leave a star on Github if you like it! The code for this project can be found here: - -- [Github](https://github.com/Riksarkivet/HTRFLOW) - -**Note**: We will in the future package all of the code for mass HTR (batch inference on multi-GPU setup), but the code is still work in progress. - -## Models - -The models used in this demo are very much a work in progress, and as more data, and new architectures, becomes available, they will be retrained and reevaluated. For more information about the models, please refer to their model-cards on Huggingface. - -- [Riksarkivet/rtmdet_regions](https://huggingface.co/Riksarkivet/rtmdet_regions) -- [Riksarkivet/rtmdet_lines](https://huggingface.co/Riksarkivet/rtmdet_lines) -- [Riksarkivet/satrn_htr](https://huggingface.co/https://huggingface.co/Riksarkivet/satrn_htr) - -## Datasets - -Train and testsets created by the Swedish National Archives will be released here: - -- [Riksarkivet/placeholder_region_segmentation](https://huggingface.co/datasets/Riksarkivet/placeholder_region_segmentation) -- [Riksarkivet/placeholder_line_segmentation](https://huggingface.co/datasets/Riksarkivet/placeholder_line_segmentation) -- [Riksarkivet/placeholder_htr](https://huggingface.co/datasets/Riksarkivet/placeholder_htr) diff --git a/app/content/SWE/htrflow/htrflow_row1.md b/app/content/SWE/htrflow/htrflow_row1.md deleted file mode 100644 index 2c843d67260070c26f313a933b62efce4af2ed57..0000000000000000000000000000000000000000 --- a/app/content/SWE/htrflow/htrflow_row1.md +++ /dev/null @@ -1,3 +0,0 @@ -## The Pipeline in Overview - -The steps in the pipeline can be seen below as follows: diff --git a/app/content/SWE/htrflow/htrflow_tab1.md b/app/content/SWE/htrflow/htrflow_tab1.md deleted file mode 100644 index 495ec9925b467de9fa59fae157b35ba1a8172d60..0000000000000000000000000000000000000000 --- a/app/content/SWE/htrflow/htrflow_tab1.md +++ /dev/null @@ -1,7 +0,0 @@ -### Binarization - -The reason for binarizing the images before processing them is that we want the models to generalize as well as possible. By training on only binarized images and by binarizing images before running them through the pipeline, we take the target domain closer to the training domain, and reduce negative effects of background variation, background noise etc., on the final results. The pipeline implements a simple adaptive thresholding algorithm for binarization. - -
-HTR_tool -
diff --git a/app/content/SWE/htrflow/htrflow_tab2.md b/app/content/SWE/htrflow/htrflow_tab2.md deleted file mode 100644 index dbbfb3e5b19a32ab9cab2c48693945ebd11ced1a..0000000000000000000000000000000000000000 --- a/app/content/SWE/htrflow/htrflow_tab2.md +++ /dev/null @@ -1,7 +0,0 @@ -### Text-region segmentation - -To facilitate the text-line segmentation process, it is advantageous to segment the image into text-regions beforehand. This initial step offers several benefits, including reducing variations in line spacing, eliminating blank areas on the page, establishing a clear reading order, and distinguishing marginalia from the main text. The segmentation model utilized in this process predicts both bounding boxes and masks. Although the model has the capability to predict both, only the masks are utilized for the segmentation tasks of lines and regions. An essential post-processing step involves checking for regions that are contained within other regions. During this step, only the containing region is retained, while the contained region is discarded. This ensures that the final segmented text-regions are accurate and devoid of overlapping or redundant areas. This ensures that there’s no duplicate text-regions sent to the text-recognition model. - -
-HTR_tool -
diff --git a/app/content/SWE/htrflow/htrflow_tab3.md b/app/content/SWE/htrflow/htrflow_tab3.md deleted file mode 100644 index 78bcdb9c2e4b17779a65a93b303b1f616f03b128..0000000000000000000000000000000000000000 --- a/app/content/SWE/htrflow/htrflow_tab3.md +++ /dev/null @@ -1,7 +0,0 @@ -### Text-line segmentation - -This is also an instance segmentation model, trained on extracting text-lines from the cropped text-regions. The same post-processing as in the text-region segmentation step, is done in the text-line segmentation step. - -
-HTR_tool -
diff --git a/app/content/SWE/htrflow/htrflow_tab4.md b/app/content/SWE/htrflow/htrflow_tab4.md deleted file mode 100644 index 89923a5e4c23b25546cc1dd23d813f9a1870acd6..0000000000000000000000000000000000000000 --- a/app/content/SWE/htrflow/htrflow_tab4.md +++ /dev/null @@ -1,7 +0,0 @@ -### Text Recognition - -The text-recognition model was trained on approximately one million handwritten text-line images ranging from the 17th to the 19th century. See the model card for detailed evaluation results, and results from some fine-tuning experiments. - -
-HTR_tool -
diff --git a/app/gradio_config.py b/app/gradio_config.py index aa7f113f48e75e5e51683b9f189c2e8023149f0f..78a6820dd0dfe9d9d10b253ee1eafd2eb44c5bc3 100644 --- a/app/gradio_config.py +++ b/app/gradio_config.py @@ -13,12 +13,5 @@ theme = gr.themes.Default( ) css = """ -body > gradio-app > div > div > div.wrap.svelte-1rjryqp > footer > a { - display: none !important; -} -body > gradio-app > div > div > div.wrap.svelte-1rjryqp > footer > div { - display: none !important; -} -#langdropdown {width: 100px;} """ diff --git a/app/main.py b/app/main.py index d9f4867581cbb06fac43970ccd7068ed562737b9..ded01830ecee5031caa516d825c86cfac28abb73 100644 --- a/app/main.py +++ b/app/main.py @@ -1,97 +1,38 @@ -import logging - import gradio as gr from app.gradio_config import css, theme from app.tabs.adv_htrflow_tab import adv_htrflow_pipeline -from app.tabs.data_explorer_tab import data_explorer from app.tabs.examples_tab import examples -from app.tabs.htrflow_tab import htrflow_pipeline -from app.tabs.overview_tab import overview, overview_language -from app.utils.lang_helper import get_tab_updates +from app.tabs.templating import ( + templating_block, + template_image_folder, + template_yaml_folder, +) from app.utils.md_helper import load_markdown -logger = logging.getLogger("gradio_log") - - -TAB_LABELS = { - "ENG": ["Home", "Simple", "Advanced", "Examples"], - "SWE": ["Hem", "Enkel", "Avancerad", "Exempel"], -} +gr.set_static_paths(paths=[template_image_folder]) +gr.set_static_paths(paths=[template_yaml_folder]) -LANG_CHOICES = ["ENG", "SWE"] with gr.Blocks(title="HTRflow", theme=theme, css=css) as demo: with gr.Row(): - local_language = gr.BrowserState( - default_value="ENG", storage_key="selected_language" - ) - main_language = gr.State(value="ENG") - with gr.Column(scale=1): - language_selector = gr.Dropdown( - choices=LANG_CHOICES, - value="ENG", - container=False, - min_width=50, - scale=0, - elem_id="langdropdown", - ) - + pass with gr.Column(scale=2): gr.Markdown(load_markdown(None, "main_title")) with gr.Column(scale=1): gr.Markdown(load_markdown(None, "main_sub_title")) with gr.Tabs(elem_classes="top-navbar") as navbar: - with gr.Tab(label="Home") as tab_home: - overview.render() - - with gr.Tab(label="Simple") as tab_simple_htr: - htrflow_pipeline.render() + with gr.Tab(label="Templating") as tab_templating: + templating_block.render() - with gr.Tab(label="Advanced") as tab_custom_htr: + with gr.Tab(label="Submit Job") as tab_submit_job: adv_htrflow_pipeline.render() - with gr.Tab(label="Examples") as tab_examples: + with gr.Tab(label="Output & Visualize") as tab_examples: examples.render() - # with gr.Tab(label="Data Explorer") as tab_data_explorer: - # data_explorer.render() - - @demo.load( - inputs=[local_language], - outputs=[language_selector, main_language, overview_language], - ) - def load_language(saved_values): - return (saved_values,) * 3 - - @language_selector.change( - inputs=[language_selector], - outputs=[ - local_language, - main_language, - overview_language, - ], - ) - def save_language_to_browser(selected_language): - return (selected_language,) * 3 - - @main_language.change( - inputs=[main_language], - outputs=[ - tab_home, - tab_simple_htr, - tab_custom_htr, - ], - ) - def update_main_tabs(selected_language): - return (*get_tab_updates(selected_language, TAB_LABELS),) - - @main_language.change(inputs=[main_language]) - def log_on_language_change(selected_language): - logger.info(f"Language changed to: {selected_language}") - demo.queue() diff --git a/app/plot_viewer.py b/app/plot_viewer.py deleted file mode 100644 index 7cea133bb34c13782b842e1cd13fda07445969ec..0000000000000000000000000000000000000000 --- a/app/plot_viewer.py +++ /dev/null @@ -1,200 +0,0 @@ -import xml.etree.ElementTree as ET -from io import BytesIO - -import cv2 -import gradio as gr -import numpy as np -import requests -from PIL import Image - - -def parse_alto_xml(xml_file): - """Parse the ALTO XML file to extract polygons and text content for each TextLine.""" - tree = ET.parse(xml_file) - root = tree.getroot() - ns = {"alto": "http://www.loc.gov/standards/alto/ns-v4#"} - - annotations = [] - transcriptions = {} - - for text_block in root.findall(".//alto:TextBlock", ns): - for text_line in text_block.findall("alto:TextLine", ns): - shape = text_line.find("alto:Shape", ns) - - if shape is not None: - polygon = shape.find("alto:Polygon", ns) - if polygon is not None: - polygon_points = polygon.attrib["POINTS"] - points = [ - tuple(map(int, point.split(","))) - for point in polygon_points.split() - ] - else: - hpos = int(text_line.attrib["HPOS"]) - vpos = int(text_line.attrib["VPOS"]) - width = int(text_line.attrib["WIDTH"]) - height = int(text_line.attrib["HEIGHT"]) - points = [ - (hpos, vpos), - (hpos + width, vpos), - (hpos + width, vpos + height), - (hpos, vpos + height), - ] - - content = " ".join( - [ - string.attrib["CONTENT"] - for string in text_line.findall("alto:String", ns) - ] - ) - label = text_line.attrib["ID"] - - annotations.append((points, label)) - transcriptions[label] = content - - text_area_content = "\n".join(transcriptions[label] for label in transcriptions) - - return annotations, transcriptions, text_area_content - - -def visualize_polygons_on_image( - image, annotations, alpha=0.5, include_reading_order=False -): - """Visualize polygons on the image with an optional reading order overlay.""" - overlay = image.copy() - for _, (polygon, label) in enumerate(annotations): - color = ( - np.random.randint(0, 255), - np.random.randint(0, 255), - np.random.randint(0, 255), - ) - cv2.fillPoly(overlay, [np.array(polygon, dtype=np.int32)], color) - - if include_reading_order: - centroid = np.mean(np.array(polygon), axis=0).astype(int) - cv2.putText( - overlay, - str(label), - tuple(centroid), - cv2.FONT_HERSHEY_SIMPLEX, - 0.5, - (0, 0, 0), - 1, - cv2.LINE_AA, - ) - - return cv2.addWeighted(overlay, alpha, image, 1 - alpha, 0) - - -def visualize( - xml_file, image_source, image_id, uploaded_image, include_reading_order=False -): - if image_source == "Use IIIF image": - if not image_id: - raise gr.Error("Please enter an Image ID.") - image_url = f"https://iiifintern.ra.se/arkis!{image_id}/full/max/0/default.jpg" - response = requests.get(image_url) - if response.status_code != 200: - raise gr.Error(f"Failed to download image from {image_url}") - image = np.array(Image.open(BytesIO(response.content))) - else: - if uploaded_image is None: - raise gr.Error("Please upload an image.") - image = uploaded_image - - annotations, transcriptions, text_area_content = parse_alto_xml(xml_file) - annotated_image = visualize_polygons_on_image( - image, annotations, include_reading_order=include_reading_order - ) - - return annotated_image, annotations, transcriptions, text_area_content - - -def get_transcription_from_coords(annotations, transcriptions, evt: gr.SelectData): - """Get the transcription for the polygon clicked in the annotated image.""" - x, y = evt.index[0], evt.index[1] - for points, label in annotations: - polygon = np.array(points, dtype=np.int32) - if cv2.pointPolygonTest(polygon, (x, y), False) >= 0: - return transcriptions.get(label, "No transcription available.") - return "No transcription available." - - -with gr.Blocks(title="XML Visualization App") as app: - with gr.Tab("Visualize"): - annotations_state = gr.State() - transcriptions_state = gr.State() - - with gr.Row(): - with gr.Column(): - xml_input = gr.File(label="Upload ALTO XML File", file_types=[".xml"]) - with gr.Column(): - image_source = gr.Radio( - choices=["Use IIIF image", "Upload your own image"], - label="Image Source", - value="Use IIIF image", - ) - image_id_input = gr.Textbox( - label="Image ID", - placeholder="Enter image ID (e.g., 30003365_00001)", - visible=True, - ) - image_upload = gr.Image( - label="Upload Image", type="numpy", visible=False - ) - include_reading_order_input = gr.Checkbox(label="Include Reading Order") - process_button = gr.Button("Visualize Alto", scale=0, variant="primary") - - def update_image_source(choice): - if choice == "Use IIIF image": - return [gr.update(visible=True), gr.update(visible=False)] - else: - return [gr.update(visible=False), gr.update(visible=True)] - - image_source.change( - update_image_source, - inputs=image_source, - outputs=[image_id_input, image_upload], - ) - - with gr.Row(): - with gr.Column(scale=3): - annotated_image_output = gr.Image( - label="Annotated Image", interactive=True - ) - with gr.Column(scale=2): - transcription_output = gr.TextArea( - label="Transcription", - interactive=False, - show_copy_button=True, - lines=30, - ) - transcription_selected = gr.Textbox( - label="Selected Polygon", interactive=False, show_copy_button=True - ) - - process_button.click( - visualize, - inputs=[ - xml_input, - image_source, - image_id_input, - image_upload, - include_reading_order_input, - ], - outputs=[ - annotated_image_output, - annotations_state, - transcriptions_state, - transcription_output, - ], - ) - - annotated_image_output.select( - get_transcription_from_coords, - inputs=[annotations_state, transcriptions_state], - outputs=transcription_selected, - ) - -app.queue() -app.launch() diff --git a/app/tabs/adv_htrflow_tab.py b/app/tabs/adv_htrflow_tab.py index 72ffade97cc922a137195aa835f3059192e602cd..2c0c46826e76b767a6a353203bef31db8bc4b1f8 100644 --- a/app/tabs/adv_htrflow_tab.py +++ b/app/tabs/adv_htrflow_tab.py @@ -1,35 +1,22 @@ -import time - import gradio as gr + with gr.Blocks() as adv_htrflow_pipeline: with gr.Row(variant="panel"): with gr.Column(scale=3): - image_mask2 = gr.ImageEditor( - label="Uploaded image", - sources="upload", - interactive=True, - layers=False, - eraser=False, - brush=False, - height=400, - transforms="crop", - crop_size="16,5", - visible=False, - ) - - image_mask = gr.Gallery( + image_batch_input = gr.Gallery( file_types=["image"], label="Upload images", interactive=True, - height=400, object_fit="cover", + preview=True, columns=5, ) + with gr.Row(visible=True) as yaml_pipeline: with gr.Accordion(label="Run Template", open=False): - gr.Checkbox(value=True, label="Batch", container=False, scale=0) + custom_template_yaml = gr.Code( value="Paste your custom pipeline here", language="yaml", @@ -58,7 +45,11 @@ with gr.Blocks() as adv_htrflow_pipeline: info="Checkboxgroup should be basedon output structure from htrflow", ) - gr.Image(interactive=False) + gr.Image( + interactive=False, + show_fullscreen_button=True, + show_share_button=True, + ) with gr.Tab("Table"): pass @@ -67,6 +58,7 @@ with gr.Blocks() as adv_htrflow_pipeline: pass def foo(): + gr.Info("hello morgan") return gr.update(visible=True), "test" click_event = run_button.click( @@ -79,21 +71,3 @@ with gr.Blocks() as adv_htrflow_pipeline: outputs=cancel_button, cancels=[click_event], ) - - image_mask2.upload( - fn=None, - inputs=None, - outputs=None, - js=""" - () => { - // Target the button using its attributes - const button = document.querySelector('button[aria-label="Transform button"][title="Transform button"]'); - if (button) { - button.click(); // Simulate a click - console.log('Transform button clicked.'); - } else { - console.error('Transform button not found.'); - } - } - """, - ).then(fn=lambda: gr.update(crop=None), inputs=None, outputs=image_mask2) diff --git a/app/tabs/data_explorer_tab copy.py b/app/tabs/data_explorer_tab copy.py deleted file mode 100644 index a2d00093e43cea96b0c99f4ae3b5bde0ef29a88e..0000000000000000000000000000000000000000 --- a/app/tabs/data_explorer_tab copy.py +++ /dev/null @@ -1,62 +0,0 @@ -import gradio as gr -from gradio_modal import Modal - -output_image_placehholder = gr.Image(label="Output image", height=400, show_share_button=True) - - -def show_warning(selection: gr.SelectData): - gr.Warning(f"Your choice is #{selection.index}, with image: {selection.value['image']['path']}!") - - -with gr.Blocks() as data_explorer: - with gr.Row(variant="panel"): - with gr.Column(scale=1): - output_dataframe_pipeline = gr.Textbox(label="Path", info="path s3 or path, hf-dataset.") - with gr.Group(): - ( - gr.Dropdown( - ["ran", "swam", "ate", "slept"], - value=["swam", "slept"], - multiselect=True, - label="Activity", - info="Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ", - ), - ) - gr.Slider(2, 20, value=4, label="Count", info="Choose between 2 and 20") - gr.Slider(2, 20, value=4, label="Another", info="Choose between 2 and 20") - output_dataframe_pipeline = gr.Textbox(label="search", info="search image bla bla..") - gr.Button("Search", variant="primary", scale=0) - with gr.Column(scale=4): - with gr.Tabs(): - with gr.Tab("Gallery"): - image_gallery = gr.Gallery( - [ - "https://unsplash.com/photos/4oaDBgVROGo/download?ixid=M3wxMjA3fDB8MXxhbGx8NHx8fHx8fDJ8fDE3MTA0NjI1MzZ8&force=true&w=640", - "https://unsplash.com/photos/4oaDBgVROGo/download?ixid=M3wxMjA3fDB8MXxhbGx8NHx8fHx8fDJ8fDE3MTA0NjI1MzZ8&force=true&w=640", - "https://unsplash.com/photos/4oaDBgVROGo/download?ixid=M3wxMjA3fDB8MXxhbGx8NHx8fHx8fDJ8fDE3MTA0NjI1MzZ8&force=true&w=640", - "https://unsplash.com/photos/4oaDBgVROGo/download?ixid=M3wxMjA3fDB8MXxhbGx8NHx8fHx8fDJ8fDE3MTA0NjI1MzZ8&force=true&w=640", - ] - * 10, - allow_preview=False, - label="Image Gallery", - preview=False, - columns=[7], - rows=[10], - show_download_button=True, - show_share_button=True, - ) - with gr.Tab("Embeddings"): - pass - # TODO: add a embedding plot here - # user needs to login hf and get a datasets with embeddings - - with Modal(visible=False) as gallery_modal: - with gr.Row(): - with gr.Column(scale=0): - gr.Markdown("") - with gr.Column(scale=4): - gr.Image() - with gr.Column(scale=0): - gr.Markdown("") - - image_gallery.select(fn=show_warning, inputs=None).then(lambda: Modal(visible=True), None, gallery_modal) diff --git a/app/tabs/data_explorer_tab.py b/app/tabs/data_explorer_tab.py deleted file mode 100644 index b0bf99cf158f2640035091a50bf0e2c294363df3..0000000000000000000000000000000000000000 --- a/app/tabs/data_explorer_tab.py +++ /dev/null @@ -1,33 +0,0 @@ -import gradio as gr - - -def display_dataset(dataset_repo): - return gr.HTML( - f"""""" - ) - - -with gr.Blocks() as data_explorer: - with gr.Row(variant="panel"): - with gr.Column(scale=0, min_width=160): - input_datasets_path = gr.Textbox( - label="HF datasets", - placeholder="Gabriel/linkoping", - scale=0, - container=False, - ) - view_dataset = gr.Button("View dataseet", variant="primary", scale=0) - # gr.LoginButton("Login to HF", variant="secondary", scale=0) - with gr.Column(): - iframe_output = gr.HTML() - - view_dataset.click( - fn=display_dataset, - inputs=input_datasets_path, - outputs=[iframe_output], - ) diff --git a/app/tabs/examples_tab.py b/app/tabs/examples_tab.py index a41d6ac4b38266ec70e4d99eca645b343c00142f..2060b651463ca805f9bdd27f32e0e6bec82c7aac 100644 --- a/app/tabs/examples_tab.py +++ b/app/tabs/examples_tab.py @@ -2,10 +2,6 @@ import gradio as gr from gradio_modal import Modal import pandas as pd -from app.assets.examples import DemoImages - -images_for_demo = DemoImages() - output_image_placehholder = gr.Image( label="Output image", @@ -27,25 +23,29 @@ with gr.Blocks() as examples: with gr.Row(variant="panel"): with gr.Column(): example_text_input_placeholder = gr.Markdown(visible=False, container=False) - example_method_input_placeholder = gr.Markdown(visible=False, container=False) - example_text_output_placeholder = gr.Markdown(visible=False, container=False) + example_method_input_placeholder = gr.Markdown( + visible=False, container=False + ) + example_text_output_placeholder = gr.Markdown( + visible=False, container=False + ) user_image_input = gr.Image(visible=False, container=False) - gr.Examples( # TODO: add so example has a alto xml file on hf datasets - fn=htr_image_placehholder, - examples=images_for_demo.examples_list, - inputs=[ - example_text_input_placeholder, - example_method_input_placeholder, - user_image_input, - ], - outputs=[example_text_output_placeholder, markdown_selected_option, output_image_placehholder], - cache_examples=True, - cache_mode="eager", - label="Example images", - examples_per_page=6, - ) + # gr.Examples( # TODO: add so example has a alto xml file on hf datasets + # fn=htr_image_placehholder, + # examples=images_for_demo.examples_list, + # inputs=[ + # example_text_input_placeholder, + # example_method_input_placeholder, + # user_image_input, + # ], + # outputs=[example_text_output_placeholder, markdown_selected_option, output_image_placehholder], + # cache_examples=True, + # cache_mode="eager", + # label="Example images", + # examples_per_page=6, + # ) with gr.Column(): with gr.Tabs(): @@ -55,19 +55,35 @@ with gr.Blocks() as examples: output_image_placehholder.render() with gr.Row(): markdown_selected_option.render() - show_output_modal = gr.Button("Show output", variant="secondary", scale=0) + show_output_modal = gr.Button( + "Show output", variant="secondary", scale=0 + ) with gr.Row(): - output_dataframe_pipeline = gr.Textbox(label="Click text", info="click on image bla bla..") + output_dataframe_pipeline = gr.Textbox( + label="Click text", info="click on image bla bla.." + ) with gr.Tab("YAML, DF, Graph?") as htrflow_output_table_tab: with gr.Group(): with gr.Row(): - output_dataframe_pipeline = gr.Image(label="Output image", interactive=False, height="100") + output_dataframe_pipeline = gr.Image( + label="Output image", interactive=False, height="100" + ) with gr.Row(): - output_dataframe_pipeline = gr.Dataframe(label="Output image", col_count=2) + output_dataframe_pipeline = gr.Dataframe( + label="Output image", col_count=2 + ) with Modal(visible=False) as output_modal: - output_files_pipeline = gr.Files(label="Output files", height=100, visible=True, scale=0) - output_yaml_code = gr.Code(language="yaml", label="yaml", interactive=True, visible=True, scale=0) + output_files_pipeline = gr.Files( + label="Output files", height=100, visible=True, scale=0 + ) + output_yaml_code = gr.Code( + language="yaml", + label="yaml", + interactive=True, + visible=True, + scale=0, + ) # TODO: yaml should parse from datasets.. and show the yaml code show_output_modal.click(lambda: Modal(visible=True), None, output_modal) diff --git a/app/tabs/htrflow_tab.py b/app/tabs/htrflow_tab.py deleted file mode 100644 index 000498eef3f165afe2832fc0333bd734a97eee24..0000000000000000000000000000000000000000 --- a/app/tabs/htrflow_tab.py +++ /dev/null @@ -1,289 +0,0 @@ -import gradio as gr -import pandas as pd -from gradio_modal import Modal -from jinja2 import Environment, FileSystemLoader - -from app.assets.examples import DemoImages - -images_for_demo = DemoImages() - - -def toggle_visibility_default_templates(selected_option): - return [ - gr.update(visible=(selected_option == "Simple layout")), - gr.update(visible=(selected_option == "Nested segmentation")), - gr.update(visible=(selected_option == "Tables")), - selected_option, - ] - - -def dummy_revealer(reveal_bool): - if reveal_bool == 0: - return gr.update(visible=False) - else: - return gr.update(visible=True) - - -def submit_button_pipeline_fn(method, input_image, yaml_str): - data = {"transcription": ["Example transcription"], "prediction score": [0.95]} - - df = pd.DataFrame(data) - - # HTRflow code here - - serialized_files = ( - "https://raw.githubusercontent.com/Borg93/htr_gradio_file_placeholder/refs/heads/main/app_project_line.png" - ) - - return f"Selected Option: {method}", input_image, serialized_files, gr.update(visible=True), df - - -def get_yaml_button_fn( - method, - output_formats, - reading_order, - simple_segment_model=None, - simple_htr_model=None, - simple_htr_model_type=None, - simple_segment_model_type=None, - nested_segment_model_1=None, - nested_segment_model_2=None, - nested_htr_model=None, - nested_segment_model_1_type=None, - nested_segment_model_2_type=None, - nested_htr_model_type=None, -): - env = Environment(loader=FileSystemLoader("app/templates")) - - if output_formats is None: - output_formats = ["txt"] - - template_name = "steps_template.yaml.j2" - try: - if method == "Simple layout": - steps = [ - { - "step": "Segmentation", - "model": simple_segment_model_type, - "model_settings": {"model": simple_segment_model}, - }, - { - "step": "TextRecognition", - "model": simple_htr_model_type, - "model_settings": {"model": simple_htr_model}, - }, - ] - elif method == "Nested segmentation": - steps = [ - { - "step": "Segmentation", - "model": nested_segment_model_1_type, - "model_settings": {"model": nested_segment_model_1}, - }, - { - "step": "Segmentation", - "model": nested_segment_model_2_type, - "model_settings": {"model": nested_segment_model_2}, - }, - { - "step": "TextRecognition", - "model": nested_htr_model_type, - "model_settings": {"model": nested_htr_model}, - }, - ] - else: - return "Invalid method or not yet supported." - - steps.append({"step": reading_order}) - - steps.extend( - { - "step": "Export", - "settings": {"format": format, "dest": f"{format}-outputs"}, - } - for format in output_formats - ) - - template = env.get_template(template_name) - - yaml_value = template.render(steps=steps) - return yaml_value - - except Exception as e: - return f"Error generating YAML: {str(e)}" - - -output_image_placehholder = gr.Image(label="Output image", height=400, show_share_button=True) -markdown_selected_option = gr.Markdown(container=True) - -inital_state_selection_option = "Simple layout" - -with gr.Blocks() as htrflow_pipeline: - with gr.Row(variant="panel"): - with gr.Column(scale=2): - # gr.Markdown("

Control Panel

") - - with gr.Group(): - example_text_input_placeholder = gr.Markdown(visible=False, container=False) - example_method_input_placeholder = gr.Markdown(visible=False, container=False) - example_text_output_placeholder = gr.Markdown(visible=False, container=False) - - selected_option = gr.State(inital_state_selection_option) - dummy_none = gr.State(0) - - user_image_input = gr.Image( - interactive=True, sources=["upload", "clipboard"], label="Input image", height=300 - ) - - template_method_radio = gr.Dropdown( - [inital_state_selection_option, "Nested segmentation", "Tables"], - value=inital_state_selection_option, - label="Select template", - info="Will add more templates later!", - ) - - with gr.Accordion(label="Pipeline", open=False): - with gr.Row() as simple_pipeline: - with gr.Column(): - with gr.Row(): # TODO: use dynamic rendering instead to make it more clean :https://www.youtube.com/watch?v=WhAMvOEOWJw&ab_channel=HuggingFace - simple_segment_model = gr.Textbox( - "model1", label="Segmentation", info="Info about the Segmentation model" - ) - simple_segment_model_type = gr.Dropdown( - choices=["yolo"], value="yolo", label="Segmentation", info="Model" - ) - with gr.Row(): - simple_htr_model = gr.Textbox("model1", label="HTR", info="Info about the HTR model") - simple_htr_model_type = gr.Dropdown( - choices=["TrOCR"], value="TrOCR", label="HTR", info="Model" - ) - - with gr.Row(visible=False) as nested_pipeline: - with gr.Column(): - with gr.Row(): - nested_segment_model_1 = gr.Textbox( - "model1", label="Segmentation", info="Info about the Segmentation model" - ) - nested_segment_model_1_type = gr.Dropdown( - choices=["yolo"], value="yolo", label="Segmentation", info="Model" - ) - with gr.Row(): - nested_segment_model_2 = gr.Textbox( - "model2", label="Segmentation", info="Info about the Segmentation model" - ) - nested_segment_model_2_type = gr.Dropdown( - choices=["yolo"], value="yolo", label="Segmentation", info="Model" - ) - with gr.Row(): - nested_htr_model = gr.Textbox("model1", label="HTR", info="Info about the HTR model") - nested_htr_model_type = gr.Dropdown( - choices=["TrOCR"], value="TrOCR", label="HTR", info="Model" - ) - - with gr.Row(visible=False) as table_pipeline: - with gr.Column(): - gr.Textbox("WIP") - with gr.Row(): - reading_order = gr.Dropdown( - choices=["OrderLines", "ReadingOrderMarginalia"], - value="OrderLines", - multiselect=False, - label="Reading Order", - info="Supported format are: ...", - ) - output_formats = gr.Dropdown( - choices=["txt", "alto", "page"], - value="txt", - multiselect=True, - label="Serialized Output", - info="Supported format are: ...", - ) - - with gr.Row(): - submit_button_pipeline = gr.Button("Submit", variant="primary", scale=0) - get_yaml_button = gr.Button("Get Yaml", variant="secondary", scale=0) - output_files_pipeline = gr.Files(label="Output files", height=100, visible=False) - - with gr.Column(scale=2): - with gr.Tabs(): - with gr.Tab("Viewer"): - with gr.Group(): - with gr.Row(): - output_image_placehholder.render() - with gr.Row(): - markdown_selected_option.render() - with gr.Row(): - output_dataframe_pipeline = gr.Textbox(label="Click text", info="click on image bla bla..") - with gr.Tab("Table") as htrflow_output_table_tab: - with gr.Group(): - with gr.Row(): - output_dataframe_pipeline = gr.Image(label="Output image", interactive=False, height="100") - with gr.Row(): - output_dataframe_pipeline = gr.Dataframe(label="Output image", col_count=2) - - with Modal(visible=False) as yaml_modal: - with gr.Row(): - with gr.Column(scale=0): - gr.Markdown("") - with gr.Column(scale=4): - output_yaml_code = gr.Code(language="yaml", label="yaml", interactive=True, visible=False) - with gr.Column(scale=0): - gr.Markdown("") - - submit_button_pipeline.click( - get_yaml_button_fn, - inputs=[ - template_method_radio, - output_formats, - reading_order, - simple_segment_model, - simple_htr_model, - simple_htr_model_type, - simple_segment_model_type, - nested_segment_model_1, - nested_segment_model_2, - nested_htr_model, - nested_segment_model_1_type, - nested_segment_model_2_type, - nested_htr_model_type, - ], - outputs=[output_yaml_code], - ).then( - submit_button_pipeline_fn, - inputs=[template_method_radio, user_image_input, output_yaml_code], - outputs=[ - markdown_selected_option, - output_image_placehholder, - output_files_pipeline, - output_files_pipeline, - output_dataframe_pipeline, - ], - ) - - get_yaml_button.click( - get_yaml_button_fn, - inputs=[ - template_method_radio, - output_formats, - reading_order, - simple_segment_model, - simple_htr_model, - simple_htr_model_type, - simple_segment_model_type, - nested_segment_model_1, - nested_segment_model_2, - nested_htr_model, - nested_segment_model_1_type, - nested_segment_model_2_type, - nested_htr_model_type, - ], - outputs=[output_yaml_code], - ).then(dummy_revealer, inputs=output_yaml_code, outputs=output_yaml_code) - - template_method_radio.select( - lambda choice: toggle_visibility_default_templates(choice), - inputs=template_method_radio, - outputs=[simple_pipeline, nested_pipeline, table_pipeline, selected_option], - ) - - get_yaml_button.click(lambda: Modal(visible=True), None, yaml_modal) diff --git a/app/tabs/overview_tab.py b/app/tabs/overview_tab.py deleted file mode 100644 index b65ffac23699afc868a6c36ae4922c1591c4a958..0000000000000000000000000000000000000000 --- a/app/tabs/overview_tab.py +++ /dev/null @@ -1,114 +0,0 @@ -import gradio as gr - -from app.utils.lang_helper import get_tab_updates -from app.utils.md_helper import load_markdown - -TAB_LABELS = { - "ENG": ["Overview", "About App", "Guide", "Model & Data", "Contributions", "Duplicate App", "FAQ & Contact"], - "SWE": ["Översikt", "Om appen", "Guide", "Modell & Data", "Bidrag", "Duplicera App", "FAQ & Kontakt"], -} - -with gr.Blocks() as overview: - overview_language = gr.State(value="ENG") - - with gr.Column(variant="panel"): - with gr.Tabs(elem_classes="top-navbar") as navbar: - with gr.Tab("Overview") as tab_overview: - with gr.Column(variant="panel"): - md1 = gr.Markdown("some text") - - with gr.Tab("About App") as tab_about: - with gr.Column(): - about_md = gr.Markdown(load_markdown(overview_language.value, "htrflow/htrflow_col1")) - - with gr.Tab("Guide") as tab_guide: - with gr.Column(): - with gr.Row(): - with gr.Column(): - gr.Markdown("## Fast track") - gr.Video( - value="https://github.com/Borg93/htr_gradio_file_placeholder/blob/main/eating_spaghetti.mp4", - format="mp4", - ) - with gr.Row(): - with gr.Column(): - gr.Markdown("## Stepwise") - gr.Video( - "https://github.com/Borg93/htr_gradio_file_placeholder/blob/main/htr_tool_media_cut.mp4", - format="mp4", - ) - - with gr.Tab("Model & Data") as tab_model_data: - with gr.Column(): - # gr.Markdown(TextOverview.htrflow_row1) - with gr.Tabs(): - with gr.Tab("Binarization"): - gr.Markdown("") # gr.Markdown(TextOverview.htrflow_tab1) - with gr.Tab("Region segmentation"): - gr.Markdown("") # gr.Markdown(TextOverview.htrflow_tab2) - with gr.Tab("Line segmentation"): - gr.Markdown("") # gr.Markdown(TextOverview.htrflow_tab3) - with gr.Tab("Text recognition"): - gr.Markdown("") # gr.Markdown(TextOverview.htrflow_tab4) - - with gr.Tab("Contributions") as tab_contributions: - with gr.Column(): - gr.Markdown("") # gr.Markdown(TextOverview.contributions) - gr.Markdown("") # gr.Markdown(TextOverview.huminfra_image) - - with gr.Tab("Duplicate App") as tab_duplicate_app: - with gr.Column(): - gr.Markdown("") # gr.Markdown(TextOverview.duplicate) - - with gr.Column(): - gr.Markdown("") # gr.Markdown(TextOverview.api1) - # gr.Code( - # value=TextOverview.api_code1, - # language="python", - # interactive=False, - # show_label=False,) - - gr.Markdown("") # gr.Markdown(TextOverview.api2) - - # gr.Code( - # value=TextOverview.api_code2, - # language=None, - # interactive=False, - # show_label=False, - # ) - - with gr.Tab("FAQ & Contact") as tab_faq_contact: - with gr.Column(): - gr.Markdown("") # gr.Markdown(TextOverview.text_faq) - with gr.Column(): - gr.Markdown("") # gr.Markdown(TextOverview.text_discussion) - - overview.load( - inputs=[overview_language], - outputs=[about_md], - ) - - def load_md_text(selected_language): - return load_markdown(selected_language, "htrflow/htrflow_col1") - - @overview_language.change( - inputs=[overview_language], - outputs=[about_md], - ) - def change_md_text(selected_language): - return load_markdown(selected_language, "htrflow/htrflow_col1") - - @overview_language.change( - inputs=[overview_language], - outputs=[ - tab_overview, - tab_about, - tab_guide, - tab_model_data, - tab_contributions, - tab_duplicate_app, - tab_faq_contact, - ], - ) - def save_language_to_browser(selected_language): - return (*get_tab_updates(selected_language, TAB_LABELS),) diff --git a/app/tabs/templating.py b/app/tabs/templating.py new file mode 100644 index 0000000000000000000000000000000000000000..f78fa8953127acd5a110b63fbfc509c5e47fcbb6 --- /dev/null +++ b/app/tabs/templating.py @@ -0,0 +1,114 @@ +import gradio as gr +import os +import re + +template_image_folder = "app/assets/images" +template_yaml_folder = "app/assets/templates" + +image_files = sorted( + [ + os.path.join(template_image_folder, img) + for img in os.listdir(template_image_folder) + if img.lower().endswith((".png", ".jpg", ".jpeg", ".webp")) + ], + key=lambda x: ( + int(re.search(r"\d+", os.path.basename(x)).group()) + if re.search(r"\d+", os.path.basename(x)) + else float("inf") + ), +) + +yaml_files = [ + os.path.join(template_yaml_folder, yml) + for yml in os.listdir(template_yaml_folder) + if yml.lower().endswith(".yaml") +] + +yaml_files_numbered = sorted( + [yml for yml in yaml_files if re.match(r"^\d", os.path.basename(yml))], + key=lambda x: ( + int(re.search(r"\d+", os.path.basename(x)).group()) + if re.search(r"\d+", os.path.basename(x)) + else float("inf") + ), +) + +yaml_files_c_letter = [ + yml for yml in yaml_files if re.match(r"^[cC]", os.path.basename(yml)) +] + +name_yaml_files_c_letter_cleaned = [ + ( + os.path.basename(yml)[2:] + if os.path.basename(yml).startswith("c_") + else os.path.basename(yml) + ) + for yml in yaml_files_c_letter +] + + +def get_yaml_content(yaml_path): + if yaml_path and os.path.isfile(yaml_path): + with open(yaml_path, "r") as file: + return file.read() + return "YAML content not available" + + +with gr.Blocks() as templating_block: + with gr.Row(variant="panel"): + with gr.Column(scale=2): + dropdown_selection_template = gr.Dropdown( + label="Choice template", + info="template info", + value="Simple", + choices=["Simple", "Nested", "Custom"], + max_choices=1, + interactive=True, + ) + template_image = gr.Image( + label="Example Templates", value=image_files[0], height=400 + ) + + custom_dropdown = gr.Dropdown( + label="Custom template", + info="Choice a different custom templates...", + value=name_yaml_files_c_letter_cleaned[0], + choices=name_yaml_files_c_letter_cleaned, + max_choices=1, + interactive=True, + visible=False, + ) + + output_yaml_code = gr.Code( + language="yaml", + label="yaml", + interactive=True, + visible=True, + lines=15, + ) + + @dropdown_selection_template.select( + inputs=dropdown_selection_template, + outputs=[template_image, output_yaml_code, custom_dropdown], + ) + def on_template_select(dropdown_selection_template): + + if dropdown_selection_template == "Simple": + yaml_content = get_yaml_content(yaml_files_numbered[0]) + return image_files[0], yaml_content, gr.update(visible=False) + elif dropdown_selection_template == "Nested": + yaml_content = get_yaml_content(yaml_files_numbered[1]) + return image_files[1], yaml_content, gr.update(visible=False) + elif dropdown_selection_template == "Custom": + yaml_content = get_yaml_content(yaml_files_c_letter[0]) + return image_files[2], yaml_content, gr.update(visible=True) + else: + return gr.Error( + f"{dropdown_selection_template} - is not a valid Template selection" + ) + + templating_block.load( + fn=on_template_select, + inputs=dropdown_selection_template, + outputs=[template_image, output_yaml_code, custom_dropdown], + ) diff --git a/app/templates/steps_template.yaml.j2 b/app/templates/steps_template.yaml.j2 deleted file mode 100644 index 3fd0bcf4f5bea71f46662227cdc3ca77481d814f..0000000000000000000000000000000000000000 --- a/app/templates/steps_template.yaml.j2 +++ /dev/null @@ -1,16 +0,0 @@ -steps: -{% for step in steps -%} - - step: {{ step.step }} - {% if step.model -%} - settings: - model: {{ step.model }} - model_settings: - model: {{ step.model_settings.model }} - {% endif -%} - {% if step.settings -%} - settings: - {% for key, value in step.settings.items() -%} - {{ key }}: {{ value }} - {% endfor -%} - {% endif -%} -{% endfor %} diff --git a/app/utils/lang_helper.py b/app/utils/lang_helper.py deleted file mode 100644 index bbe0bfe5fd8d1ed53849f56e77ab88eadd4d6b97..0000000000000000000000000000000000000000 --- a/app/utils/lang_helper.py +++ /dev/null @@ -1,7 +0,0 @@ -import gradio as gr - - -def get_tab_updates(selected_language, TAB_LABELS): - """Helper to generate tab updates for the selected language.""" - labels = TAB_LABELS[selected_language] - return [gr.update(label=label) for label in labels] diff --git a/app/utils/yaml_helper.py b/app/utils/yaml_helper.py index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..aca153fd82ae1a5b0122af970557bdddabb9a5e2 100644 --- a/app/utils/yaml_helper.py +++ b/app/utils/yaml_helper.py @@ -0,0 +1,91 @@ +from jinja2 import Environment, FileSystemLoader + + +def get_yaml_button_fn( + method, + output_formats, + reading_order, + simple_segment_model=None, + simple_htr_model=None, + simple_htr_model_type=None, + simple_segment_model_type=None, + nested_segment_model_1=None, + nested_segment_model_2=None, + nested_htr_model=None, + nested_segment_model_1_type=None, + nested_segment_model_2_type=None, + nested_htr_model_type=None, +): + env = Environment(loader=FileSystemLoader("app/templates")) + + if output_formats is None: + output_formats = ["txt"] + + template_name = "steps_template.yaml.j2" + try: + if method == "Simple layout": + steps = [ + { + "step": "Segmentation", + "model": simple_segment_model_type, + "model_settings": {"model": simple_segment_model}, + }, + { + "step": "TextRecognition", + "model": simple_htr_model_type, + "model_settings": {"model": simple_htr_model}, + }, + ] + elif method == "Nested segmentation": + steps = [ + { + "step": "Segmentation", + "model": nested_segment_model_1_type, + "model_settings": {"model": nested_segment_model_1}, + }, + { + "step": "Segmentation", + "model": nested_segment_model_2_type, + "model_settings": {"model": nested_segment_model_2}, + }, + { + "step": "TextRecognition", + "model": nested_htr_model_type, + "model_settings": {"model": nested_htr_model}, + }, + ] + else: + return "Invalid method or not yet supported." + + steps.append({"step": reading_order}) + + # TODO: fix reading order + # - step: ReadingOrderMarginalia + # settings: + # two_page: always + + # TODO: fix labeling format + # # Sets label format to regionX_lineY_wordZ + # labels: + # level_labels: + # - region + # - line + # - word + # sep: _ + # template: "{label}{number}" + + steps.extend( + { + "step": "Export", + "settings": {"format": format, "dest": f"{format}-outputs"}, + } + for format in output_formats + ) + + template = env.get_template(template_name) + + yaml_value = template.render(steps=steps) + return yaml_value + + except Exception as e: + return f"Error generating YAML: {str(e)}" diff --git a/notebooks/demo_api.ipynb b/notebooks/demo_api.ipynb deleted file mode 100644 index d38d8176728c08b42bc636815f2e99aa14474ed5..0000000000000000000000000000000000000000 --- a/notebooks/demo_api.ipynb +++ /dev/null @@ -1,95 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded as API: http://127.0.0.1:7860/ ✔\n", - "\n", - "\n", - " \n", - " Swedish National Archives\n", - " 2023-11-23, 09:41:42\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " Hushållspenningar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " Priminieration å Snällporten\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " Gasverksräkning för för ä kronor\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n" - ] - } - ], - "source": [ - "from gradio_client import Client # pip install gradio_client\n", - "\n", - "# Change url to your client (localhost: http://127.0.0.1:7860/)\n", - "\n", - "client = Client(\"http://127.0.0.1:7860/\")\n", - "job = client.submit(\n", - " \"https://github.com/Swedish-National-Archives-AI-lab/htrflow_core/blob/main/data/raw/demo_image.jpg?raw=true\",\n", - " \"Riksarkivet/satrn_htr\",\n", - " api_name=\"/run_htr_pipeline\",\n", - ")\n", - "\n", - "print(job.result())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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": "3.10.9" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/todo.txt b/todo.txt deleted file mode 100644 index 6eb98dccb4d023fa259469adc737fd67b4d3d6cc..0000000000000000000000000000000000000000 --- a/todo.txt +++ /dev/null @@ -1,17 +0,0 @@ - -TODO: laang, shoulde perhaps est to do it with change event. and update the value on each component. Also we should find a nice way to keep lang stae if we update, e.g. use browser state. -TODO: graph viz of pipeline -TODO: Seperate analysis tab, https://www.gradio.app/docs/gradio/highlightedtext,https://huggingface.co/spaces/pngwn/gradio_imageslider -TODO: Seperate "fiftyone tab", använd https://www.gradio.app/docs/gradio/gallery -TODO: add support for batch inference and you can load image trough filepath or s3. -TODO: support hämta från iiif-lb för att köra inference på custom run. -TODO: accordin on sidetab -TODO: alot of documentation / tutorials -TODO: toggle dark and light mode: https://github.com/gradio-app/gradio/issues/7384 -TODO: ssr mode: https://www.gradio.app/docs/gradio/blocks -TODO: enable monitoring and test api mode: https://www.gradio.app/docs/gradio/blocks -TODO: tes usage of modal: https://www.gradio.app/custom-components/gallery -TODO: new docker container.. https://huggingface.co/spaces/pngwn/gradio-docker/blob/main/Dockerfile, need cuda and uv stuff.. -- use https://www.gradio.app/docs/gradio/set_static_paths -- https://huggingface.co/spaces/Wauplin/space_to_dataset_saver -- https://www.freddyboulton.com/blog/gradio-logfire \ No newline at end of file diff --git a/uv.lock b/uv.lock index d649878cce2004d5cc46d9e78bdb73b791e65004..1ef05a5ab76bb039c76b62bfb301fa4109f11b33 100644 --- a/uv.lock +++ b/uv.lock @@ -1,25 +1,18 @@ version = 1 requires-python = ">=3.10, <3.13" resolution-markers = [ - "python_full_version < '3.11' and platform_system == 'Darwin' and sys_platform == 'darwin'", - "python_full_version < '3.11' and platform_system == 'Darwin' and sys_platform == 'win32'", - "python_full_version < '3.11' and platform_system == 'Darwin' and sys_platform != 'darwin' and sys_platform != 'win32'", - "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'win32'", - "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 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