carpelan commited on
Commit
c9d685b
·
1 Parent(s): 1e41501

minor fixes in docs and sidebar

Browse files
.github/README.md CHANGED
@@ -109,6 +109,12 @@ Then open your web browser and navigate to `http://localhost:7860` (or the addre
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  ## Running with Docker
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  ### Locally with Docker
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  1. **Build the Docker Image:**
 
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  ## Running with Docker
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+ Please remove this line in dockerfile, when running locally:
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+ ```sh
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+ ENV PYTHONUNBUFFERED=1 \
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+ SYSTEM=spaces \
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+ ```
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+
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  ### Locally with Docker
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  1. **Build the Docker Image:**
Dockerfile CHANGED
@@ -9,7 +9,7 @@ ENV PYTHONUNBUFFERED=1 \
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  GRADIO_SERVER_NAME=0.0.0.0 \
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  GRADIO_THEME=huggingface \
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  GRADIO_CACHE_DIR=/home/appuser/.gradio_cache \
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- SYSTEM=spaces \
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  AM_I_IN_A_DOCKER_CONTAINER=Yes \
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  PYTHONPATH=/home/appuser/app \
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  HF_HOME=/home/appuser/.cache \
 
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  GRADIO_SERVER_NAME=0.0.0.0 \
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  GRADIO_THEME=huggingface \
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  GRADIO_CACHE_DIR=/home/appuser/.gradio_cache \
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+ SYSTEM=spaces \
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  AM_I_IN_A_DOCKER_CONTAINER=Yes \
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  PYTHONPATH=/home/appuser/app \
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  HF_HOME=/home/appuser/.cache \
app/content/sidebar.md CHANGED
@@ -1,15 +1,24 @@
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- ## HTRflow Demo
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- This web application is developed by the **National Archives of Sweden** as part of [Huminfra](https://www.huminfra.se/)
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- With this demo, users can explore step by step how **AI transforms historical manuscripts into digital text**. Behind the scenes, it runs our open-source **Python package** [HTRflow](https://ai-riksarkivet.github.io/htrflow/latest). Both the app and HTRflow’s code are fully open source.
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- ### How It Works
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- The demo follows a simple three-step workflow:
 
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- 1. **Upload** - Upload your own image and run HTRflow on it.
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- 2. **Results** – View the transcribed text generated by HTRflow.
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- 3. **Export** – Select your preferred format and download the results.
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- 📌 **Note:** This is a **demo application** and is not intended for production use.
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+ ## HTRflow Demo
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+ Welcome to the **HTRflow Demo** – a web application developed by the **National Archives of Sweden** in collaboration with [Huminfra](https://www.huminfra.se/). This demo lets you explore how AI transforms historical manuscripts into digital text using[HTRflow](https://ai-riksarkivet.github.io/htrflow/latest).
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+ ### How It Works
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+ 1. **Upload:** Submit your own image to run through HTRflow.
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+ 2. **Results:** View the transcribed text generated by the system.
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+ 3. **Export:** Choose your preferred format and download your results.
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+ > Note: This demo application is for demonstration purposes only and is not intended for production use.
 
 
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+ ### Contact
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+
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+ ### Projects
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+
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+ Both the App and HTRflow's code are completely open source. Explore and contribute on GitHub:
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+
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+ - [APP](https://github.com/AI-Riksarkivet/htrflow_app).
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+ - [HTRflow](https://ai-riksarkivet.github.io/htrflow/latest).
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+
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+ If you find our projects useful, please consider giving us a star ⭐!
app/main.py CHANGED
@@ -57,11 +57,14 @@ matomo = """
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  <!-- End Matomo Code -->
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  """
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  with gr.Blocks(
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  title="HTRflow",
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  theme=theme,
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  css=css,
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- head=matomo,
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  ) as demo:
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  gr.Markdown(load_markdown(None, "main_title"))
 
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  <!-- End Matomo Code -->
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  """
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+ system_env = os.getenv("SYSTEM", "local")
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+ head_content = matomo if system_env == "spaces" else None
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+
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  with gr.Blocks(
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  title="HTRflow",
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  theme=theme,
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  css=css,
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+ head=head_content,
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  ) as demo:
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  gr.Markdown(load_markdown(None, "main_title"))
app/tabs/submit.py CHANGED
@@ -9,7 +9,6 @@ from htrflow.pipeline.pipeline import Pipeline
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  from htrflow.pipeline.steps import init_step
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  from htrflow.volume.volume import Collection
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- from app.main import load_markdown
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  from app.pipelines import PIPELINES
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  logger = logging.getLogger(__name__)
@@ -289,7 +288,7 @@ with gr.Blocks() as submit:
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  image_iiif_url.submit(
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  fn=return_iiif_url, inputs=image_iiif_url, outputs=batch_image_gallery
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- )
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  run_button.click(
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  fn=run_htrflow,
@@ -301,3 +300,5 @@ with gr.Blocks() as submit:
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  examples.select(get_selected_example_pipeline, None, pipeline_dropdown)
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  edit_pipeline_button.click(lambda: Modal(visible=True), None, edit_pipeline_modal)
 
 
 
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  from htrflow.pipeline.steps import init_step
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  from htrflow.volume.volume import Collection
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  from app.pipelines import PIPELINES
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  logger = logging.getLogger(__name__)
 
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  image_iiif_url.submit(
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  fn=return_iiif_url, inputs=image_iiif_url, outputs=batch_image_gallery
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+ ).then(fn=lambda: "Swedish - Spreads", outputs=pipeline_dropdown)
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  run_button.click(
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  fn=run_htrflow,
 
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  examples.select(get_selected_example_pipeline, None, pipeline_dropdown)
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  edit_pipeline_button.click(lambda: Modal(visible=True), None, edit_pipeline_modal)
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+
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+ # TODO: submit on image_id .. --> yaml swedish - spreads