Revert

docs/source/tutorials/bert_classifier.md

@@ -15,6 +15,10 @@ categories:
 image: "/tutorials/bert.png"
 ---
 
+<!--
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 # BERT-based text classification
 
 The NewModel is a BERT-based text classification model that is designed to work with Label Studio. This model uses the Hugging Face Transformers library to fine-tune a BERT model for text classification. The model is trained on the labeled data from Label Studio and then used to make predictions on new data.  With this model connected to Label Studio, you can: 

docs/source/tutorials/easyocr.md

@@ -14,6 +14,10 @@ categories:
 image: "/tutorials/easyocr.png"
 ---
 
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 # EasyOCR model connection
 
 The [EasyOCR](https://github.com/JaidedAI/EasyOCR) model connection is a powerful tool that integrates the capabilities of EasyOCR with Label Studio. It is designed to assist in machine learning labeling tasks, specifically those involving Optical Character Recognition (OCR). 

docs/source/tutorials/flair.md

@@ -14,6 +14,10 @@ categories:
 image: "/tutorials/flair.png"
 ---
 
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+-->
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 # Flair NER example
 
 This example demonstrates how to use Flair NER model with Label Studio.

docs/source/tutorials/gliner.md

@@ -16,6 +16,10 @@ categories:
 image: "/tutorials/gliner.png"
 ---
 
+<!--
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 # Use GLiNER for NER annotation
 
 The GLiNER model is a BERT family model for generalist NER. We download the model from HuggingFace, but the original

docs/source/tutorials/grounding_dino.md

@@ -15,6 +15,10 @@ categories:
 image: "/tutorials/grounding-dino.png"
 ---
 
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 https://github.com/HumanSignal/label-studio-ml-backend/assets/106922533/d1d2f233-d7c0-40ac-ba6f-368c3c01fd36
 
 

docs/source/tutorials/grounding_sam.md

@@ -125,4 +125,4 @@ https://github.com/HumanSignal/label-studio-ml-backend/assets/106922533/79b788e3
 
 Adjust `BOX_THRESHOLD` and `TEXT_THRESHOLD` values in the Dockerfile to a number between 0 to 1 if experimenting. Defaults are set in `dino.py`. For more information about these values, [click here](https://github.com/IDEA-Research/GroundingDINO#star-explanationstips-for-grounding-dino-inputs-and-outputs).
 
-If you want to use SAM models saved from either directories, you can use the `MOBILESAM_CHECKPOINT` and `SAM_CHECKPOINT` as shown in the Dockerfile.
+If you want to use SAM models saved from either directories, you can use the `MOBILESAM_CHECKPOINT` and `SAM_CHECKPOINT` as shown in the Dockerfile.

docs/source/tutorials/huggingface_llm.md

@@ -15,6 +15,10 @@ categories:
 image: "/tutorials/hf-llm.png"
 ---
 
+<!--
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 # Hugging Face Large Language Model backend
 
 This machine learning backend is designed to work with Label Studio, providing a custom model for text generation. The model is based on the Hugging Face's transformers library and uses a pre-trained model.

docs/source/tutorials/huggingface_ner.md

@@ -14,6 +14,10 @@ categories:
 image: "/tutorials/hf-ner.png"
 ---
 
+<!--
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 # Hugging Face NER model with Label Studio
 
 This project uses a custom machine learning backend model for Named Entity Recognition (NER) with Hugging Face's transformers and Label Studio.

docs/source/tutorials/interactive_substring_matching.md

@@ -14,6 +14,10 @@ categories:
 image: "/tutorials/interactive-substring-matching.png"
 ---
 
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 # Interactive substring matching
 
 The Machine Learning (ML) backend is designed to enhance the efficiency of auto-labeling in Named Entity Recognition (NER) tasks. It achieves this by selecting a keyword and automatically matching the same keyword in the provided text. 

docs/source/tutorials/langchain_search_agent.md

@@ -16,6 +16,12 @@ categories:
 image: "/tutorials/langchain.png"
 ---
 
+<!--
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+
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 # Langchain search agent
 
 This example demonstrates how to use Label Studio with a custom Machine Learning backend.

docs/source/tutorials/llm_interactive.md

@@ -17,6 +17,10 @@ categories:
 image: "/tutorials/llm-interactive.png"
 ---
 
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 # Interactive LLM labeling
 
 This example server connects Label Studio to [OpenAI](https://platform.openai.com/), [Ollama](https://ollama.com/),

docs/source/tutorials/mmdetection-3.md

@@ -16,14 +16,18 @@ categories:
 image: "/tutorials/openmmlab.png"
 ---
 
+<!--
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 # Object detection with bounding boxes using MMDetection
 
 https://mmdetection.readthedocs.io/en/latest/
 
 This example demonstrates how to use the MMDetection model with Label Studio to annotate images with bounding boxes. 
 The model is based on the YOLOv3 architecture with a MobileNetV2 backbone and trained on the COCO dataset. 
 
-![screenshot.png](screenshot.png)
+![screenshot.png](/tutorials/screenshot.png)
 
 ## Quick usage
 
@@ -160,4 +164,4 @@ gunicorn --preload --bind :9090 --workers 1 --threads 1 --timeout 0 _wsgi:app
 ```
 
 * Use this guide to find out your access token: https://labelstud.io/guide/api.html
-* You can use and increased value of `SCORE_THRESHOLD` parameter when you see a lot of unwanted detections or lower its value if you don't see any detections.
+* You can use and increased value of `SCORE_THRESHOLD` parameter when you see a lot of unwanted detections or lower its value if you don't see any detections.

docs/source/tutorials/nemo_asr.md

@@ -15,6 +15,10 @@ categories:
 image: "/tutorials/nvidia.png"
 ---
 
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 # ASR with NeMo
 
 This example demonstrates how to use the [NeMo](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/README.md) to perform ASR (Automatic Speech Recognition) in Label Studio.

docs/source/tutorials/segment_anything_2_image.md

@@ -14,6 +14,10 @@ categories:
 image: "/tutorials/sam2-images.png"
 ---
 
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+-->
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 # Using SAM2 with Label Studio for Image Annotation
 
 Segment Anything 2, or SAM 2, is a model released by Meta in July 2024. An update to the original Segment Anything Model, 

docs/source/tutorials/segment_anything_2_video.md

@@ -14,14 +14,18 @@ categories:
 image: "/tutorials/sam2-video.png"
 ---
 
+<!--
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 # Using SAM2 with Label Studio for Video Annotation
 
 This guide describes the simplest way to start using **SegmentAnything 2** with Label Studio.
 
 This repository is specifically for working with object tracking in videos. For working with images, 
 see the [segment_anything_2_image repository](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/segment_anything_2_image)
 
-![sam2](./Sam2Video.gif)
+![sam2](/tutorials/Sam2Video.gif)
 
 ## Running from source
 
@@ -77,4 +81,4 @@ If you want to contribute to this repository to help with some of these limitati
 
 ## Customization
 
-The ML backend can be customized by adding your own models and logic inside the `./segment_anything_2_video` directory.
+The ML backend can be customized by adding your own models and logic inside the `./segment_anything_2_video` directory.

docs/source/tutorials/segment_anything_model.md

@@ -17,6 +17,10 @@ categories:
 image: "/tutorials/segment-anything.png"
 ---
 
+<!--
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 # Interactive annotation in Label Studio with Segment Anything Model
 
 https://github.com/shondle/label-studio-ml-backend/assets/106922533/42a8a535-167c-404a-96bd-c2e2382df99a

docs/source/tutorials/sklearn_text_classifier.md

@@ -14,6 +14,10 @@ categories:
 image: "/tutorials/scikit-learn.png"
 ---
 
+<!--
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 # Sklearn Text Classifier model for Label Studio
 
 The Sklearn Text Classifier model is a custom machine learning backend for Label Studio. It uses a [Logistic Regression model from the Scikit-learn](https://scikit-learn.org/) library to classify text data. This model is particularly useful for text classification tasks in Label Studio, providing an efficient way to generate pre-annotations based on the model's predictions.

docs/source/tutorials/spacy.md

@@ -14,6 +14,10 @@ categories:
 image: "/tutorials/spacy.png"
 ---
 
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+
 This ML backend provides a simple way to use [spaCy](https://spacy.io/) models for Named Entity Recognition (NER) and Part-of-Speech (POS) tagging.
 
 Current implementation includes the following models:

docs/source/tutorials/tesseract.md

@@ -14,6 +14,10 @@ categories:
 image: "/tutorials/tesseract.png"
 ---
 
+<!--
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 # Interactive bounding boxes OCR using Tesseract
 
 Use an OCR engine for interactive ML-assisted labeling, facilitating faster 
@@ -43,7 +47,7 @@ Launch Label Studio. You can follow the guide from the [official documentation](
    docker run -it \
       -p 8080:8080 \
       -v `pwd`/mydata:/label-studio/data \
-      heartex/label-studio:latest
+      heartexlabs/label-studio:latest
    ```
 
    Optionally, you may enable local file serving in Label Studio
@@ -54,7 +58,7 @@ Launch Label Studio. You can follow the guide from the [official documentation](
       -v `pwd`/mydata:/label-studio/data \
       --env LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED=true \
       --env LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT=/label-studio/data/images \
-      heartex/label-studio:latest
+      heartexlabs/label-studio:latest
    ```
    If you're using local file serving, be sure to [get a copy of the API token](https://labelstud.io/guide/user_account#Access-token) from
    Label Studio to connect the model.
@@ -171,4 +175,4 @@ Example below:
 
 Reference links: 
 - https://labelstud.io/blog/Improve-OCR-quality-with-Tesseract-and-Label-Studio.html
-- https://labelstud.io/blog/release-130.html
+- https://labelstud.io/blog/release-130.html

docs/source/tutorials/watsonx_llm.md

@@ -7,12 +7,16 @@ hide_menu: true
 hide_frontmatter_title: true
 meta_title: Integrate WatsonX with Label Studio
 categories:
-    - Computer Vision
+    - Generative AI
     - Large Language Model
     - WatsonX
 image: "/tutorials/watsonx.png"
 ---
 
+<!--
+
+-->
+
 # Integrate WatsonX to Label Studio
 
 WatsonX offers a suite of machine learning tools, including access to many LLMs, prompt
@@ -163,4 +167,4 @@ To get the host and port information below, you can follow the steps under [Pre-
 - `WATSONX_ENG_PORT` - the port information for your WatsonX.data Engine
 - `WATSONX_CATALOG` - the name of the catalog for the table you'll insert your data into. Must be created in the WatsonX.data platform.
 - `WATSONX_SCHEMA` - the name of the schema for the table you'll insert your data into. Must be created in the WatsonX.data platform.
-- `WATSONX_TABLE` - the name of the table you'll insert your data into. Does not need to be already created.
+- `WATSONX_TABLE` - the name of the table you'll insert your data into. Does not need to be already created.