NLU-61: Jenkins release

README.md

@@ -13,7 +13,7 @@ See how easy it is to use any of the **thousands** of models in 1 line of code,
 This 1 line let's you visualize and play with **1000+ SOTA NLU & NLP models** in **200** languages 
 
 ```shell
-streamlit run https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/examples/streamlit/01_dashboard.py
+streamlit run https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/examples/streamlit/01_dashboard.py 
 ```
 <img  src="https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/docs/assets/streamlit_docs_assets/gif/start.gif">
 

VERSION

@@ -0,0 +1 @@
+1.1.11

examples/colab/ocr/ocr_form_relation.ipynb

@@ -0,0 +1,183 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/ocr/ocr_form_relation_extractor.ipynb)\n",
+    "\n",
+    "[Tutorial Notebook](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/ocr/ocr_form_relation_extractor.ipynb \"https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/ocr/ocr_form_relation_extractor.ipynb\")\n"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# **FormRelationExtractor**\n",
+    "\n",
+    "\n",
+    "The **FormRelationExtractor** is a tool designed to identify the relationships between keys and values. It’s particularly useful in the context of data extracted by a Named Entity Recognition (NER) system, such as VisualDocumentNER.\n",
+    "\n",
+    "**All the available models:**\n",
+    "\n",
+    "| NLU Spell            | Transformer Class                                                                       |\n",
+    "|----------------------|-----------------------------------------------------------------------------------------|\n",
+    "| nlu.load(`visual_form_relation_extractor`) | [FormRelationExtractor](https://nlp.johnsnowlabs.com/docs/en/ocr_visual_document_understanding#formrelationextractor) |"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **Install NLU**"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "!pip install johnsnowlabs\n",
+    "nlp.install(visual=True,force_browser=True)\n",
+    "nlp.start(visual=True)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **Form Relation Extraction**"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🚨 Outdated Medical Secrets in license file. Version=5.3.1 but should be Version=5.1.1\n",
+      "🚨 Outdated OCR Secrets in license file. Version=5.3.1 but should be Version=5.0.2\n",
+      "📋 Loading license number 0 from C:\\Users\\gadde/.johnsnowlabs\\licenses/license_number_0_for_Spark-Healthcare_Spark-OCR.json\n",
+      "👷 Trying to install compatible secrets. Use nlp.settings.enforce_versions=False if you want to install outdated secrets.\n",
+      "👷 Trying to install compatible secrets. Use nlp.settings.enforce_versions=False if you want to install outdated secrets.\n",
+      "👷 Setting up  John Snow Labs home in C:\\Users\\gadde/.johnsnowlabs, this might take a few minutes.\n",
+      "Downloading 🫘+🚀 Java Library spark-nlp-assembly-5.1.1.jar\n",
+      "🙆 JSL Home setup in C:\\Users\\gadde/.johnsnowlabs\n",
+      "🤓 Looks like you are missing some jars, trying fetching them ...\n",
+      "👷 Trying to install compatible secrets. Use nlp.settings.enforce_versions=False if you want to install outdated secrets.\n",
+      "Downloading 🫘+💊 Java Library spark-nlp-jsl-5.1.1.jar\n",
+      "Downloading 🫘+🕶 Java Library spark-ocr-assembly-5.0.2.jar\n",
+      "🙆 JSL Home setup in C:\\Users\\gadde/.johnsnowlabs\n",
+      "👷 Trying to install compatible secrets. Use nlp.settings.enforce_versions=False if you want to install outdated secrets.\n",
+      "👌 Launched \u001B[92mcpu optimized\u001B[39m session with with: 🚀Spark-NLP==5.3.1, 💊Spark-Healthcare==5.1.1, 🕶Spark-OCR==5.0.2, running on ⚡ PySpark==3.1.2\n",
+      "Warning::Spark Session already created, some configs may not take.\n",
+      "Warning::Spark Session already created, some configs may not take.\n",
+      "lilt_roberta_funsd_v1 download started this may take some time.\n",
+      "Approximate size to download 419.6 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "from johnsnowlabs import nlp,visual\n",
+    "model = nlp.load('visual_form_relation_extractor')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-05-13T08:27:37.781697200Z",
+     "start_time": "2024-05-13T08:17:43.901075500Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning::Spark Session already created, some configs may not take.\n"
+     ]
+    }
+   ],
+   "source": [
+    "res = model.predict(['form.png','form2.jpg'])"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "      form_relation_prediction_key   form_relation_prediction_value  \\\n0                         division                    allied health   \n0                           course                              hce   \n0                           number                              116   \n0                            title      calculations medical dosage   \n0                          credits                                2   \n0                     developed by                      dr . by taz   \n0  lecture / lab lecture / o ratio                                2   \n0                  course activity                               no   \n0                         cip code                        51 . 0800   \n0                         semester                         fall and   \n0                      ge category                             none   \n0                     separate lab                               no   \n0                 course awareness                               no   \n0                           course                               no   \n1                           name :                   dribbler , bbb   \n1                     study date :          12 - 09 - 2006 , 6 : 34   \n1                             bp :                    120 / 80 mmhg   \n1                            mrn :                   12341820060912   \n1               patient location :                             room   \n1                             hr :                          100 bpm   \n1                            dob :                   19 - 06 - 1979   \n1                         gender :                             male   \n1                         height :                           123 cm   \n1                            age :                         27 years   \n1                         weight :                            25 kg   \n1               reason for study :                               mi   \n1                            bsa :                         0 . 92 m   \n1                        history :                       asfgfdgsdg   \n1                    medications :           heparine , paracetamol   \n1                      performed .  the study technically limited .   \n1                                .                               no   \n\n                                                path  \n0  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n0  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n0  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n0  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n0  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n0  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n0  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n0  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n0  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n0  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n0  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n0  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n0  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n0  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n1  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n1  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n1  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n1  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n1  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n1  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n1  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n1  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n1  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n1  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n1  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n1  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n1  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n1  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n1  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n1  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  \n1  file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>form_relation_prediction_key</th>\n      <th>form_relation_prediction_value</th>\n      <th>path</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>division</td>\n      <td>allied health</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>course</td>\n      <td>hce</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>number</td>\n      <td>116</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>title</td>\n      <td>calculations medical dosage</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>credits</td>\n      <td>2</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>developed by</td>\n      <td>dr . by taz</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>lecture / lab lecture / o ratio</td>\n      <td>2</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>course activity</td>\n      <td>no</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>cip code</td>\n      <td>51 . 0800</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>semester</td>\n      <td>fall and</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>ge category</td>\n      <td>none</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>separate lab</td>\n      <td>no</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>course awareness</td>\n      <td>no</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>course</td>\n      <td>no</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>name :</td>\n      <td>dribbler , bbb</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>study date :</td>\n      <td>12 - 09 - 2006 , 6 : 34</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>bp :</td>\n      <td>120 / 80 mmhg</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>mrn :</td>\n      <td>12341820060912</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>patient location :</td>\n      <td>room</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>hr :</td>\n      <td>100 bpm</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>dob :</td>\n      <td>19 - 06 - 1979</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>gender :</td>\n      <td>male</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>height :</td>\n      <td>123 cm</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>age :</td>\n      <td>27 years</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>weight :</td>\n      <td>25 kg</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>reason for study :</td>\n      <td>mi</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>bsa :</td>\n      <td>0 . 92 m</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>history :</td>\n      <td>asfgfdgsdg</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>medications :</td>\n      <td>heparine , paracetamol</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>performed .</td>\n      <td>the study technically limited .</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>.</td>\n      <td>no</td>\n      <td>file:/F:/Work/repos/nlu_new/ner/nlu/examples/c...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res_filtered = res[['form_relation_prediction_key','form_relation_prediction_value','path']]\n",
+    "res_filtered"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-05-13T08:40:51.701641600Z",
+     "start_time": "2024-05-13T08:40:51.627215600Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "name": "myenv",
+   "language": "python",
+   "display_name": "myenv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

examples/colab/ocr/table_extraction.ipynb

@@ -2752,4 +2752,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}

nlu/__init__.py

@@ -1,6 +1,3 @@
-__version__ = '5.1.5rc19'
-
-
 import nlu.utils.environment.env_utils as env_utils
 
 if not env_utils.try_import_pyspark_in_streamlit():
@@ -23,6 +20,13 @@
 import pandas as pd
 pd.DataFrame.iteritems = pd.DataFrame.items
 
+def version():
+    version_path = os.path.abspath(os.path.dirname(__file__))
+    with open(os.path.join(version_path, '../VERSION'), encoding="utf-8") as version_file:
+        return version_file.read().strip()
+
+__version__ = version()
+
 def version(): return __version__
 
 
@@ -325,7 +329,7 @@ def load_nlu_pipe_from_hdd(pipe_path, request) -> NLUPipeline:
     If it is a component_list,  load the component_list and return it.
     If it is a singular model_anno_obj, load it to the correct AnnotatorClass and NLU component_to_resolve and then generate pipeline for it
     """
-    if is_running_in_databricks():
+    if is_running_in_databricks_runtime():
         return load_nlu_pipe_from_hdd_in_databricks(pipe_path, request)
     pipe = NLUPipeline()
     pipe.nlu_ref = request

nlu/ocr_components/form_relation_extractor/form_relation_extractor.py

@@ -0,0 +1,8 @@
+
+class FormRelationExtractor:
+    @staticmethod
+    def get_default_model():
+        from sparkocr.transformers import FormRelationExtractor
+        return FormRelationExtractor() \
+            .setInputCol("text_entity") \
+            .setOutputCol("ocr_relations")

nlu/ocr_components/utils/hocr_tokenizer/hocr_tokenizer.py

@@ -0,0 +1,7 @@
+class HocrTokenizer:
+    @staticmethod
+    def get_default_model():
+        from sparkocr.transformers import HocrTokenizer
+        return HocrTokenizer() \
+            .setInputCol("hocr") \
+            .setOutputCol("text_tokenized")

nlu/ocr_components/visual_ner/visual_document_ner/visual_document_ner.py

@@ -0,0 +1,8 @@
+class VisualDocumentNer:
+    @staticmethod
+    def get_default_model():
+        from sparkocr.transformers import VisualDocumentNer
+        return VisualDocumentNer()\
+            .pretrained("lilt_roberta_funsd_v1", "en", "clinical/ocr")\
+            .setInputCols(["text_tokenized", "image"])\
+            .setOutputCol("text_entity")