SPARKNLP-1259 Introducing Reader2Doc Annotator

python/sparknlp/reader/reader2doc.py

@@ -0,0 +1,188 @@
+#  Copyright 2017-2025 John Snow Labs
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+from pyspark import keyword_only
+from pyspark.ml.param import TypeConverters, Params, Param
+
+from sparknlp.common import AnnotatorType
+from sparknlp.internal import AnnotatorTransformer
+from sparknlp.partition.partition_properties import *
+
+
+class Reader2Doc(
+    AnnotatorTransformer,
+    HasEmailReaderProperties,
+    HasExcelReaderProperties,
+    HasHTMLReaderProperties,
+    HasPowerPointProperties,
+    HasTextReaderProperties,
+):
+    """
+    The Reader2Doc annotator allows you to use reading files more smoothly within existing
+    Spark NLP workflows, enabling seamless reuse of your pipelines.
+
+    Reader2Doc can be used for extracting structured content from various document types
+    using Spark NLP readers. It supports reading from many file types and returns parsed
+    output as a structured Spark DataFrame.
+
+    Supported formats include:
+
+    - Plain text
+    - HTML
+    - Word (.doc/.docx)
+    - Excel (.xls/.xlsx)
+    - PowerPoint (.ppt/.pptx)
+    - Email files (.eml, .msg)
+    - PDFs
+
+    Examples
+    --------
+    >>> from johnsnowlabs.reader import Reader2Doc
+    >>> from johnsnowlabs.nlp.base import DocumentAssembler
+    >>> from pyspark.ml import Pipeline
+    >>> # Initialize Reader2Doc for PDF files
+    >>> reader2doc = Reader2Doc() \\
+    ...     .setContentType("application/pdf") \\
+    ...     .setContentPath(f"{pdf_directory}/")
+    >>> # Build the pipeline with the Reader2Doc stage
+    >>> pipeline = Pipeline(stages=[reader2doc])
+    >>> # Fit the pipeline to an empty DataFrame
+    >>> pipeline_model = pipeline.fit(empty_data_set)
+    >>> result_df = pipeline_model.transform(empty_data_set)
+    >>> # Show the resulting DataFrame
+    >>> result_df.show()
+    +------------------------------------------------------------------------------------------------------------------------------------+
+    |document                                                                                                                            |
+    +------------------------------------------------------------------------------------------------------------------------------------+
+    |[{'document', 0, 14, 'This is a Title', {'pageNumber': 1, 'elementType': 'Title', 'fileName': 'pdf-title.pdf'}, []}]               |
+    |[{'document', 15, 38, 'This is a narrative text', {'pageNumber': 1, 'elementType': 'NarrativeText', 'fileName': 'pdf-title.pdf'}, []}]|
+    |[{'document', 39, 68, 'This is another narrative text', {'pageNumber': 1, 'elementType': 'NarrativeText', 'fileName': 'pdf-title.pdf'}, []}]|
+    +------------------------------------------------------------------------------------------------------------------------------------+
+"""
+
+    name = "Reader2Doc"
+    outputAnnotatorType = AnnotatorType.DOCUMENT
+
+    contentPath = Param(
+        Params._dummy(),
+        "contentPath",
+        "contentPath path to files to read",
+        typeConverter=TypeConverters.toString,
+    )
+
+    outputCol = Param(
+        Params._dummy(),
+        "outputCol",
+        "output column name",
+        typeConverter=TypeConverters.toString,
+    )
+
+    contentType = Param(
+        Params._dummy(),
+        "contentType",
+        "Set the content type to load following MIME specification",
+        typeConverter=TypeConverters.toString,
+    )
+
+    explodeDocs = Param(
+        Params._dummy(),
+        "explodeDocs",
+        "whether to explode the documents into separate rows",
+        typeConverter=TypeConverters.toBoolean,
+    )
+
+    flattenOutput = Param(
+        Params._dummy(),
+        "flattenOutput",
+        "If true, output is flattened to plain text with minimal metadata",
+        typeConverter=TypeConverters.toBoolean,
+    )
+
+    titleThreshold = Param(
+        Params._dummy(),
+        "titleThreshold",
+        "Minimum font size threshold for title detection in PDF docs",
+        typeConverter=TypeConverters.toFloat,
+    )
+
+    @keyword_only
+    def __init__(self):
+        super(Reader2Doc, self).__init__(classname="com.johnsnowlabs.reader.Reader2Doc")
+        self._setDefault(outputCol="document")
+
+    @keyword_only
+    def setParams(self):
+        kwargs = self._input_kwargs
+        return self._set(**kwargs)
+
+    def setContentPath(self, value):
+        """Sets content path.
+
+        Parameters
+        ----------
+        value : str
+            contentPath path to files to read
+        """
+        return self._set(contentPath=value)
+
+    def setContentType(self, value):
+        """
+        Set the content type to load following MIME specification
+
+        Parameters
+        ----------
+        value : str
+            content type to load following MIME specification
+        """
+        return self._set(contentType=value)
+
+    def setExplodeDocs(self, value):
+        """Sets whether to explode the documents into separate rows.
+
+
+        Parameters
+        ----------
+        value : boolean
+        Whether to explode the documents into separate rows
+        """
+        return self._set(explodeDocs=value)
+
+    def setOutputCol(self, value):
+        """Sets output column name.
+
+        Parameters
+        ----------
+        value : str
+            Name of the Output Column
+        """
+        return self._set(outputCol=value)
+
+    def setFlattenOutput(self, value):
+        """Sets whether to flatten the output to plain text with minimal metadata.
+
+        Parameters
+        ----------
+        value : bool
+            If true, output is flattened to plain text with minimal metadata
+        """
+        return self._set(flattenOutput=value)
+
+    def setTitleThreshold(self, value):
+        """Sets the minimum font size threshold for title detection in PDF documents.
+
+        Parameters
+        ----------
+        value : float
+            Minimum font size threshold for title detection in PDF docs
+        """
+        return self._set(titleThreshold=value)

python/sparknlp/reader/sparknlp_reader.py

@@ -413,4 +413,49 @@ def md(self, filePath):
         if not isinstance(filePath, str):
             raise TypeError("filePath must be a string")
         jdf = self._java_obj.md(filePath)
+        return self.getDataFrame(self.spark, jdf)
+
+    def csv(self, csvPath):
+        """Reads CSV files and returns a Spark DataFrame.
+
+        Parameters
+        ----------
+        docPath : str
+            Path to an CSV file or a directory containing CSV files.
+
+        Returns
+        -------
+        pyspark.sql.DataFrame
+            A DataFrame containing parsed CSV content.
+
+        Examples
+        --------
+        >>> from sparknlp.reader import SparkNLPReader
+        >>> csv_df = SparkNLPReader(spark).csv("home/user/csv-directory")
+
+        You can use SparkNLP for one line of code
+
+        >>> import sparknlp
+        >>> csv_df = sparknlp.read().csv("home/user/csv-directory")
+        >>> csv_df.show(truncate=False)
+        +-----------------------------------------------------------------------------------------------------------------------------------------+
+        |csv                                                                                                                                      |
+        +-----------------------------------------------------------------------------------------------------------------------------------------+
+        |[{NarrativeText, Alice 100 Bob 95, {}}, {Table, <table><tr><td>Alice</td><td>100</td></tr><tr><td>Bob</td><td>95</td></tr></table>, {}}] |
+        +-----------------------------------------------------------------------------------------------------------------------------------------+
+
+        >>> csv_df.printSchema()
+        root
+         |-- path: string (nullable = true)
+         |-- csv: array (nullable = true)
+         |    |-- element: struct (containsNull = true)
+         |    |    |-- elementType: string (nullable = true)
+         |    |    |-- content: string (nullable = true)
+         |    |    |-- metadata: map (nullable = true)
+         |    |    |    |-- key: string
+         |    |    |    |-- value: string (valueContainsNull = true)
+        """
+        if not isinstance(csvPath, str):
+            raise TypeError("docPath must be a string")
+        jdf = self._java_obj.csv(csvPath)
         return self.getDataFrame(self.spark, jdf)

python/test/partition/partition_test.py

@@ -101,7 +101,7 @@ def runTest(self):
         self.assertTrue(html_file_df.select("html").count() > 0)
 
 
-@pytest.mark.fast
+@pytest.mark.slow
 class PartitionUrlTesSpec(unittest.TestCase):
 
     def runTest(self):
@@ -122,8 +122,8 @@ def runTest(self):
         pdf_df = Partition(content_type = "application/pdf").partition(self.html_directory)
         pdf_file_df = Partition().partition(f"{self.html_directory}/text_3_pages.pdf")
 
-        self.assertTrue(pdf_df.select("text").count() > 0)
-        self.assertTrue(pdf_file_df.select("text").count() > 0)
+        self.assertTrue(pdf_df.select("pdf").count() > 0)
+        self.assertTrue(pdf_file_df.select("pdf").count() > 0)
 
 @pytest.mark.fast
 class PartitionTextInMemoryTesSpec(unittest.TestCase):
@@ -139,6 +139,5 @@ def setUp(self):
 
     def runTest(self):
         text_df = Partition(group_broken_paragraphs=True).partition_text(text = self.raw_text )
-        text_df.show(truncate=False)
 
         self.assertTrue(text_df.select("txt").count() > 0)

python/test/partition/partition_transformer_test.py

@@ -79,7 +79,6 @@ def runTest(self):
         pipelineModel = pipeline.fit(emptyDataSet)
 
         resultDf = pipelineModel.transform(self.testDataSet)
-        resultDf.show(truncate=False)
 
         self.assertTrue(resultDf.select("partition").count() > 0)
 
@@ -108,6 +107,5 @@ def runTest(self):
         pipelineModel = pipeline.fit(self.emptyDataSet)
 
         resultDf = pipelineModel.transform(self.emptyDataSet)
-        resultDf.show(truncate=False)
 
         self.assertTrue(resultDf.select("partition").count() >= 0)

python/test/reader/reader2doc_test.py

@@ -0,0 +1,93 @@
+
+#  Copyright 2017-2024 John Snow Labs
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+import unittest
+
+import pytest
+import os
+
+from sparknlp.annotator import *
+from sparknlp.base import *
+from sparknlp.reader.reader2doc import Reader2Doc
+from test.util import SparkContextForTest
+from pyspark.ml import Pipeline
+
+@pytest.mark.fast
+class Reader2DocTest(unittest.TestCase):
+
+    def setUp(self):
+        spark = SparkContextForTest.spark
+        self.empty_df = spark.createDataFrame([], "string").toDF("text")
+
+    def runTest(self):
+        reader2doc = Reader2Doc() \
+            .setContentType("text/html") \
+            .setContentPath(f"file:///{os.getcwd()}/../src/test/resources/reader/html/title-test.html") \
+            .setOutputCol("document")
+
+        pipeline = Pipeline(stages=[reader2doc])
+        model = pipeline.fit(self.empty_df)
+
+        result_df = model.transform(self.empty_df)
+
+        self.assertTrue(result_df.select("document").count() > 0)
+
+
+@pytest.mark.fast
+class Reader2DocTokenTest(unittest.TestCase):
+
+    def setUp(self):
+        spark = SparkContextForTest.spark
+        self.empty_df = spark.createDataFrame([], "string").toDF("text")
+
+    def runTest(self):
+        reader2doc = Reader2Doc() \
+            .setContentType("text/html") \
+            .setContentPath(f"file:///{os.getcwd()}/../src/test/resources/reader/html/example-div.html") \
+            .setOutputCol("document") \
+            .setTitleThreshold(18.5)
+
+        regex_tok = RegexTokenizer() \
+            .setInputCols(["document"]) \
+            .setOutputCol("regex_token")
+
+        pipeline = Pipeline(stages=[reader2doc, regex_tok])
+        model = pipeline.fit(self.empty_df)
+
+        result_df = model.transform(self.empty_df)
+
+        self.assertTrue(result_df.select("document").count() > 0)
+
+
+@pytest.mark.fast
+class Reader2DocPdfTest(unittest.TestCase):
+
+    def setUp(self):
+        spark = SparkContextForTest.spark
+        self.empty_df = spark.createDataFrame([], "string").toDF("text")
+
+    def runTest(self):
+        reader2doc = Reader2Doc() \
+            .setContentType("application/pdf") \
+            .setContentPath(f"file:///{os.getcwd()}/../src/test/resources/reader/pdf/pdf-title.pdf") \
+            .setOutputCol("document") \
+            .setTitleThreshold(18.5)
+
+        pipeline = Pipeline(stages=[reader2doc])
+        model = pipeline.fit(self.empty_df)
+
+        result_df = model.transform(self.empty_df)
+
+        self.assertTrue(result_df.select("document").count() > 0)