From b3b0c8fe373c9dffb555bf8871ac2e006dbe3ef7 Mon Sep 17 00:00:00 2001
From: Tyler <terrifictyler8@gmail.com>
Date: Mon, 31 Jul 2023 17:24:56 -0700
Subject: [PATCH 01/14] added pdf transform and tests

---
 src/autolabel/transforms/__init__.py      |   3 +-
 src/autolabel/transforms/base.py          |   3 +-
 src/autolabel/transforms/pdf.py           |  46 +++++++++++++++
 tests/assets/data_loading/Resume.pdf      | Bin 0 -> 98553 bytes
 tests/unit/data_loaders/test_transform.py |  67 ++++++++++++++++++++++
 5 files changed, 117 insertions(+), 2 deletions(-)
 create mode 100644 src/autolabel/transforms/pdf.py
 create mode 100644 tests/assets/data_loading/Resume.pdf
 create mode 100644 tests/unit/data_loaders/test_transform.py

diff --git a/src/autolabel/transforms/__init__.py b/src/autolabel/transforms/__init__.py
index 855fc0df..10bab44e 100644
--- a/src/autolabel/transforms/__init__.py
+++ b/src/autolabel/transforms/__init__.py
@@ -1,12 +1,13 @@
 import logging
 
 from .base import BaseTransform
+from .pdf import PDFTransform
 from typing import Dict, List
 from autolabel.schema import TransformType
 
 logger = logging.getLogger(__name__)
 
-TRANSFORM_REGISTRY = {}
+TRANSFORM_REGISTRY = {PDFTransform.name(): PDFTransform}
 
 
 class TransformFactory:
diff --git a/src/autolabel/transforms/base.py b/src/autolabel/transforms/base.py
index da9835e2..f49bae24 100644
--- a/src/autolabel/transforms/base.py
+++ b/src/autolabel/transforms/base.py
@@ -7,8 +7,9 @@ def __init__(self, output_columns: List[str]) -> None:
         super().__init__()
         self.output_columns = output_columns
 
+    @staticmethod
     @abstractmethod
-    def name(self) -> str:
+    def name() -> str:
         pass
 
     @abstractmethod
diff --git a/src/autolabel/transforms/pdf.py b/src/autolabel/transforms/pdf.py
new file mode 100644
index 00000000..7e116632
--- /dev/null
+++ b/src/autolabel/transforms/pdf.py
@@ -0,0 +1,46 @@
+from typing import List, Dict, Any
+
+from langchain.document_loaders import PyPDFLoader
+
+from autolabel.transforms import BaseTransform
+
+
+class PDFTransform(BaseTransform):
+    def __init__(
+        self,
+        output_columns: List[str],
+        file_path_column: str,
+        page_header: str = "Page {page_num}: ",
+        page_sep: str = "\n\n",
+    ) -> None:
+        """The output columns for this class should be in the order: [content_column, num_pages_column]"""
+        super().__init__(output_columns)
+        self.file_path_column = file_path_column
+        self.page_header = page_header
+        self.page_sep = page_sep
+
+    @staticmethod
+    def name() -> str:
+        return "pdf"
+
+    def transform(self, row: Dict[str, any]) -> Dict[str, any]:
+        """This function transforms a PDF file into a string of text. It uses the PyPDFLoader to load and split the PDF into pages.
+        Each page is then converted into text and appended to the output string.
+
+        Args:
+            row (Dict[str, any]): The row of data to be transformed.
+
+        Returns:
+            Dict[str, any]: The transformed row of data.
+        """
+        loader = PyPDFLoader(row[self.file_path_column])
+        page_contents = []
+        for idx, page in enumerate(loader.load_and_split()):
+            page_contents.append(
+                self.page_header.format(page_num=idx + 1) + page.page_content
+            )
+        output = self.page_sep.join(page_contents)
+        return {
+            self.output_columns[0]: output,
+            self.output_columns[1]: len(page_contents),
+        }
diff --git a/tests/assets/data_loading/Resume.pdf b/tests/assets/data_loading/Resume.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..90bdb4d6f45ae113815f5d0ec984309de5ef6231
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HcmV?d00001

diff --git a/tests/unit/data_loaders/test_transform.py b/tests/unit/data_loaders/test_transform.py
new file mode 100644
index 00000000..8b97c5f7
--- /dev/null
+++ b/tests/unit/data_loaders/test_transform.py
@@ -0,0 +1,67 @@
+import pytest
+from autolabel.transforms.pdf import PDFTransform
+
+
+RESUME_PDF_CONTENT = """Page 1: Functional Resume Sample 
+ 
+John W. Smith   
+2002 Front Range Way Fort Collins, CO 80525  
+jwsmith@colostate.edu  
+ 
+Career Summary 
+ 
+Four years experience in early childhood development with a di verse background in the care of 
+special needs children and adults.  
+  
+Adult Care Experience  
+ 
+• Determined work placement for 150 special needs adult clients.  
+• Maintained client databases and records.  
+• Coordinated client contact with local health care professionals on a monthly basis.     
+• Managed 25 volunteer workers.     
+ 
+Childcare Experience  
+ 
+• Coordinated service assignments for 20 part -time counselors and 100 client families. 
+• Oversaw daily activity and outing planning for 100 clients.  
+• Assisted families of special needs clients with researching financial assistance and 
+healthcare. 
+• Assisted teachers with managing daily classroom activities.    
+• Oversaw daily and special st udent activities.     
+ 
+Employment History  
+ 1999-2002  Counseling Supervisor, The Wesley Ce nter, Little Rock, Arkansas.    
+1997-1999  Client Specialist, Rainbow Special Ca re Center, Little Rock, Arkansas  
+1996-1997 Teacher’s Assistant, Cowell Elem entary, Conway, Arkansas     
+ 
+Education 
+ 
+University of Arkansas at Little Rock, Little Rock, AR  
+ 
+• BS in Early Childhood Development (1999) 
+• BA in Elementary Education (1998) 
+• GPA (4.0 Scale):  Early Childhood Developm ent – 3.8, Elementary Education – 3.5, 
+Overall 3.4.  
+• Dean’s List, Chancellor’s List"""
+
+
+def test_pdf_transform():
+    # Initialize the PDFTransform class
+    transform = PDFTransform(
+        output_columns=["content", "num_pages"],
+        file_path_column="file_path",
+        page_header="Page {page_num}: ",
+        page_sep="\n\n",
+    )
+
+    # Create a mock row of data
+    row = {"file_path": "tests/assets/data_loading/Resume.pdf"}
+
+    # Transform the row
+    transformed_row = transform.transform(row)
+
+    assert set(transformed_row.keys()) == set(["content", "num_pages"])
+    assert isinstance(transformed_row["content"], str)
+    assert isinstance(transformed_row["num_pages"], int)
+    assert transformed_row["num_pages"] == 1
+    assert transformed_row["content"] == RESUME_PDF_CONTENT

From a4cdc2b23d0a112ba587d95d4024514c99479e58 Mon Sep 17 00:00:00 2001
From: Tyler <terrifictyler8@gmail.com>
Date: Tue, 1 Aug 2023 10:06:42 -0700
Subject: [PATCH 02/14] moved pdf transform to schema and dependencies

---
 pyproject.toml                       | 3 ++-
 src/autolabel/schema.py              | 1 +
 src/autolabel/transforms/__init__.py | 2 +-
 3 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index bcc7d123..a69681cc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -39,7 +39,8 @@ dependencies = [
     "jsonschema >= 4.17.3",
     "tabulate >= 0.9.0",
     "typer[all] >= 0.9.0",
-    "simple-term-menu >= 1.6.1"
+    "simple-term-menu >= 1.6.1",
+    "pypdf >= 3.14.0"
 ]
 requires-python = ">=3.6"
 
diff --git a/src/autolabel/schema.py b/src/autolabel/schema.py
index 1644b23d..e0ef63a9 100644
--- a/src/autolabel/schema.py
+++ b/src/autolabel/schema.py
@@ -218,3 +218,4 @@ class TransformType(str, Enum):
     """Enum containing all Transforms supported by autolabel"""
 
     WEBPAGE_TRANSFORM = "webpage_transform"
+    PDF = "pdf"
diff --git a/src/autolabel/transforms/__init__.py b/src/autolabel/transforms/__init__.py
index 10bab44e..616f7838 100644
--- a/src/autolabel/transforms/__init__.py
+++ b/src/autolabel/transforms/__init__.py
@@ -7,7 +7,7 @@
 
 logger = logging.getLogger(__name__)
 
-TRANSFORM_REGISTRY = {PDFTransform.name(): PDFTransform}
+TRANSFORM_REGISTRY = {TransformType.PDF: PDFTransform}
 
 
 class TransformFactory:

From 6adab26453272be89f09faa89c4471d2ee60b9ba Mon Sep 17 00:00:00 2001
From: Tyler <terrifictyler8@gmail.com>
Date: Tue, 1 Aug 2023 10:20:05 -0700
Subject: [PATCH 03/14] removed usused pytest import

---
 tests/unit/data_loaders/test_transform.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tests/unit/data_loaders/test_transform.py b/tests/unit/data_loaders/test_transform.py
index 8b97c5f7..6615a9e0 100644
--- a/tests/unit/data_loaders/test_transform.py
+++ b/tests/unit/data_loaders/test_transform.py
@@ -1,4 +1,3 @@
-import pytest
 from autolabel.transforms.pdf import PDFTransform
 
 

From 6852cb1b2dbc0f2c544ebbc5994a8fbfb9ebe877 Mon Sep 17 00:00:00 2001
From: Tyler <terrifictyler8@gmail.com>
Date: Tue, 1 Aug 2023 15:42:58 -0700
Subject: [PATCH 04/14] removed the pypdf dependency

---
 pyproject.toml                  | 6 +++---
 src/autolabel/transforms/pdf.py | 8 ++++++--
 2 files changed, 9 insertions(+), 5 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index a69681cc..5a43f5d3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -39,8 +39,7 @@ dependencies = [
     "jsonschema >= 4.17.3",
     "tabulate >= 0.9.0",
     "typer[all] >= 0.9.0",
-    "simple-term-menu >= 1.6.1",
-    "pypdf >= 3.14.0"
+    "simple-term-menu >= 1.6.1"
 ]
 requires-python = ">=3.6"
 
@@ -91,7 +90,8 @@ all = [
     "transformers >= 4.25.0",
     "google-cloud-aiplatform>=1.25.0",
     "cohere>=4.11.2",
-    "sentence_transformers"
+    "sentence_transformers",
+    "pypdf >= 3.14.0"
 ]
 
 [project.urls]
diff --git a/src/autolabel/transforms/pdf.py b/src/autolabel/transforms/pdf.py
index 7e116632..7785bb27 100644
--- a/src/autolabel/transforms/pdf.py
+++ b/src/autolabel/transforms/pdf.py
@@ -1,7 +1,5 @@
 from typing import List, Dict, Any
 
-from langchain.document_loaders import PyPDFLoader
-
 from autolabel.transforms import BaseTransform
 
 
@@ -33,6 +31,12 @@ def transform(self, row: Dict[str, any]) -> Dict[str, any]:
         Returns:
             Dict[str, any]: The transformed row of data.
         """
+        try:
+            from langchain.document_loaders import PyPDFLoader
+        except ImportError:
+            raise ImportError(
+                "pypdf is required to use the pdf transform. Please install pypdf with the following command: pip install pypdf"
+            )
         loader = PyPDFLoader(row[self.file_path_column])
         page_contents = []
         for idx, page in enumerate(loader.load_and_split()):

From 5e89c58a652c15a5c1f1650d2a3f162938c32765 Mon Sep 17 00:00:00 2001
From: Tyler <terrifictyler8@gmail.com>
Date: Tue, 1 Aug 2023 17:07:58 -0700
Subject: [PATCH 05/14] added extract text OCR method

---
 src/autolabel/transforms/pdf.py | 28 +++++++++++++++++++++++-----
 1 file changed, 23 insertions(+), 5 deletions(-)

diff --git a/src/autolabel/transforms/pdf.py b/src/autolabel/transforms/pdf.py
index 7785bb27..8506ed8a 100644
--- a/src/autolabel/transforms/pdf.py
+++ b/src/autolabel/transforms/pdf.py
@@ -1,5 +1,8 @@
 from typing import List, Dict, Any
 
+from pdf2image import convert_from_path
+import pytesseract
+
 from autolabel.transforms import BaseTransform
 
 
@@ -21,6 +24,23 @@ def __init__(
     def name() -> str:
         return "pdf"
 
+    @staticmethod
+    def extract_text(path: str) -> List[str]:
+        """This function extracts text from a PDF file using the pdf2image and pytesseract libraries.
+
+        Args:
+            path (str): The path to the PDF file.
+
+        Returns:
+            List[str]: A list of strings, one for each page of the PDF file.
+        """
+        pages = convert_from_path(path)
+        texts = []
+        for page in pages:
+            text = pytesseract.image_to_string(page)
+            texts.append(text)
+        return texts
+
     def transform(self, row: Dict[str, any]) -> Dict[str, any]:
         """This function transforms a PDF file into a string of text. It uses the PyPDFLoader to load and split the PDF into pages.
         Each page is then converted into text and appended to the output string.
@@ -37,12 +57,10 @@ def transform(self, row: Dict[str, any]) -> Dict[str, any]:
             raise ImportError(
                 "pypdf is required to use the pdf transform. Please install pypdf with the following command: pip install pypdf"
             )
-        loader = PyPDFLoader(row[self.file_path_column])
+        pages = self.extract_text(row[self.file_path_column])
         page_contents = []
-        for idx, page in enumerate(loader.load_and_split()):
-            page_contents.append(
-                self.page_header.format(page_num=idx + 1) + page.page_content
-            )
+        for idx, page in enumerate(pages):
+            page_contents.append(self.page_header.format(page_num=idx + 1) + page)
         output = self.page_sep.join(page_contents)
         return {
             self.output_columns[0]: output,

From e9dbcab3ab3c92dc11ef77812cc2964eacebb9c3 Mon Sep 17 00:00:00 2001
From: Tyler <terrifictyler8@gmail.com>
Date: Wed, 2 Aug 2023 11:49:25 -0700
Subject: [PATCH 06/14] default non-ocr, moved imports to optional

---
 pyproject.toml                            |  2 +-
 src/autolabel/transforms/pdf.py           | 59 ++++++++++++++-----
 tests/unit/data_loaders/test_transform.py | 69 ++++++++++-------------
 3 files changed, 74 insertions(+), 56 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 5a43f5d3..e3c802d5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -91,7 +91,7 @@ all = [
     "google-cloud-aiplatform>=1.25.0",
     "cohere>=4.11.2",
     "sentence_transformers",
-    "pypdf >= 3.14.0"
+    "pdfplumber >= 0.10.2"
 ]
 
 [project.urls]
diff --git a/src/autolabel/transforms/pdf.py b/src/autolabel/transforms/pdf.py
index 8506ed8a..b8b94975 100644
--- a/src/autolabel/transforms/pdf.py
+++ b/src/autolabel/transforms/pdf.py
@@ -1,8 +1,5 @@
 from typing import List, Dict, Any
 
-from pdf2image import convert_from_path
-import pytesseract
-
 from autolabel.transforms import BaseTransform
 
 
@@ -11,12 +8,14 @@ def __init__(
         self,
         output_columns: List[str],
         file_path_column: str,
+        ocr_enabled: bool = False,
         page_header: str = "Page {page_num}: ",
         page_sep: str = "\n\n",
     ) -> None:
         """The output columns for this class should be in the order: [content_column, num_pages_column]"""
         super().__init__(output_columns)
         self.file_path_column = file_path_column
+        self.ocr_enabled = ocr_enabled
         self.page_header = page_header
         self.page_sep = page_sep
 
@@ -26,6 +25,26 @@ def name() -> str:
 
     @staticmethod
     def extract_text(path: str) -> List[str]:
+        """
+        This function extracts text from a PDF file using the pdfplumber library.
+
+        Args:
+            path (str): The path to the PDF file.
+
+        Returns:
+            List[str]: A list of strings, each index containing the extracted text from each page of the PDF file.
+        """
+        try:
+            from langchain.document_loaders import PDFPlumberLoader
+        except ImportError:
+            raise ImportError(
+                "pdfplumber is required to use the pdf transform. Please install pdfplumber with the following command: pip install pdfplumber"
+            )
+        loader = PDFPlumberLoader(path)
+        return [doc.page_content for doc in loader.load()]
+
+    @staticmethod
+    def extract_text_ocr(path: str) -> List[str]:
         """This function extracts text from a PDF file using the pdf2image and pytesseract libraries.
 
         Args:
@@ -34,12 +53,25 @@ def extract_text(path: str) -> List[str]:
         Returns:
             List[str]: A list of strings, one for each page of the PDF file.
         """
+        try:
+            from pdf2image import convert_from_path
+            import pytesseract
+            from pytesseract import TesseractNotFoundError
+        except ImportError:
+            raise ImportError(
+                "pdf2image and pytesseract are required to use the pdf transform with ocr. Please install pdf2image and pytesseract with the following command: pip install pdf2image pytesseract"
+            )
         pages = convert_from_path(path)
-        texts = []
-        for page in pages:
-            text = pytesseract.image_to_string(page)
-            texts.append(text)
-        return texts
+        try:
+            texts = []
+            for page in pages:
+                text = pytesseract.image_to_string(page)
+                texts.append(text)
+            return texts
+        except TesseractNotFoundError:
+            raise ImportError(
+                "The tesseract engine is required to use the pdf transform with ocr. Please see https://tesseract-ocr.github.io/tessdoc/Installation.html for installation instructions."
+            )
 
     def transform(self, row: Dict[str, any]) -> Dict[str, any]:
         """This function transforms a PDF file into a string of text. It uses the PyPDFLoader to load and split the PDF into pages.
@@ -51,13 +83,10 @@ def transform(self, row: Dict[str, any]) -> Dict[str, any]:
         Returns:
             Dict[str, any]: The transformed row of data.
         """
-        try:
-            from langchain.document_loaders import PyPDFLoader
-        except ImportError:
-            raise ImportError(
-                "pypdf is required to use the pdf transform. Please install pypdf with the following command: pip install pypdf"
-            )
-        pages = self.extract_text(row[self.file_path_column])
+        if self.ocr_enabled:
+            pages = self.extract_text_ocr(row[self.file_path_column])
+        else:
+            pages = self.extract_text(row[self.file_path_column])
         page_contents = []
         for idx, page in enumerate(pages):
             page_contents.append(self.page_header.format(page_num=idx + 1) + page)
diff --git a/tests/unit/data_loaders/test_transform.py b/tests/unit/data_loaders/test_transform.py
index 6615a9e0..a7b1b2ab 100644
--- a/tests/unit/data_loaders/test_transform.py
+++ b/tests/unit/data_loaders/test_transform.py
@@ -1,46 +1,35 @@
 from autolabel.transforms.pdf import PDFTransform
 
 
-RESUME_PDF_CONTENT = """Page 1: Functional Resume Sample 
- 
-John W. Smith   
-2002 Front Range Way Fort Collins, CO 80525  
-jwsmith@colostate.edu  
- 
-Career Summary 
- 
-Four years experience in early childhood development with a di verse background in the care of 
-special needs children and adults.  
-  
-Adult Care Experience  
- 
-• Determined work placement for 150 special needs adult clients.  
-• Maintained client databases and records.  
-• Coordinated client contact with local health care professionals on a monthly basis.     
-• Managed 25 volunteer workers.     
- 
-Childcare Experience  
- 
-• Coordinated service assignments for 20 part -time counselors and 100 client families. 
-• Oversaw daily activity and outing planning for 100 clients.  
-• Assisted families of special needs clients with researching financial assistance and 
-healthcare. 
-• Assisted teachers with managing daily classroom activities.    
-• Oversaw daily and special st udent activities.     
- 
-Employment History  
- 1999-2002  Counseling Supervisor, The Wesley Ce nter, Little Rock, Arkansas.    
-1997-1999  Client Specialist, Rainbow Special Ca re Center, Little Rock, Arkansas  
-1996-1997 Teacher’s Assistant, Cowell Elem entary, Conway, Arkansas     
- 
-Education 
- 
-University of Arkansas at Little Rock, Little Rock, AR  
- 
-• BS in Early Childhood Development (1999) 
-• BA in Elementary Education (1998) 
-• GPA (4.0 Scale):  Early Childhood Developm ent – 3.8, Elementary Education – 3.5, 
-Overall 3.4.  
+RESUME_PDF_CONTENT = """Page 1: Functional Resume Sample
+John W. Smith
+2002 Front Range Way Fort Collins, CO 80525
+jwsmith@colostate.edu
+Career Summary
+Four years experience in early childhood development with a diverse background in the care of
+special needs children and adults.
+Adult Care Experience
+• Determined work placement for 150 special needs adult clients.
+• Maintained client databases and records.
+• Coordinated client contact with local health care professionals on a monthly basis.
+• Managed 25 volunteer workers.
+Childcare Experience
+• Coordinated service assignments for 20 part-time counselors and 100 client families.
+• Oversaw daily activity and outing planning for 100 clients.
+• Assisted families of special needs clients with researching financial assistance and
+healthcare.
+• Assisted teachers with managing daily classroom activities.
+• Oversaw daily and special student activities.
+Employment History
+1999-2002 Counseling Supervisor, The Wesley Center, Little Rock, Arkansas.
+1997-1999 Client Specialist, Rainbow Special Care Center, Little Rock, Arkansas
+1996-1997 Teacher’s Assistant, Cowell Elementary, Conway, Arkansas
+Education
+University of Arkansas at Little Rock, Little Rock, AR
+• BS in Early Childhood Development (1999)
+• BA in Elementary Education (1998)
+• GPA (4.0 Scale): Early Childhood Development – 3.8, Elementary Education – 3.5,
+Overall 3.4.
 • Dean’s List, Chancellor’s List"""
 
 

From 19d9c7e456b3c7ccc310bd09e3b6890b73895758 Mon Sep 17 00:00:00 2001
From: Tyler <terrifictyler8@gmail.com>
Date: Wed, 2 Aug 2023 12:07:10 -0700
Subject: [PATCH 07/14] moved imports to constructor

---
 src/autolabel/transforms/pdf.py | 49 ++++++++++++++++++---------------
 1 file changed, 27 insertions(+), 22 deletions(-)

diff --git a/src/autolabel/transforms/pdf.py b/src/autolabel/transforms/pdf.py
index b8b94975..158d5511 100644
--- a/src/autolabel/transforms/pdf.py
+++ b/src/autolabel/transforms/pdf.py
@@ -19,12 +19,32 @@ def __init__(
         self.page_header = page_header
         self.page_sep = page_sep
 
+        if self.ocr_enabled:
+            try:
+                from pdf2image import convert_from_path
+                import pytesseract
+
+                self.convert_from_path = convert_from_path
+                self.pytesseract = pytesseract
+            except ImportError:
+                raise ImportError(
+                    "pdf2image and pytesseract are required to use the pdf transform with ocr. Please install pdf2image and pytesseract with the following command: pip install pdf2image pytesseract"
+                )
+        else:
+            try:
+                from langchain.document_loaders import PDFPlumberLoader
+
+                self.PDFPlumberLoader = PDFPlumberLoader
+            except ImportError:
+                raise ImportError(
+                    "pdfplumber is required to use the pdf transform. Please install pdfplumber with the following command: pip install pdfplumber"
+                )
+
     @staticmethod
     def name() -> str:
         return "pdf"
 
-    @staticmethod
-    def extract_text(path: str) -> List[str]:
+    def extract_text(self, path: str) -> List[str]:
         """
         This function extracts text from a PDF file using the pdfplumber library.
 
@@ -34,17 +54,10 @@ def extract_text(path: str) -> List[str]:
         Returns:
             List[str]: A list of strings, each index containing the extracted text from each page of the PDF file.
         """
-        try:
-            from langchain.document_loaders import PDFPlumberLoader
-        except ImportError:
-            raise ImportError(
-                "pdfplumber is required to use the pdf transform. Please install pdfplumber with the following command: pip install pdfplumber"
-            )
-        loader = PDFPlumberLoader(path)
+        loader = self.PDFPlumberLoader(path)
         return [doc.page_content for doc in loader.load()]
 
-    @staticmethod
-    def extract_text_ocr(path: str) -> List[str]:
+    def extract_text_ocr(self, path: str) -> List[str]:
         """This function extracts text from a PDF file using the pdf2image and pytesseract libraries.
 
         Args:
@@ -53,22 +66,14 @@ def extract_text_ocr(path: str) -> List[str]:
         Returns:
             List[str]: A list of strings, one for each page of the PDF file.
         """
-        try:
-            from pdf2image import convert_from_path
-            import pytesseract
-            from pytesseract import TesseractNotFoundError
-        except ImportError:
-            raise ImportError(
-                "pdf2image and pytesseract are required to use the pdf transform with ocr. Please install pdf2image and pytesseract with the following command: pip install pdf2image pytesseract"
-            )
-        pages = convert_from_path(path)
+        pages = self.convert_from_path(path)
         try:
             texts = []
             for page in pages:
-                text = pytesseract.image_to_string(page)
+                text = self.pytesseract.image_to_string(page)
                 texts.append(text)
             return texts
-        except TesseractNotFoundError:
+        except Exception as e:
             raise ImportError(
                 "The tesseract engine is required to use the pdf transform with ocr. Please see https://tesseract-ocr.github.io/tessdoc/Installation.html for installation instructions."
             )

From 20f3d726e0318579e8fda86fa2204b536e19920a Mon Sep 17 00:00:00 2001
From: Tyler <terrifictyler8@gmail.com>
Date: Fri, 4 Aug 2023 10:39:58 -0700
Subject: [PATCH 08/14] split pdf transform into pdf and pdf_ocr

---
 src/autolabel/schema.py              |  1 +
 src/autolabel/transforms/__init__.py |  6 +-
 src/autolabel/transforms/pdf.py      | 92 +++++++---------------------
 src/autolabel/transforms/pdf_ocr.py  | 61 ++++++++++++++++++
 4 files changed, 89 insertions(+), 71 deletions(-)
 create mode 100644 src/autolabel/transforms/pdf_ocr.py

diff --git a/src/autolabel/schema.py b/src/autolabel/schema.py
index e0ef63a9..756594d0 100644
--- a/src/autolabel/schema.py
+++ b/src/autolabel/schema.py
@@ -219,3 +219,4 @@ class TransformType(str, Enum):
 
     WEBPAGE_TRANSFORM = "webpage_transform"
     PDF = "pdf"
+    PDF_OCR = "pdf_ocr"
diff --git a/src/autolabel/transforms/__init__.py b/src/autolabel/transforms/__init__.py
index 616f7838..c9b88842 100644
--- a/src/autolabel/transforms/__init__.py
+++ b/src/autolabel/transforms/__init__.py
@@ -2,12 +2,16 @@
 
 from .base import BaseTransform
 from .pdf import PDFTransform
+from .pdf_ocr import PDFOCRTransform
 from typing import Dict, List
 from autolabel.schema import TransformType
 
 logger = logging.getLogger(__name__)
 
-TRANSFORM_REGISTRY = {TransformType.PDF: PDFTransform}
+TRANSFORM_REGISTRY = {
+    TransformType.PDF: PDFTransform,
+    TransformType.PDF_OCR: PDFOCRTransform,
+}
 
 
 class TransformFactory:
diff --git a/src/autolabel/transforms/pdf.py b/src/autolabel/transforms/pdf.py
index 158d5511..ab8a62e5 100644
--- a/src/autolabel/transforms/pdf.py
+++ b/src/autolabel/transforms/pdf.py
@@ -8,95 +8,47 @@ def __init__(
         self,
         output_columns: List[str],
         file_path_column: str,
-        ocr_enabled: bool = False,
-        page_header: str = "Page {page_num}: ",
+        page_header: str = "Page {page_num}: {page_content}",
         page_sep: str = "\n\n",
     ) -> None:
         """The output columns for this class should be in the order: [content_column, num_pages_column]"""
         super().__init__(output_columns)
         self.file_path_column = file_path_column
-        self.ocr_enabled = ocr_enabled
-        self.page_header = page_header
+        self.page_format = page_header
         self.page_sep = page_sep
 
-        if self.ocr_enabled:
-            try:
-                from pdf2image import convert_from_path
-                import pytesseract
-
-                self.convert_from_path = convert_from_path
-                self.pytesseract = pytesseract
-            except ImportError:
-                raise ImportError(
-                    "pdf2image and pytesseract are required to use the pdf transform with ocr. Please install pdf2image and pytesseract with the following command: pip install pdf2image pytesseract"
-                )
-        else:
-            try:
-                from langchain.document_loaders import PDFPlumberLoader
+        try:
+            from langchain.document_loaders import PDFPlumberLoader
 
-                self.PDFPlumberLoader = PDFPlumberLoader
-            except ImportError:
-                raise ImportError(
-                    "pdfplumber is required to use the pdf transform. Please install pdfplumber with the following command: pip install pdfplumber"
-                )
+            self.PDFPlumberLoader = PDFPlumberLoader
+        except ImportError:
+            raise ImportError(
+                "pdfplumber is required to use the pdf transform. Please install pdfplumber with the following command: pip install pdfplumber"
+            )
 
     @staticmethod
     def name() -> str:
         return "pdf"
 
-    def extract_text(self, path: str) -> List[str]:
-        """
-        This function extracts text from a PDF file using the pdfplumber library.
-
-        Args:
-            path (str): The path to the PDF file.
-
-        Returns:
-            List[str]: A list of strings, each index containing the extracted text from each page of the PDF file.
-        """
-        loader = self.PDFPlumberLoader(path)
-        return [doc.page_content for doc in loader.load()]
-
-    def extract_text_ocr(self, path: str) -> List[str]:
-        """This function extracts text from a PDF file using the pdf2image and pytesseract libraries.
-
-        Args:
-            path (str): The path to the PDF file.
-
-        Returns:
-            List[str]: A list of strings, one for each page of the PDF file.
-        """
-        pages = self.convert_from_path(path)
-        try:
-            texts = []
-            for page in pages:
-                text = self.pytesseract.image_to_string(page)
-                texts.append(text)
-            return texts
-        except Exception as e:
-            raise ImportError(
-                "The tesseract engine is required to use the pdf transform with ocr. Please see https://tesseract-ocr.github.io/tessdoc/Installation.html for installation instructions."
-            )
-
-    def transform(self, row: Dict[str, any]) -> Dict[str, any]:
-        """This function transforms a PDF file into a string of text. It uses the PyPDFLoader to load and split the PDF into pages.
-        Each page is then converted into text and appended to the output string.
+    async def _apply(self, row: Dict[str, any]) -> Dict[str, any]:
+        """This function transforms a PDF file into a string of text.
+        It uses the pdfplumber library to convert the PDF into text.
+        The text is then formatted according to the page_format and
+        page_sep parameters and returned as a string.
 
         Args:
             row (Dict[str, any]): The row of data to be transformed.
 
         Returns:
-            Dict[str, any]: The transformed row of data.
+            Dict[str, any]: The dict of output columns.
         """
-        if self.ocr_enabled:
-            pages = self.extract_text_ocr(row[self.file_path_column])
-        else:
-            pages = self.extract_text(row[self.file_path_column])
-        page_contents = []
-        for idx, page in enumerate(pages):
-            page_contents.append(self.page_header.format(page_num=idx + 1) + page)
-        output = self.page_sep.join(page_contents)
+        loader = self.PDFPlumberLoader(row[self.file_path_column])
+        texts = []
+        for idx, page in enumerate(loader.load()):
+            text = page.page_content
+            texts.append(self.page_format.format(page_num=idx + 1, page_content=text))
+        output = self.page_sep.join(texts)
         return {
             self.output_columns[0]: output,
-            self.output_columns[1]: len(page_contents),
+            self.output_columns[1]: len(texts),
         }
diff --git a/src/autolabel/transforms/pdf_ocr.py b/src/autolabel/transforms/pdf_ocr.py
new file mode 100644
index 00000000..7906ed3f
--- /dev/null
+++ b/src/autolabel/transforms/pdf_ocr.py
@@ -0,0 +1,61 @@
+from typing import List, Dict
+
+from autolabel.transforms import BaseTransform
+
+
+class PDFOCRTransform(BaseTransform):
+    def __init__(
+        self,
+        output_columns: List[str],
+        file_path_column: str,
+        page_format: str = "Page {page_num}: {page_content}",
+        page_sep: str = "\n\n",
+    ) -> None:
+        """The output columns for this class should be in the order: [content_column, num_pages_column]"""
+        super().__init__(output_columns)
+        self.file_path_column = file_path_column
+        self.page_format = page_format
+        self.page_sep = page_sep
+
+        try:
+            from pdf2image import convert_from_path
+            import pytesseract
+
+            self.convert_from_path = convert_from_path
+            self.pytesseract = pytesseract
+            self.pytesseract.get_tesseract_version()
+        except ImportError:
+            raise ImportError(
+                "pdf2image and pytesseract are required to use the pdf transform with ocr. Please install pdf2image and pytesseract with the following command: pip install pdf2image pytesseract"
+            )
+        except EnvironmentError:
+            raise EnvironmentError(
+                "The tesseract engine is required to use the pdf transform with ocr. Please see https://tesseract-ocr.github.io/tessdoc/Installation.html for installation instructions."
+            )
+
+    @staticmethod
+    def name() -> str:
+        return "pdf_ocr"
+
+    async def _apply(self, row: Dict[str, any]) -> Dict[str, any]:
+        """This function transforms a PDF file into a string of text using OCR.
+        It uses the pdf2image library to convert the PDF into images and then uses
+        pytesseract to convert the images into text. The text is then formatted
+        according to the page_format and page_sep parameters and returned as a string.
+
+        Args:
+            row (Dict[str, any]): The row of data to be transformed.
+
+        Returns:
+            Dict[str, any]: The dict of output columns.
+        """
+        pages = self.convert_from_path(row[self.file_path_column])
+        texts = []
+        for idx, page in enumerate(pages):
+            text = self.pytesseract.image_to_string(page)
+            texts.append(self.page_format.format(page_num=idx + 1, page_content=text))
+        output = self.page_sep.join(texts)
+        return {
+            self.output_columns[0]: output,
+            self.output_columns[1]: len(texts),
+        }

From 9471c3eeb48ac2cdae53bbd39e7e18632fc5d1e7 Mon Sep 17 00:00:00 2001
From: Tyler <terrifictyler8@gmail.com>
Date: Fri, 4 Aug 2023 12:46:41 -0700
Subject: [PATCH 09/14] added dependencies to all

---
 pyproject.toml | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index e3c802d5..97a7fa80 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -91,7 +91,9 @@ all = [
     "google-cloud-aiplatform>=1.25.0",
     "cohere>=4.11.2",
     "sentence_transformers",
-    "pdfplumber >= 0.10.2"
+    "pdfplumber >= 0.10.2",
+    "pdf2image >= 1.16.3",
+    "pytesseract >= 0.3.10"
 ]
 
 [project.urls]

From 045e42a547de627fa56e4649566c45a4c26b9881 Mon Sep 17 00:00:00 2001
From: Tyler <terrifictyler8@gmail.com>
Date: Mon, 7 Aug 2023 10:15:52 -0700
Subject: [PATCH 10/14] both ocr and regular transforms back to one file

---
 src/autolabel/transforms/pdf.py     | 58 +++++++++++++++++++++------
 src/autolabel/transforms/pdf_ocr.py | 61 -----------------------------
 2 files changed, 46 insertions(+), 73 deletions(-)
 delete mode 100644 src/autolabel/transforms/pdf_ocr.py

diff --git a/src/autolabel/transforms/pdf.py b/src/autolabel/transforms/pdf.py
index ab8a62e5..00f080b7 100644
--- a/src/autolabel/transforms/pdf.py
+++ b/src/autolabel/transforms/pdf.py
@@ -8,32 +8,68 @@ def __init__(
         self,
         output_columns: List[str],
         file_path_column: str,
+        ocr_enabled: bool = False,
         page_header: str = "Page {page_num}: {page_content}",
         page_sep: str = "\n\n",
     ) -> None:
         """The output columns for this class should be in the order: [content_column, num_pages_column]"""
         super().__init__(output_columns)
         self.file_path_column = file_path_column
+        self.ocr_enabled = ocr_enabled
         self.page_format = page_header
         self.page_sep = page_sep
 
-        try:
-            from langchain.document_loaders import PDFPlumberLoader
+        if self.ocr_enabled:
+            try:
+                from pdf2image import convert_from_path
+                import pytesseract
 
-            self.PDFPlumberLoader = PDFPlumberLoader
-        except ImportError:
-            raise ImportError(
-                "pdfplumber is required to use the pdf transform. Please install pdfplumber with the following command: pip install pdfplumber"
-            )
+                self.convert_from_path = convert_from_path
+                self.pytesseract = pytesseract
+                self.pytesseract.get_tesseract_version()
+            except ImportError:
+                raise ImportError(
+                    "pdf2image and pytesseract are required to use the pdf transform with ocr. Please install pdf2image and pytesseract with the following command: pip install pdf2image pytesseract"
+                )
+            except EnvironmentError:
+                raise EnvironmentError(
+                    "The tesseract engine is required to use the pdf transform with ocr. Please see https://tesseract-ocr.github.io/tessdoc/Installation.html for installation instructions."
+                )
+        else:
+            try:
+                from langchain.document_loaders import PDFPlumberLoader
+
+                self.PDFPlumberLoader = PDFPlumberLoader
+            except ImportError:
+                raise ImportError(
+                    "pdfplumber is required to use the pdf transform. Please install pdfplumber with the following command: pip install pdfplumber"
+                )
 
     @staticmethod
     def name() -> str:
         return "pdf"
 
+    def get_page_texts(self, row: Dict[str, any]) -> List[str]:
+        """This function gets the text from each page of a PDF file.
+        If OCR is enabled, it uses the pdf2image library to convert the PDF into images and then uses
+        pytesseract to convert the images into text. Otherwise, it uses pdfplumber to extract the text.
+
+        Args:
+            row (Dict[str, any]): The row of data to be transformed.
+
+        Returns:
+            List[str]: A list of strings containing the text from each page of the PDF.
+        """
+        if self.ocr_enabled:
+            pages = self.convert_from_path(row[self.file_path_column])
+            return [self.pytesseract.image_to_string(page) for page in pages]
+        else:
+            loader = self.PDFPlumberLoader(row[self.file_path_column])
+            return [page.page_content for page in loader.load()]
+
     async def _apply(self, row: Dict[str, any]) -> Dict[str, any]:
         """This function transforms a PDF file into a string of text.
-        It uses the pdfplumber library to convert the PDF into text.
-        The text is then formatted according to the page_format and
+        The text is formatted according to the page_format and
         page_sep parameters and returned as a string.
 
         Args:
@@ -42,10 +78,8 @@ async def _apply(self, row: Dict[str, any]) -> Dict[str, any]:
         Returns:
             Dict[str, any]: The dict of output columns.
         """
-        loader = self.PDFPlumberLoader(row[self.file_path_column])
         texts = []
-        for idx, page in enumerate(loader.load()):
-            text = page.page_content
+        for idx, text in enumerate(get_page_texts(row)):
             texts.append(self.page_format.format(page_num=idx + 1, page_content=text))
         output = self.page_sep.join(texts)
         return {
diff --git a/src/autolabel/transforms/pdf_ocr.py b/src/autolabel/transforms/pdf_ocr.py
deleted file mode 100644
index 7906ed3f..00000000
--- a/src/autolabel/transforms/pdf_ocr.py
+++ /dev/null
@@ -1,61 +0,0 @@
-from typing import List, Dict
-
-from autolabel.transforms import BaseTransform
-
-
-class PDFOCRTransform(BaseTransform):
-    def __init__(
-        self,
-        output_columns: List[str],
-        file_path_column: str,
-        page_format: str = "Page {page_num}: {page_content}",
-        page_sep: str = "\n\n",
-    ) -> None:
-        """The output columns for this class should be in the order: [content_column, num_pages_column]"""
-        super().__init__(output_columns)
-        self.file_path_column = file_path_column
-        self.page_format = page_format
-        self.page_sep = page_sep
-
-        try:
-            from pdf2image import convert_from_path
-            import pytesseract
-
-            self.convert_from_path = convert_from_path
-            self.pytesseract = pytesseract
-            self.pytesseract.get_tesseract_version()
-        except ImportError:
-            raise ImportError(
-                "pdf2image and pytesseract are required to use the pdf transform with ocr. Please install pdf2image and pytesseract with the following command: pip install pdf2image pytesseract"
-            )
-        except EnvironmentError:
-            raise EnvironmentError(
-                "The tesseract engine is required to use the pdf transform with ocr. Please see https://tesseract-ocr.github.io/tessdoc/Installation.html for installation instructions."
-            )
-
-    @staticmethod
-    def name() -> str:
-        return "pdf_ocr"
-
-    async def _apply(self, row: Dict[str, any]) -> Dict[str, any]:
-        """This function transforms a PDF file into a string of text using OCR.
-        It uses the pdf2image library to convert the PDF into images and then uses
-        pytesseract to convert the images into text. The text is then formatted
-        according to the page_format and page_sep parameters and returned as a string.
-
-        Args:
-            row (Dict[str, any]): The row of data to be transformed.
-
-        Returns:
-            Dict[str, any]: The dict of output columns.
-        """
-        pages = self.convert_from_path(row[self.file_path_column])
-        texts = []
-        for idx, page in enumerate(pages):
-            text = self.pytesseract.image_to_string(page)
-            texts.append(self.page_format.format(page_num=idx + 1, page_content=text))
-        output = self.page_sep.join(texts)
-        return {
-            self.output_columns[0]: output,
-            self.output_columns[1]: len(texts),
-        }

From 4d948e7cb1ec1b5d5308462ca3dc37df84a5ad54 Mon Sep 17 00:00:00 2001
From: Tyler <terrifictyler8@gmail.com>
Date: Mon, 7 Aug 2023 10:45:57 -0700
Subject: [PATCH 11/14] updated typing and some metadata

---
 src/autolabel/transforms/pdf.py | 50 ++++++++++++++++++++-------------
 1 file changed, 31 insertions(+), 19 deletions(-)

diff --git a/src/autolabel/transforms/pdf.py b/src/autolabel/transforms/pdf.py
index 00f080b7..870a9b19 100644
--- a/src/autolabel/transforms/pdf.py
+++ b/src/autolabel/transforms/pdf.py
@@ -1,12 +1,13 @@
 from typing import List, Dict, Any
 
+from autolabel.schema import TransformType
 from autolabel.transforms import BaseTransform
 
 
 class PDFTransform(BaseTransform):
     def __init__(
         self,
-        output_columns: List[str],
+        output_columns: Dict[str, Any],
         file_path_column: str,
         ocr_enabled: bool = False,
         page_header: str = "Page {page_num}: {page_content}",
@@ -19,6 +20,15 @@ def __init__(
         self.page_format = page_header
         self.page_sep = page_sep
 
+        # PDFPlumber is required for both for metadata extraction
+        try:
+            from langchain.document_loaders import PDFPlumberLoader
+
+            self.PDFPlumberLoader = PDFPlumberLoader
+        except ImportError:
+            raise ImportError(
+                "pdfplumber is required to use the pdf transform. Please install pdfplumber with the following command: pip install pdfplumber"
+            )
         if self.ocr_enabled:
             try:
                 from pdf2image import convert_from_path
@@ -35,27 +45,26 @@ def __init__(
                 raise EnvironmentError(
                     "The tesseract engine is required to use the pdf transform with ocr. Please see https://tesseract-ocr.github.io/tessdoc/Installation.html for installation instructions."
                 )
-        else:
-            try:
-                from langchain.document_loaders import PDFPlumberLoader
-
-                self.PDFPlumberLoader = PDFPlumberLoader
-            except ImportError:
-                raise ImportError(
-                    "pdfplumber is required to use the pdf transform. Please install pdfplumber with the following command: pip install pdfplumber"
-                )
 
     @staticmethod
     def name() -> str:
-        return "pdf"
+        return TransformType.PDF
+
+    @property
+    def output_columns(self) -> Dict[str, Any]:
+        COLUMN_NAMES = [
+            "content_column",
+            "metadata_column",
+        ]
+        return {k: self._output_columns.get(k, k) for k in COLUMN_NAMES}
 
-    def get_page_texts(self, row: Dict[str, any]) -> List[str]:
+    def get_page_texts(self, row: Dict[str, Any]) -> List[str]:
         """This function gets the text from each page of a PDF file.
         If OCR is enabled, it uses the pdf2image library to convert the PDF into images and then uses
         pytesseract to convert the images into text. Otherwise, it uses pdfplumber to extract the text.
 
         Args:
-            row (Dict[str, any]): The row of data to be transformed.
+            row (Dict[str, Any]): The row of data to be transformed.
 
         Returns:
             List[str]: A list of strings containing the text from each page of the PDF.
@@ -67,22 +76,25 @@ def get_page_texts(self, row: Dict[str, any]) -> List[str]:
             loader = self.PDFPlumberLoader(row[self.file_path_column])
             return [page.page_content for page in loader.load()]
 
-    async def _apply(self, row: Dict[str, any]) -> Dict[str, any]:
+    async def _apply(self, row: Dict[str, Any]) -> Dict[str, Any]:
         """This function transforms a PDF file into a string of text.
         The text is formatted according to the page_format and
         page_sep parameters and returned as a string.
 
         Args:
-            row (Dict[str, any]): The row of data to be transformed.
+            row (Dict[str, Any]): The row of data to be transformed.
 
         Returns:
-            Dict[str, any]: The dict of output columns.
+            Dict[str, Any]: The dict of output columns.
         """
         texts = []
         for idx, text in enumerate(get_page_texts(row)):
             texts.append(self.page_format.format(page_num=idx + 1, page_content=text))
         output = self.page_sep.join(texts)
-        return {
-            self.output_columns[0]: output,
-            self.output_columns[1]: len(texts),
+        transformed_row = {
+            self.output_columns["content_column"]: output,
+            self.output_columns["metadata_column"]: {
+                "num_pages": len(texts)
+            },  # TODO: add more metadata
         }
+        return transformed_row

From 87b677e9db056e11e8c31daf10429d3af20b067c Mon Sep 17 00:00:00 2001
From: Tyler <terrifictyler8@gmail.com>
Date: Mon, 7 Aug 2023 11:36:17 -0700
Subject: [PATCH 12/14] added tests and moved import

---
 pyproject.toml                              |  2 +-
 src/autolabel/transforms/pdf.py             | 24 +++++----
 tests/unit/data_loaders/test_transform.py   | 55 ---------------------
 tests/unit/transforms/test_pdf_transform.py | 51 +++++++++++++++++++
 4 files changed, 63 insertions(+), 69 deletions(-)
 delete mode 100644 tests/unit/data_loaders/test_transform.py
 create mode 100644 tests/unit/transforms/test_pdf_transform.py

diff --git a/pyproject.toml b/pyproject.toml
index b24d0e04..deb444cc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -95,7 +95,7 @@ all = [
     "sentence_transformers",
     "pdfplumber >= 0.10.2",
     "pdf2image >= 1.16.3",
-    "pytesseract >= 0.3.10"
+    "pytesseract >= 0.3.10",
     "bs4",
     "httpx",
     "fake_useragent"
diff --git a/src/autolabel/transforms/pdf.py b/src/autolabel/transforms/pdf.py
index 870a9b19..a24d6553 100644
--- a/src/autolabel/transforms/pdf.py
+++ b/src/autolabel/transforms/pdf.py
@@ -20,15 +20,6 @@ def __init__(
         self.page_format = page_header
         self.page_sep = page_sep
 
-        # PDFPlumber is required for both for metadata extraction
-        try:
-            from langchain.document_loaders import PDFPlumberLoader
-
-            self.PDFPlumberLoader = PDFPlumberLoader
-        except ImportError:
-            raise ImportError(
-                "pdfplumber is required to use the pdf transform. Please install pdfplumber with the following command: pip install pdfplumber"
-            )
         if self.ocr_enabled:
             try:
                 from pdf2image import convert_from_path
@@ -45,6 +36,15 @@ def __init__(
                 raise EnvironmentError(
                     "The tesseract engine is required to use the pdf transform with ocr. Please see https://tesseract-ocr.github.io/tessdoc/Installation.html for installation instructions."
                 )
+        else:
+            try:
+                from langchain.document_loaders import PDFPlumberLoader
+
+                self.PDFPlumberLoader = PDFPlumberLoader
+            except ImportError:
+                raise ImportError(
+                    "pdfplumber is required to use the pdf transform. Please install pdfplumber with the following command: pip install pdfplumber"
+                )
 
     @staticmethod
     def name() -> str:
@@ -88,13 +88,11 @@ async def _apply(self, row: Dict[str, Any]) -> Dict[str, Any]:
             Dict[str, Any]: The dict of output columns.
         """
         texts = []
-        for idx, text in enumerate(get_page_texts(row)):
+        for idx, text in enumerate(self.get_page_texts(row)):
             texts.append(self.page_format.format(page_num=idx + 1, page_content=text))
         output = self.page_sep.join(texts)
         transformed_row = {
             self.output_columns["content_column"]: output,
-            self.output_columns["metadata_column"]: {
-                "num_pages": len(texts)
-            },  # TODO: add more metadata
+            self.output_columns["metadata_column"]: {"num_pages": len(texts)},
         }
         return transformed_row
diff --git a/tests/unit/data_loaders/test_transform.py b/tests/unit/data_loaders/test_transform.py
deleted file mode 100644
index a7b1b2ab..00000000
--- a/tests/unit/data_loaders/test_transform.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from autolabel.transforms.pdf import PDFTransform
-
-
-RESUME_PDF_CONTENT = """Page 1: Functional Resume Sample
-John W. Smith
-2002 Front Range Way Fort Collins, CO 80525
-jwsmith@colostate.edu
-Career Summary
-Four years experience in early childhood development with a diverse background in the care of
-special needs children and adults.
-Adult Care Experience
-• Determined work placement for 150 special needs adult clients.
-• Maintained client databases and records.
-• Coordinated client contact with local health care professionals on a monthly basis.
-• Managed 25 volunteer workers.
-Childcare Experience
-• Coordinated service assignments for 20 part-time counselors and 100 client families.
-• Oversaw daily activity and outing planning for 100 clients.
-• Assisted families of special needs clients with researching financial assistance and
-healthcare.
-• Assisted teachers with managing daily classroom activities.
-• Oversaw daily and special student activities.
-Employment History
-1999-2002 Counseling Supervisor, The Wesley Center, Little Rock, Arkansas.
-1997-1999 Client Specialist, Rainbow Special Care Center, Little Rock, Arkansas
-1996-1997 Teacher’s Assistant, Cowell Elementary, Conway, Arkansas
-Education
-University of Arkansas at Little Rock, Little Rock, AR
-• BS in Early Childhood Development (1999)
-• BA in Elementary Education (1998)
-• GPA (4.0 Scale): Early Childhood Development – 3.8, Elementary Education – 3.5,
-Overall 3.4.
-• Dean’s List, Chancellor’s List"""
-
-
-def test_pdf_transform():
-    # Initialize the PDFTransform class
-    transform = PDFTransform(
-        output_columns=["content", "num_pages"],
-        file_path_column="file_path",
-        page_header="Page {page_num}: ",
-        page_sep="\n\n",
-    )
-
-    # Create a mock row of data
-    row = {"file_path": "tests/assets/data_loading/Resume.pdf"}
-
-    # Transform the row
-    transformed_row = transform.transform(row)
-
-    assert set(transformed_row.keys()) == set(["content", "num_pages"])
-    assert isinstance(transformed_row["content"], str)
-    assert isinstance(transformed_row["num_pages"], int)
-    assert transformed_row["num_pages"] == 1
-    assert transformed_row["content"] == RESUME_PDF_CONTENT
diff --git a/tests/unit/transforms/test_pdf_transform.py b/tests/unit/transforms/test_pdf_transform.py
new file mode 100644
index 00000000..8f1f4574
--- /dev/null
+++ b/tests/unit/transforms/test_pdf_transform.py
@@ -0,0 +1,51 @@
+from autolabel.transforms.pdf import PDFTransform
+import pytest
+
+pytest_plugins = ("pytest_asyncio",)
+
+
+@pytest.mark.asyncio
+async def test_pdf_transform():
+    # Initialize the PDFTransform class
+    transform = PDFTransform(
+        output_columns={
+            "content_column": "content",
+            "metadata_column": "metadata",
+        },
+        file_path_column="file_path",
+    )
+
+    # Create a mock row
+    row = {"file_path": "tests/assets/data_loading/Resume.pdf"}
+    # Transform the row
+    transformed_row = await transform.apply(row)
+    # Check the output
+    assert set(transformed_row.keys()) == set(["content", "metadata"])
+    assert isinstance(transformed_row["content"], str)
+    assert isinstance(transformed_row["metadata"], dict)
+    assert len(transformed_row["content"]) > 0
+    assert transformed_row["metadata"]["num_pages"] == 1
+
+
+@pytest.mark.asyncio
+async def test_pdf_transform_ocr():
+    # Initialize the PDFTransform class
+    transform = PDFTransform(
+        output_columns={
+            "content_column": "content",
+            "metadata_column": "metadata",
+        },
+        file_path_column="file_path",
+        ocr_enabled=True,
+    )
+
+    # Create a mock row
+    row = {"file_path": "tests/assets/data_loading/Resume.pdf"}
+    # Transform the row
+    transformed_row = await transform.apply(row)
+    # Check the output
+    assert set(transformed_row.keys()) == set(["content", "metadata"])
+    assert isinstance(transformed_row["content"], str)
+    assert isinstance(transformed_row["metadata"], dict)
+    assert len(transformed_row["content"]) > 0
+    assert transformed_row["metadata"]["num_pages"] == 1

From 205962c428e53f1e5d112a6d7e81c703875aad97 Mon Sep 17 00:00:00 2001
From: Tyler <terrifictyler8@gmail.com>
Date: Mon, 7 Aug 2023 11:41:33 -0700
Subject: [PATCH 13/14] can't test pdf ocr without tesseract engine

---
 tests/unit/transforms/test_pdf_transform.py | 24 ---------------------
 1 file changed, 24 deletions(-)

diff --git a/tests/unit/transforms/test_pdf_transform.py b/tests/unit/transforms/test_pdf_transform.py
index 8f1f4574..59d54e3c 100644
--- a/tests/unit/transforms/test_pdf_transform.py
+++ b/tests/unit/transforms/test_pdf_transform.py
@@ -25,27 +25,3 @@ async def test_pdf_transform():
     assert isinstance(transformed_row["metadata"], dict)
     assert len(transformed_row["content"]) > 0
     assert transformed_row["metadata"]["num_pages"] == 1
-
-
-@pytest.mark.asyncio
-async def test_pdf_transform_ocr():
-    # Initialize the PDFTransform class
-    transform = PDFTransform(
-        output_columns={
-            "content_column": "content",
-            "metadata_column": "metadata",
-        },
-        file_path_column="file_path",
-        ocr_enabled=True,
-    )
-
-    # Create a mock row
-    row = {"file_path": "tests/assets/data_loading/Resume.pdf"}
-    # Transform the row
-    transformed_row = await transform.apply(row)
-    # Check the output
-    assert set(transformed_row.keys()) == set(["content", "metadata"])
-    assert isinstance(transformed_row["content"], str)
-    assert isinstance(transformed_row["metadata"], dict)
-    assert len(transformed_row["content"]) > 0
-    assert transformed_row["metadata"]["num_pages"] == 1

From da0c40c34d124adb65b54b8f22525e1b8c5de602 Mon Sep 17 00:00:00 2001
From: Tyler <terrifictyler8@gmail.com>
Date: Mon, 7 Aug 2023 11:43:38 -0700
Subject: [PATCH 14/14] removed pdf_ocr from schema

---
 src/autolabel/schema.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/autolabel/schema.py b/src/autolabel/schema.py
index 756594d0..e0ef63a9 100644
--- a/src/autolabel/schema.py
+++ b/src/autolabel/schema.py
@@ -219,4 +219,3 @@ class TransformType(str, Enum):
 
     WEBPAGE_TRANSFORM = "webpage_transform"
     PDF = "pdf"
-    PDF_OCR = "pdf_ocr"