Save table prediction in cells format (#335)

This pull request allows to return predictions in raw cell
representation from table transformer. It will used in to 
save prediction in a cells format for simpler metrics calculation

---------

Co-authored-by: Yao You <[email protected]>

CHANGELOG.md

@@ -1,3 +1,7 @@
+## 0.7.28
+
+* feat: allow table transformer agent to return table prediction in not parsed format
+
 ## 0.7.27
 
 * fix: remove pin from `onnxruntime` dependency.

test_unstructured_inference/models/test_tables.py

@@ -932,6 +932,25 @@ def test_table_prediction_output_format(
         assert expectation in result
 
 
+def test_table_prediction_output_format_when_wrong_type_then_value_error(
+    table_transformer,
+    example_image,
+    mocker,
+    example_table_cells,
+    mocked_ocr_tokens,
+):
+    mocker.patch.object(tables, "recognize", return_value=example_table_cells)
+    mocker.patch.object(
+        tables.UnstructuredTableTransformerModel,
+        "get_structure",
+        return_value=None,
+    )
+    with pytest.raises(ValueError):
+        table_transformer.run_prediction(
+            example_image, result_format="Wrong format", ocr_tokens=mocked_ocr_tokens
+        )
+
+
 def test_table_prediction_runs_with_empty_recognize(
     table_transformer,
     example_image,

unstructured_inference/__version__.py

@@ -1 +1 @@
-__version__ = "0.7.27"  # pragma: no cover
+__version__ = "0.7.28"  # pragma: no cover

unstructured_inference/models/tables.py

@@ -27,7 +27,12 @@ class UnstructuredTableTransformerModel(UnstructuredModel):
     def __init__(self):
         pass
 
-    def predict(self, x: PILImage.Image, ocr_tokens: Optional[List[Dict]] = None):
+    def predict(
+        self,
+        x: PILImage.Image,
+        ocr_tokens: Optional[List[Dict]] = None,
+        result_format: str = "html",
+    ):
         """Predict table structure deferring to run_prediction with ocr tokens
 
         Note:
@@ -44,7 +49,7 @@ def predict(self, x: PILImage.Image, ocr_tokens: Optional[List[Dict]] = None):
         FIXME: refactor token data into a dataclass so we have clear expectations of the fields
         """
         super().predict(x)
-        return self.run_prediction(x, ocr_tokens=ocr_tokens)
+        return self.run_prediction(x, ocr_tokens=ocr_tokens, result_format=result_format)
 
     def initialize(
         self,
@@ -109,6 +114,14 @@ def run_prediction(
             prediction = cells_to_html(prediction) or ""
         elif result_format == "dataframe":
             prediction = table_cells_to_dataframe(prediction)
+        elif result_format == "cells":
+            prediction = prediction
+        else:
+            raise ValueError(
+                f"result_format {result_format} is not a valid format. "
+                f'Valid formats are: "html", "dataframe", "cells"'
+            )
+
         return prediction