Fix minor issues

README.md

@@ -82,7 +82,7 @@ Model weights will automatically download the first time you run surya.
 I've included a streamlit app that lets you interactively try Surya on images or PDF files.  Run it with:
 
 ```shell
-pip install streamlit
+pip install streamlit pdftext
 surya_gui
 ```
 

pyproject.toml

@@ -33,7 +33,6 @@ opencv-python = "^4.9.0.80"
 tabulate = "^0.9.0"
 filetype = "^1.2.0"
 ftfy = "^6.1.3"
-pdftext = "~0.4.1"
 click = "^8.1.8"
 
 [tool.poetry.group.dev.dependencies]
@@ -47,6 +46,7 @@ arabic-reshaper = "^3.0.0"
 streamlit = "^1.31.0"
 playwright = "^1.41.2"
 pytest = "^8.3.4"
+pdftext = "^0.4.1"
 
 [tool.poetry.scripts]
 surya_detect = "detect_text:main"

surya/detection/loader.py

@@ -22,6 +22,11 @@ def model(
             device: Optional[torch.device | str] = None,
             dtype: Optional[torch.dtype | str] = None
     ) -> EfficientViTForSemanticSegmentation:
+        if device is None:
+            device = settings.TORCH_DEVICE_MODEL
+        if dtype is None:
+            dtype = settings.MODEL_DTYPE
+
         config = EfficientViTConfig.from_pretrained(self.checkpoint)
         model = EfficientViTForSemanticSegmentation.from_pretrained(self.checkpoint, torch_dtype=dtype, config=config,
                                                                     ignore_mismatched_sizes=True)

surya/layout/loader.py

@@ -21,6 +21,11 @@ def model(
         device=settings.TORCH_DEVICE_MODEL,
         dtype=settings.MODEL_DTYPE
     ) -> SuryaLayoutModel:
+        if device is None:
+            device = settings.TORCH_DEVICE_MODEL
+        if dtype is None:
+            dtype = settings.MODEL_DTYPE
+
         config = SuryaLayoutConfig.from_pretrained(self.checkpoint)
         decoder_config = config.decoder
         decoder = SuryaLayoutDecoderConfig(**decoder_config)

surya/ocr_error/loader.py

@@ -21,6 +21,11 @@ def model(
         device=settings.TORCH_DEVICE_MODEL,
         dtype=settings.MODEL_DTYPE
     ) -> DistilBertForSequenceClassification:
+        if device is None:
+            device = settings.TORCH_DEVICE_MODEL
+        if dtype is None:
+            dtype = settings.MODEL_DTYPE
+
         config = DistilBertConfig.from_pretrained(self.checkpoint)
         model = DistilBertForSequenceClassification.from_pretrained(self.checkpoint, torch_dtype=dtype, config=config).to(
             device).eval()

surya/recognition/loader.py

@@ -27,6 +27,10 @@ def model(
         device=settings.TORCH_DEVICE_MODEL,
         dtype=settings.MODEL_DTYPE
     ) -> OCREncoderDecoderModel:
+        if device is None:
+            device = settings.TORCH_DEVICE_MODEL
+        if dtype is None:
+            dtype = settings.MODEL_DTYPE
         config = SuryaOCRConfig.from_pretrained(self.checkpoint)
         decoder_config = config.decoder
         decoder = SuryaOCRDecoderConfig(**decoder_config)

surya/table_rec/__init__.py

@@ -264,6 +264,7 @@ def decode_batch_predictions(self, rowcol_predictions, cell_predictions, orig_si
                             merge_up=spanning_cell["merges"] in [MERGE_KEYS["merge_up"], MERGE_KEYS["merge_both"]],
                             merge_down=spanning_cell["merges"] in [MERGE_KEYS["merge_down"],
                                                                    MERGE_KEYS["merge_both"]],
+                            is_header=z == 0
                         )
                     )
                     cell_id += 1
@@ -293,7 +294,8 @@ def decode_batch_predictions(self, rowcol_predictions, cell_predictions, orig_si
                                 colspan=1,
                                 merge_up=False,
                                 merge_down=False,
-                                col_id=l
+                                col_id=l,
+                                is_header=z == 0
                             )
                         )
                         cell_id += 1

surya/table_rec/loader.py

@@ -21,6 +21,10 @@ def model(
             device=settings.TORCH_DEVICE_MODEL,
             dtype=settings.MODEL_DTYPE
     ) -> TableRecEncoderDecoderModel:
+        if device is None:
+            device = settings.TORCH_DEVICE_MODEL
+        if dtype is None:
+            dtype = settings.MODEL_DTYPE
         config = SuryaTableRecConfig.from_pretrained(self.checkpoint)
         decoder_config = config.decoder
         decoder = SuryaTableRecDecoderConfig(**decoder_config)

surya/table_rec/schema.py

@@ -10,10 +10,12 @@ class TableCell(PolygonBox):
     colspan: int
     within_row_id: int
     cell_id: int
+    is_header: bool
     rowspan: int | None = None
     merge_up: bool = False
     merge_down: bool = False
     col_id: int | None = None
+    text: str | None = None
 
     @property
     def label(self):