completed initial handler.py

.gitignore

@@ -0,0 +1,13 @@
+__pycache__
+*.ipynb
+*.pdf
+
+test_handler_local.py
+test_handler.py
+test_endpoint.py
+
+setup
+upload_to_hf
+requirements.txt
+hf_token.py
+

README.md

@@ -7,11 +7,16 @@ tags:
  - document-question-answering
  - pdf
  - invoices
+ - endpoints-template
 widget:
 - text: "What is the invoice number?"
   src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"
 - text: "What is the purchase amount?"
   src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/contract.jpeg"
+library_name: generic
+model-index:
+  - name: layoutlm-invoices
+    results: []
 ---
 
 # LayoutLM for Invoices

handler.py

@@ -0,0 +1,56 @@
+from typing import Any
+from subprocess import run
+from docquery import document, pipeline
+import tempfile
+import os
+# from transformers import AutoConfig, AutoTokenizer, LayoutLMForQuestionAnswering
+
+# install tesseract-ocr and pytesseract
+run("apt install -y tesseract-ocr", shell=True, check=True)
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # config = AutoConfig.from_pretrained(model_checkpoint, revision=rev)
+        # self.tokenizer = AutoTokenizer.from_pretrained(path)
+        # self.model = LayoutLMForQuestionAnswering.from_pretrained(path)
+        # self.pipeline = pipeline('document-question-answering', model=self.model, tokenizer=self.tokenizer)
+        # self.pipeline = pipeline('document-question-answering', model='impira/layoutlm-invoices')
+        self.pipeline = pipeline('document-question-answering', model=path)
+
+    def __call__(self, data: dict[str, bytes]) -> dict[str, list[Any]]:
+        """
+        Args:
+            data (:obj:):
+                includes:
+                    - pdf bytes
+        """
+        # process input
+        f_bytes = data.pop("inputs", data)
+        try:
+            temp_file_name = next(tempfile._get_candidate_names())
+            temp_file_path = os.path.join('/tmp', f'{temp_file_name}.pdf')
+            with open(temp_file_path, 'wb') as temp_file:
+                temp_file.write(f_bytes)
+
+            if not os.path.exists(temp_file_path):
+                raise ValueError(f'File not found at path: {temp_file_path}')
+
+            results = []
+            doc = document.load_document(temp_file_path)
+            for q in ["What is the invoice number?", "What is the invoice total?"]:
+                result = self.pipeline(question=q, **doc.context)
+                results.append(result)
+
+        except Exception as e:
+            raise
+        else:
+            return {"predictions": results}
+        finally:
+            try:
+                os.remove(temp_file_path)
+            except FileNotFoundError as e:
+                print(e)
+
+
+
+