add custom pipeline

distilbert-base-uncased-emotion

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+Subproject commit d12ff2a4b521b7bfd526aa7055665815c67e113b

handler.py

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+from typing import Dict, Any
+from transformers import pipeline
+import holidays
+import PIL.Image
+import io
+import pytesseract
+
+class PreTrainedPipeline():
+    def __init__(self, model_path="PrimWong/layout_qa_hparam_tuning"):
+        # Initializing the document-question-answering pipeline with the specified model
+        self.pipeline = pipeline("document-question-answering", model=model_path)
+        self.holidays = holidays.US()
+
+    def __call__(self, data: Dict[str, Any]) -> str:
+        """
+        Process input data for document question answering with optional holiday checking.
+
+        Args:
+            data (Dict[str, Any]): Input data containing an 'inputs' field with 'image' and 'question',
+                                   and optionally a 'date' field.
+
+        Returns:
+            str: The answer to the question or a holiday message if applicable.
+        """
+        inputs = data.get('inputs', {})
+        date = data.get("date")
+
+        # Check if date is provided and if it's a holiday
+        if date and date in self.holidays:
+            return "Today is a holiday!"
+
+        # Process the image and question for document question answering
+        image_path = inputs.get("image")
+        question = inputs.get("question")
+
+        # Load and process an image
+        image = PIL.Image.open(image_path)
+        image_text = pytesseract.image_to_string(image)  # Use OCR to extract text
+
+        # Run prediction (Note: this now uses the extracted text, not the image directly)
+        prediction = self.pipeline(question=question, context=image_text)
+        return prediction["answer"]  # Adjust based on actual output format of the model
+
+# Note: This script assumes the use of pytesseract for OCR to process images. Ensure pytesseract is configured properly.