Upload handler.py

Implementing handler.py for it to be used under Inference API.

handler.py

@@ -0,0 +1,46 @@
+from typing import Dict, List, Any
+from transformers import DonutProcessor, VisionEncoderDecoderModel
+import torch
+
+
+# check for GPU
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # load the model
+        self.processor = DonutProcessor.from_pretrained(path)
+        self.model = VisionEncoderDecoderModel.from_pretrained(path)
+        # move model to device
+        self.model.to(device)
+        self.decoder_input_ids = self.processor.tokenizer(
+            "<s_cord-v2>", add_special_tokens=False, return_tensors="pt"
+        ).input_ids
+
+    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
+
+        inputs = data.pop("inputs", data)
+
+
+        # preprocess the input
+        pixel_values = self.processor(inputs, return_tensors="pt").pixel_values
+
+        # forward pass
+        outputs = self.model.generate(
+            pixel_values.to(device),
+            decoder_input_ids=self.decoder_input_ids.to(device),
+            max_length=self.model.decoder.config.max_position_embeddings,
+            early_stopping=True,
+            pad_token_id=self.processor.tokenizer.pad_token_id,
+            eos_token_id=self.processor.tokenizer.eos_token_id,
+            use_cache=True,
+            num_beams=1,
+            bad_words_ids=[[self.processor.tokenizer.unk_token_id]],
+            return_dict_in_generate=True,
+        )
+        # process output
+        prediction = self.processor.batch_decode(outputs.sequences)[0]
+        prediction = self.processor.token2json(prediction)
+
+        return prediction