Upload handler.py

handler.py

@@ -0,0 +1,47 @@
+from typing import Dict, List, Any
+from transformers import NougatProcessor, 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 = NougatProcessor.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):
+
+        inputs = data.pop("inputs", data)
+
+
+        # preprocess the input
+        pixel_values = self.processor(inputs, return_tensors="pt").pixel_values
+        print(type(pixel_values))
+        # forward pass
+        outputs = self.model.generate(
+            pixel_values.to(device),
+            min_length = 1,
+            # decoder_input_ids=self.decoder_input_ids.to(device),
+            max_length=3584,
+            # 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, skip_special_tokens=True)[0]
+        prediction = self.processor.post_process_generation(prediction, fix_markdown=False)
+
+        return prediction