Create inference.py

inference.py

@@ -0,0 +1,50 @@
+import os
+import torch
+import re
+from PIL import Image
+from transformers import DonutProcessor, VisionEncoderDecoderModel
+import base64
+import io
+
+def model_fn(model_dir):
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    processor = DonutProcessor.from_pretrained(model_dir)
+    model = VisionEncoderDecoderModel.from_pretrained(model_dir)
+    model.to(device)
+    return model, processor, device
+
+def input_fn(request_body, content_type):
+    if content_type == 'application/json':
+        data = json.loads(request_body)
+        image_data = data['inputs']
+        image = Image.open(io.BytesIO(base64.b64decode(image_data))).convert("RGB")
+        return image
+    else:
+        raise ValueError(f"Unsupported content type: {content_type}")
+
+def predict_fn(input_data, model_and_processor):
+    model, processor, device = model_and_processor
+    pixel_values = processor(input_data, return_tensors="pt").pixel_values.to(device)
+    
+    model.eval()
+    with torch.no_grad():
+        task_prompt = "<s_receipt>"
+        decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids.to(device)
+        generated_outputs = model.generate(
+            pixel_values,
+            decoder_input_ids=decoder_input_ids,
+            max_length=model.config.decoder.max_position_embeddings, 
+            pad_token_id=processor.tokenizer.pad_token_id,
+            eos_token_id=processor.tokenizer.eos_token_id,
+            early_stopping=True,
+            bad_words_ids=[[processor.tokenizer.unk_token_id]],
+            return_dict_in_generate=True
+        )
+    
+    decoded_text = processor.batch_decode(generated_outputs.sequences)[0]
+    decoded_text = decoded_text.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
+    decoded_text = re.sub(r"<.*?>", "", decoded_text, count=1).strip()
+    return decoded_text
+
+def output_fn(prediction, accept):
+    return json.dumps({'result': prediction}), accept