diff --git a/single_eval.py b/single_eval.py
new file mode 100644
index 0000000..e31c2d8
--- /dev/null
+++ b/single_eval.py
@@ -0,0 +1,69 @@
+from transformers import DonutProcessor, VisionEncoderDecoderModel, VisionEncoderDecoderConfig
+import re
+import torch
+from PIL import Image
+import time
+from fastapi import FastAPI, UploadFile, File
+import io
+import os   
+
+print("Set up config")
+config_vision = VisionEncoderDecoderConfig.from_pretrained("Zombely/plwiki-proto-fine-tuned-v3.2")
+config_vision.encoder.image_size = [1920, 2560] # (height, width)
+config_vision.decoder.max_length = 768
+
+processor = DonutProcessor.from_pretrained("Zombely/plwiki-proto-fine-tuned-v3.2")
+model = VisionEncoderDecoderModel.from_pretrained("Zombely/plwiki-proto-fine-tuned-v3.2", config=config_vision)
+
+processor.image_processor.size = [1920, 2560][::-1] # should be (width, height)
+processor.image_processor.do_align_long_axis = False
+
+# dataset = load_dataset(config.validation_dataset_path, split=config.validation_dataset_split)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.eval()
+model.to(device)
+
+print("Print ipconfig")
+os.system("ipconfig")
+
+print("Starting server")
+app = FastAPI()
+
+
+@app.post("/process")
+async def process_image(file: UploadFile= File(...)):
+
+    request_object_content = await file.read()
+    input_image = Image.open(io.BytesIO(request_object_content))
+
+    # prepare encoder inputs
+    pixel_values = processor(input_image.convert("RGB"), return_tensors="pt").pixel_values
+    pixel_values = pixel_values.to(device)
+    # prepare decoder inputs
+    task_prompt = "<s_cord-v2>"
+    decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
+    decoder_input_ids = decoder_input_ids.to(device)
+
+    print("Start processing")
+    # autoregressively generate sequence
+    start_time = time.time()
+    outputs = model.generate(
+            pixel_values,
+            decoder_input_ids=decoder_input_ids,
+            max_length=model.decoder.config.max_position_embeddings,
+            early_stopping=True,
+            pad_token_id=processor.tokenizer.pad_token_id,
+            eos_token_id=processor.tokenizer.eos_token_id,
+            use_cache=True,
+            num_beams=1,
+            bad_words_ids=[[processor.tokenizer.unk_token_id]],
+            return_dict_in_generate=True,
+        )
+    processing_time = (time.time() - start_time)
+
+    # turn into JSON
+    seq = processor.batch_decode(outputs.sequences)[0]
+    seq = seq.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
+    seq = re.sub(r"<.*?>", "", seq, count=1).strip()  # remove first task start token
+    seq = processor.token2json(seq)
+    return {"data": seq['text_sequence'], "processing_time": f"{processing_time} seconds"}
diff --git a/test.image.jpg b/test.image.jpg
new file mode 100644
index 0000000..6732149
Binary files /dev/null and b/test.image.jpg differ