Update README.md

README.md

@@ -1,3 +1,58 @@
 ---
 license: mit
 ---
+```
+import re
+import transformers
+from PIL import Image
+from transformers import DonutProcessor, VisionEncoderDecoderModel
+import torch
+import random
+import numpy as np
+
+fine_tuned_model = VisionEncoderDecoderModel.from_pretrained("aravind-selvam/donut_finetuned_chart")
+processor = DonutProcessor.from_pretrained("aravind-selvam/donut_finetuned_chart")
+
+# Move model to GPU
+device = "cuda" if torch.cuda.is_available() else "cpu"
+fine_tuned_model.to(device)
+
+# Load random document image from the test set
+dataset = load_dataset("hf-internal-testing/example-documents", split="test")
+sample_image = dataset[1]
+
+def run_prediction(sample, model=fine_tuned_model, processor=processor):
+    # pixel values
+    pixel_values = processor(image, return_tensors="pt").pixel_values
+    # prepare inputs
+    task_prompt = "<s>"
+    decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
+
+    # run inference
+    outputs = model.generate(
+        pixel_values.to(device),
+        decoder_input_ids=decoder_input_ids.to(device),
+        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=2,
+        # bad_words_ids=[[processor.tokenizer.unk_token_id]],
+        return_dict_in_generate=True,
+    )
+
+    # process output
+    prediction = processor.batch_decode(outputs.sequences)[0]
+    prediction = re.sub(r"<one>", "1", prediction)
+    prediction = processor.token2json(prediction)
+
+
+    # load reference target
+    target = processor.token2json(test_sample["target_sequence"])
+    return prediction, target
+
+prediction, target = run_prediction(sample_image)
+print(f"Reference:\n {target}")
+print(f"Prediction:\n {prediction}")
+```