Update README.md

README.md

@@ -11,6 +11,49 @@ widget:
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 [Evaluation on chexpert-plus](https://github.com/Stanford-AIMI/chexpert-plus)
 
+
+Usage:
+
+```python
+import torch
+from PIL import Image
+from transformers import BertTokenizer, ViTImageProcessor, VisionEncoderDecoderModel, GenerationConfig
+import requests
+
+mode = "findings"
+# Model
+model = VisionEncoderDecoderModel.from_pretrained(f"IAMJB/chexpert-mimic-cxr-{mode}-baseline").eval()
+tokenizer = BertTokenizer.from_pretrained(f"IAMJB/chexpert-mimic-cxr-{mode}-baseline")
+image_processor = ViTImageProcessor.from_pretrained(f"IAMJB/chexpert-mimic-cxr-{mode}-baseline")
+#
+# Dataset
+generation_args = {
+   "bos_token_id": model.config.bos_token_id,
+   "eos_token_id": model.config.eos_token_id,
+   "pad_token_id": model.config.pad_token_id,
+   "num_return_sequences": 1,
+   "max_length": 128,
+   "use_cache": True,
+   "beam_width": 2,
+}
+#
+# Inference
+refs = []
+hyps = []
+with torch.no_grad():
+   url = "https://huggingface.co/IAMJB/interpret-cxr-impression-baseline/resolve/main/effusions-bibasal.jpg"
+   image = Image.open(requests.get(url, stream=True).raw)
+   pixel_values = image_processor(image, return_tensors="pt").pixel_values
+   # Generate predictions
+   generated_ids = model.generate(
+       pixel_values,
+       generation_config=GenerationConfig(
+           **{**generation_args, "decoder_start_token_id": tokenizer.cls_token_id})
+   )
+   generated_texts = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+   print(generated_texts)
+```
+
 If you are using this model, please be sure to cite:
 ```
 @misc{chambon2024chexpertplusaugmentinglarge,