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example.py

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+import torch
+from PIL import Image, ImageDraw
+from transformers import AutoModel, AutoProcessor, AutoTokenizer, AutoConfig, AutoImageProcessor, GenerationConfig
+from postprocessing import extract_classes_bboxes, transform_bbox_to_original, postprocess_text
+
+#from hf_nemotron_parse_config import NemotronParseConfig
+#from hf_nemotron_parse_modeling import NemotronParseForConditionalGeneration
+#from hf_nemotron_parse_processor import NemotronParseProcessor, NemotronParseImageProcessor
+
+#AutoConfig.register("nemotron_parse", NemotronParseConfig)
+#AutoModel.register(NemotronParseConfig, NemotronParseForConditionalGeneration)
+#AutoProcessor.register("nemotron_parse", NemotronParseProcessor)
+#AutoImageProcessor.register("nemotron_parse", NemotronParseImageProcessor)
+
+
+# Load model and processor
+model_path = "nvidia/NVIDIA-Nemotron-Parse-v1.1" #Nano-12B-v2-VL-BF16"  # Or use a local path
+device = "cuda:0"
+
+model = AutoModel.from_pretrained(
+    model_path,
+    trust_remote_code=True,
+    torch_dtype=torch.bfloat16
+).to(device).eval()
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
+
+# Load image
+image = Image.open("../soa_1.png") #path/to/your/image.jpg")
+task_prompt = "</s><s><predict_bbox><predict_classes><output_markdown>"
+
+# Process image
+inputs = processor(images=[image], text=task_prompt, return_tensors="pt").to(device)
+prompt_ids = processor.tokenizer.encode(task_prompt, return_tensors="pt", add_special_tokens=False).cuda()
+
+
+generation_config = GenerationConfig.from_pretrained(model_path, trust_remote_code=True)
+# Generate text
+outputs = model.generate(**inputs,  generation_config=generation_config)
+
+# Decode the generated text
+generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0]
+classes, bboxes, texts = extract_classes_bboxes(generated_text) 
+bboxes = [transform_bbox_to_original(bbox, image.width, image.height) for bbox in bboxes]
+
+# Specify output formats for postprocessing
+table_format = 'latex' # latex | HTML | markdown
+text_format = 'markdown' # markdown | plain
+blank_text_in_figures = False # remove text inside 'Picture' class
+texts = [postprocess_text(text, cls = cls, table_format=table_format, text_format=text_format, blank_text_in_figures=blank_text_in_figures) for text, cls in zip(texts, classes)]
+
+for cl, bb, txt in zip(classes, bboxes, texts):
+    print(cl, ': ', txt)
+
+# OPTIONAL - Draw bounding boxes
+draw = ImageDraw.Draw(image)
+for bbox in bboxes:
+  draw.rectangle((bbox[0], bbox[1], bbox[2], bbox[3]), outline="red")
+