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1import io
2
3import requests
4import torch
5from PIL import Image
6from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig
7
8# step 1: Setup constant
9device = "cuda"
10dtype = torch.float16
11
12# step 2: Load Processor and Model
13processor = AutoProcessor.from_pretrained("StanfordAIMI/CheXagent-8b", trust_remote_code=True)
14generation_config = GenerationConfig.from_pretrained("StanfordAIMI/CheXagent-8b")
15model = AutoModelForCausalLM.from_pretrained("StanfordAIMI/CheXagent-8b", torch_dtype=dtype, trust_remote_code=True)
16
17# step 3: Fetch the images
18image_path = "https://upload.wikimedia.org/wikipedia/commons/3/3b/Pleural_effusion-Metastatic_breast_carcinoma_Case_166_%285477628658%29.jpg"
19images = [Image.open(io.BytesIO(requests.get(image_path).content)).convert("RGB")]
20
21# step 4: Generate the Findings section
22prompt = f'Describe "Airway"'
23inputs = processor(images=images, text=f" USER: <s>{prompt} ASSISTANT: <s>", return_tensors="pt").to(device=device, dtype=dtype)
24output = model.generate(**inputs, generation_config=generation_config)[0]
25response = processor.tokenizer.decode(output, skip_special_tokens=True)@article{chexagent-2024,
title={CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation},
author={Chen, Zhihong and Varma, Maya and Delbrouck, Jean-Benoit and Paschali, Magdalini and Blankemeier, Louis and Veen, Dave Van and Valanarasu, Jeya Maria Jose and Youssef, Alaa and Cohen, Joseph Paul and Reis, Eduardo Pontes and Tsai, Emily B. and Johnston, Andrew and Olsen, Cameron and Abraham, Tanishq Mathew and Gatidis, Sergios and Chaudhari, Akshay S and Langlotz, Curtis},
journal={arXiv preprint arXiv:2401.12208},
url={https://arxiv.org/abs/2401.12208},
year={2024}
}