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import io | |
import requests | |
import torch | |
from PIL import Image | |
from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig | |
# step 1: Setup constant | |
device = "cuda" | |
dtype = torch.float16 | |
# step 2: Load Processor and Model | |
processor = AutoProcessor.from_pretrained("StanfordAIMI/CheXagent-8b", trust_remote_code=True) | |
generation_config = GenerationConfig.from_pretrained("StanfordAIMI/CheXagent-8b") | |
model = AutoModelForCausalLM.from_pretrained("StanfordAIMI/CheXagent-8b", torch_dtype=dtype, trust_remote_code=True) | |
# step 3: Fetch the images | |
image_path = "https://upload.wikimedia.org/wikipedia/commons/3/3b/Pleural_effusion-Metastatic_breast_carcinoma_Case_166_%285477628658%29.jpg" | |
images = [Image.open(io.BytesIO(requests.get(image_path).content)).convert("RGB")] | |
# step 4: Generate the Findings section | |
prompt = f'Describe "Airway"' | |
inputs = processor(images=images, text=f" USER: <s>{prompt} ASSISTANT: <s>", return_tensors="pt").to(device=device, dtype=dtype) | |
output = model.generate(**inputs, generation_config=generation_config)[0] | |
response = processor.tokenizer.decode(output, skip_special_tokens=True) | |