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# Initialize classifier
from medimageinsightmodel import MedImageInsight
import base64
classifier = MedImageInsight(
model_dir="2024.09.27",
vision_model_name="medimageinsigt-v1.0.0.pt",
language_model_name="language_model.pth"
)
def read_image(image_path):
with open(image_path, "rb") as f:
return f.read()
# Load model
classifier.load_model()
# Set model into eval mode
classifier.model.eval()
import urllib.request
image_url = "https://openi.nlm.nih.gov/imgs/512/145/145/CXR145_IM-0290-1001.png"
image_path = "CXR145_IM-0290-1001.png"
urllib.request.urlretrieve(image_url, image_path)
print(f"Image downloaded to {image_path}")
image = base64.encodebytes(read_image(image_path)).decode("utf-8")
# Example inference
images = [image]
labels = ["normal", "Pneumonia", "unclear"]
#Zero-shot classification
results = classifier.predict(images, labels)
print(results)
#Image embeddings
results = classifier.encode(images = images)
print(results)
#Text embeddings
results = classifier.encode(texts = labels)
print(results)
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