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import gradio as gr
from transformers import AutoModelForImageClassification, pipeline, AutoImageProcessor
from torchvision import transforms
model = AutoModelForImageClassification.from_pretrained("Nicole-M/Dataset1-ViT")
image_processor = AutoImageProcessor.from_pretrained("Nicole-M/Dataset1-ViT")
clf = pipeline(model=model, task="image-classification", image_processor=image_processor)
class_names = ['Benign', 'Malignant']
def predict_image(img):
img=img.reshape(224,224,3)
img = transforms.ToPILImage()(img)
prediction=clf.predict(img)
return {class_names[i]: float(prediction[i]["score"]) for i in range(2)}
image = gr.inputs.Image(shape=(224,224))
label = gr.outputs.Label(num_top_classes=2)
gr.Interface(fn=predict_image, inputs=image, outputs=label, interpretation='default', title="Mammogram classification").launch() |